Global Economic Monitor: Inflation Analysis
Nsovo Ntuli
2025-01-11
Global Inflation Analysis: Time Series Modeling of CPI Price (2000–2024)
Introduction
This project focuses on analyzing global inflation trends using time series modeling techniques. The dataset, sourced from WorldData Bank, spans the years 2000 to 2024 and contains a wide range of macroeconomic indicators, such as GDP, population, and various economic monitors. However, the scope of this project is narrowed to inflation-related variables, specifically:
Core CPI, not seasonally adjusted
Core CPI, seasonally adjusted
CPI Price (% year-over-year, median weighted, seasonally adjusted)
Among these, the primary focus is on the CPI Price (% y-o-y, median weighted, seasonally adjusted) for conducting time series modeling.
Project Objectives
The primary goal of this project is to understand global and continental inflation dynamics over the last three decades and to forecast future trends. To achieve this, we:
Explored Historical Trends: Analyzed inflation data globally and by continent to identify patterns and anomalies.
Applied Time Series Models: Used ARIMA models to analyze and forecast CPI Price for different continents.
Generated Insights: Derived actionable insights about inflationary pressures and their implications for global economies.
#libraries
library(readr)
library(tidyverse)
library(zoo)
library(naniar)
library(janitor)
library(tseries)
library(forecast)
library(VIM)
library(mice)#import dataset and rename it
e0debd4f_365a_4f6c_96cc_0cdf803e4610_Data <- read_csv("e0debd4f-365a-4f6c-96cc-0cdf803e4610_Data.csv")
Global_Economic_Monitor <- e0debd4f_365a_4f6c_96cc_0cdf803e4610_Data#go through the dataset
sample_n(Global_Economic_Monitor, 20)## # A tibble: 20 × 129
## Country `Country Code` Series `Series Code` `2000 [2000]` `2000Q1 [2000Q1]`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Slovenia SVN CPI P… CPTOTNSXN 67.944210032… ..
## 2 Thailand THA Expor… DXGSRMRCHSAXD 0.67469296659 0.68619660419
## 3 Dominica DMA Excha… DPANUSSPB 2.7 ..
## 4 EMDE Sou… SAP Impor… DMGSRMRCHSAKD .. ..
## 5 France FRA GDP,c… NYGDPMKTPSAKD 2410249.3566… 594619.05635318
## 6 Virgin I… VIR Total… TOTRESV .. ..
## 7 Denmark DNK Expor… DXGSRMRCHNSKD 84541.341382… 19827.9994118682
## 8 Taiwan C… TWN Impor… DMGSRMRCHNSCD 112994.2 22800.8
## 9 San Mari… SMR Core … CORESA .. ..
## 10 Congo De… COD Impor… DMGSRMRCHNSCD .. ..
## 11 Benin BEN Stock… DSTKMKTXN .. ..
## 12 Suriname SUR Offic… DPANUSLCU 1.32175030414 ..
## 13 Sub-Saha… SST Expor… DXGSRMRCHSAKD 81435.570659… 20674.4586833794
## 14 Singapore SGP Expor… DXGSRMRCHSACD 137565.03468… 32082.8745474386
## 15 Italy ITA Unemp… UNEMPSA_ .. ..
## 16 Sri Lanka LKA Expor… DXGSRMRCHSAKD .. ..
## 17 Dominica DMA Unemp… UNEMPSA_ .. ..
## 18 Jamaica JAM Stock… DSTKMKTXN .. ..
## 19 Ecuador ECU Excha… DPANUSSPF 24974.628433… ..
## 20 Australia AUS Excha… DPANUSSPF 1.72524125735 ..
## # ℹ 123 more variables: `2000Q2 [2000Q2]` <chr>, `2000Q3 [2000Q3]` <chr>,
## # `2000Q4 [2000Q4]` <chr>, `2001 [2001]` <chr>, `2001Q1 [2001Q1]` <chr>,
## # `2001Q2 [2001Q2]` <chr>, `2001Q3 [2001Q3]` <chr>, `2001Q4 [2001Q4]` <chr>,
## # `2002 [2002]` <chr>, `2002Q1 [2002Q1]` <chr>, `2002Q2 [2002Q2]` <chr>,
## # `2002Q3 [2002Q3]` <chr>, `2002Q4 [2002Q4]` <chr>, `2003 [2003]` <chr>,
## # `2003Q1 [2003Q1]` <chr>, `2003Q2 [2003Q2]` <chr>, `2003Q3 [2003Q3]` <chr>,
## # `2003Q4 [2003Q4]` <chr>, `2004 [2004]` <chr>, `2004Q1 [2004Q1]` <chr>, …head(Global_Economic_Monitor, 20)## # A tibble: 20 × 129
## Country `Country Code` Series `Series Code` `2000 [2000]` `2000Q1 [2000Q1]`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Advanced… AME Core … CORENS .. ..
## 2 Advanced… AME Core … CORESA 86.388887096… ..
## 3 Advanced… AME CPI P… CPTOTSAXMZGY 2.66416327159 ..
## 4 Advanced… AME CPI P… CPTOTSAXNZGY .. ..
## 5 Advanced… AME CPI P… CPTOTSAXN 84.055145253… ..
## 6 Advanced… AME CPI P… CPTOTNSXN 84.398336219… ..
## 7 Advanced… AME Excha… DPANUSSPB 47.110472601… ..
## 8 Advanced… AME Unemp… UNEMPSA_ 5.84584165284 ..
## 9 Advanced… AME Total… TOTRESV 1362574.9335… ..
## 10 Advanced… AME Terms… TOT .. ..
## 11 Advanced… AME Stock… DSTKMKTXD .. ..
## 12 Advanced… AME Stock… DSTKMKTXN .. ..
## 13 Advanced… AME Retai… RETSALESSA 59.252174820… ..
## 14 Advanced… AME Real … REER 109.47208168… ..
## 15 Advanced… AME Offic… DPANUSLCU .. ..
## 16 Advanced… AME Nomin… NEER 105.17672551… ..
## 17 Advanced… AME Month… IMPCOV 0.28015471618 ..
## 18 Advanced… AME Indus… IPTOTNSKD 939888524752… ..
## 19 Advanced… AME Indus… IPTOTSAKD 895559268194… ..
## 20 Advanced… AME Impor… DMGSRMRCHSAXD 0.70271298953 0.7140059872
## # ℹ 123 more variables: `2000Q2 [2000Q2]` <chr>, `2000Q3 [2000Q3]` <chr>,
## # `2000Q4 [2000Q4]` <chr>, `2001 [2001]` <chr>, `2001Q1 [2001Q1]` <chr>,
## # `2001Q2 [2001Q2]` <chr>, `2001Q3 [2001Q3]` <chr>, `2001Q4 [2001Q4]` <chr>,
## # `2002 [2002]` <chr>, `2002Q1 [2002Q1]` <chr>, `2002Q2 [2002Q2]` <chr>,
## # `2002Q3 [2002Q3]` <chr>, `2002Q4 [2002Q4]` <chr>, `2003 [2003]` <chr>,
## # `2003Q1 [2003Q1]` <chr>, `2003Q2 [2003Q2]` <chr>, `2003Q3 [2003Q3]` <chr>,
## # `2003Q4 [2003Q4]` <chr>, `2004 [2004]` <chr>, `2004Q1 [2004Q1]` <chr>, …tail(Global_Economic_Monitor,20)## # A tibble: 20 × 129
## Country `Country Code` Series `Series Code` `2000 [2000]` `2000Q1 [2000Q1]`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Zambia ZMB Impor… DMGSRMRCHSACD 1092.1220193… 211.69438729382
## 2 Zambia ZMB Impor… DMGSRMRCHNSCD 1100.7486372… 201.71035009241
## 3 Zambia ZMB Impor… DMGSRMRCHSAKD .. 323.22596883259
## 4 Zambia ZMB Impor… DMGSRMRCHNSKD .. 307.9544782286
## 5 Zambia ZMB GDP,c… NYGDPMKTPSACD .. ..
## 6 Zambia ZMB GDP,c… NYGDPMKTPSACN .. ..
## 7 Zambia ZMB GDP,c… NYGDPMKTPSAKD .. ..
## 8 Zambia ZMB GDP,c… NYGDPMKTPSAKN .. ..
## 9 Zambia ZMB Expor… DXGSRMRCHSAXD .. ..
## 10 Zambia ZMB Expor… DXGSRMRCHNSKD .. ..
## 11 Zambia ZMB Expor… DXGSRMRCHSAKD .. ..
## 12 Zambia ZMB Expor… DXGSRMRCHNSCD 614.26933145… 131.21548020783
## 13 Zambia ZMB Expor… DXGSRMRCHSACD 613.04868529… 128.72709784262
## 14 Zambia ZMB Expor… DXGSRMRCHNSXD .. ..
## 15 Zambia ZMB Excha… DPANUSSPF 3.27304003309 ..
## 16 <NA> <NA> <NA> <NA> <NA> <NA>
## 17 <NA> <NA> <NA> <NA> <NA> <NA>
## 18 <NA> <NA> <NA> <NA> <NA> <NA>
## 19 Data fro… <NA> <NA> <NA> <NA> <NA>
## 20 Last Upd… <NA> <NA> <NA> <NA> <NA>
## # ℹ 123 more variables: `2000Q2 [2000Q2]` <chr>, `2000Q3 [2000Q3]` <chr>,
## # `2000Q4 [2000Q4]` <chr>, `2001 [2001]` <chr>, `2001Q1 [2001Q1]` <chr>,
## # `2001Q2 [2001Q2]` <chr>, `2001Q3 [2001Q3]` <chr>, `2001Q4 [2001Q4]` <chr>,
## # `2002 [2002]` <chr>, `2002Q1 [2002Q1]` <chr>, `2002Q2 [2002Q2]` <chr>,
## # `2002Q3 [2002Q3]` <chr>, `2002Q4 [2002Q4]` <chr>, `2003 [2003]` <chr>,
## # `2003Q1 [2003Q1]` <chr>, `2003Q2 [2003Q2]` <chr>, `2003Q3 [2003Q3]` <chr>,
## # `2003Q4 [2003Q4]` <chr>, `2004 [2004]` <chr>, `2004Q1 [2004Q1]` <chr>, …# Extract rows that contain macroeconomic indicators in the `Country` column and make
# macroeconomic indicator dataset from global monitor
macroeconomics_indicator <- Global_Economic_Monitor %>%
filter(Country %in% unique(Global_Economic_Monitor$Country)[grep("EMDE|Countries|Income|developing|Emerging Market|High Income|World|Advanced Economies|Commodity",
unique(Global_Economic_Monitor$Country))])
# Remove these rows from the original data to keep only rows with country names
Global_Economic_Monitor <- Global_Economic_Monitor %>%
filter(!Country %in% macroeconomics_indicator$Country)#remove the duplicates
distinct(Global_Economic_Monitor) ## # A tibble: 7,059 × 129
## Country `Country Code` Series `Series Code` `2000 [2000]` `2000Q1 [2000Q1]`
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Afghanis… AFG Core … CORENS .. ..
## 2 Afghanis… AFG Core … CORESA .. ..
## 3 Afghanis… AFG CPI P… CPTOTSAXMZGY .. ..
## 4 Afghanis… AFG CPI P… CPTOTSAXNZGY .. ..
## 5 Afghanis… AFG CPI P… CPTOTSAXN .. ..
## 6 Afghanis… AFG CPI P… CPTOTNSXN .. ..
## 7 Afghanis… AFG Excha… DPANUSSPB 47.356666666… ..
## 8 Afghanis… AFG Unemp… UNEMPSA_ .. ..
## 9 Afghanis… AFG Total… TOTRESV .. ..
## 10 Afghanis… AFG Terms… TOT .. ..
## # ℹ 7,049 more rows
## # ℹ 123 more variables: `2000Q2 [2000Q2]` <chr>, `2000Q3 [2000Q3]` <chr>,
## # `2000Q4 [2000Q4]` <chr>, `2001 [2001]` <chr>, `2001Q1 [2001Q1]` <chr>,
## # `2001Q2 [2001Q2]` <chr>, `2001Q3 [2001Q3]` <chr>, `2001Q4 [2001Q4]` <chr>,
## # `2002 [2002]` <chr>, `2002Q1 [2002Q1]` <chr>, `2002Q2 [2002Q2]` <chr>,
## # `2002Q3 [2002Q3]` <chr>, `2002Q4 [2002Q4]` <chr>, `2003 [2003]` <chr>,
## # `2003Q1 [2003Q1]` <chr>, `2003Q2 [2003Q2]` <chr>, …unique(Global_Economic_Monitor$Series)## [1] "Core CPI,not seas.adj,,,"
## [2] "Core CPI,seas.adj,,,"
## [3] "CPI Price, % y-o-y, median weighted, seas. adj.,"
## [4] "CPI Price, % y-o-y, not seas. adj.,,"
## [5] "CPI Price, seas. adj.,,,"
## [6] "CPI Price,not seas.adj,,,"
## [7] "Exchange rate, new LCU per USD extended backward, period average,,"
## [8] "Unemployment rate,Percent,,,"
## [9] "Total Reserves,,,,"
## [10] "Terms of Trade,,,,"
## [11] "Stock Markets, US$,,,"
## [12] "Stock Markets, LCU,,,"
## [13] "Retail Sales Volume,Index,,,"
## [14] "Real Effective Exchange Rate,,,,"
## [15] "Official exchange rate, LCU per USD, period average,,"
## [16] "Nominal Effective Exchange Rate,,,,"
## [17] "Months Import Cover of Foreign Reserves,,,,"
## [18] "Industrial Production, constant US$,,,"
## [19] "Industrial Production, constant US$, seas. adj.,,"
## [20] "Imports Merchandise, Customs, Price, US$, seas. adj."
## [21] "Imports Merchandise, Customs, Price, US$, not seas. adj."
## [22] "Imports Merchandise, Customs, current US$, millions, seas. adj."
## [23] "Imports Merchandise, Customs, current US$, millions, not seas. adj."
## [24] "Imports Merchandise, Customs, constant US$, millions, seas. adj."
## [25] "Imports Merchandise, Customs, constant US$, millions, not seas. adj."
## [26] "GDP,current US$,millions,seas. adj.,"
## [27] "GDP,current LCU,millions,seas. adj.,"
## [28] "GDP,constant 2010 US$,millions,seas. adj.,"
## [29] "GDP,constant 2010 LCU,millions,seas. adj.,"
## [30] "Exports Merchandise, Customs, Price, US$, seas. adj."
## [31] "Exports Merchandise, Customs, constant US$, millions, not seas. adj."
## [32] "Exports Merchandise, Customs, constant US$, millions, seas. adj."
## [33] "Exports Merchandise, Customs, current US$, millions, not seas. adj."
## [34] "Exports Merchandise, Customs, current US$, millions, seas. adj."
## [35] "Exports Merchandise, Customs, Price, US$, not seas. adj."
## [36] "Exchange rate, old LCU per USD extended forward, period average,,"
## [37] NAstr(Global_Economic_Monitor)## spc_tbl_ [7,061 × 129] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ Country : chr [1:7061] "Afghanistan" "Afghanistan" "Afghanistan" "Afghanistan" ...
## $ Country Code : chr [1:7061] "AFG" "AFG" "AFG" "AFG" ...
## $ Series : chr [1:7061] "Core CPI,not seas.adj,,," "Core CPI,seas.adj,,," "CPI Price, % y-o-y, median weighted, seas. adj.," "CPI Price, % y-o-y, not seas. adj.,," ...
## $ Series Code : chr [1:7061] "CORENS" "CORESA" "CPTOTSAXMZGY" "CPTOTSAXNZGY" ...
## $ 2000 [2000] : chr [1:7061] ".." ".." ".." ".." ...
## $ 2000Q1 [2000Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2000Q2 [2000Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2000Q3 [2000Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2000Q4 [2000Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2001 [2001] : chr [1:7061] ".." ".." ".." ".." ...
## $ 2001Q1 [2001Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2001Q2 [2001Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2001Q3 [2001Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2001Q4 [2001Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2002 [2002] : chr [1:7061] ".." ".." ".." ".." ...
## $ 2002Q1 [2002Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2002Q2 [2002Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2002Q3 [2002Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2002Q4 [2002Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2003 [2003] : chr [1:7061] ".." ".." ".." ".." ...
## $ 2003Q1 [2003Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2003Q2 [2003Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2003Q3 [2003Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2003Q4 [2003Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2004 [2004] : chr [1:7061] ".." ".." ".." ".." ...
## $ 2004Q1 [2004Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2004Q2 [2004Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2004Q3 [2004Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2004Q4 [2004Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2005 [2005] : chr [1:7061] ".." ".." ".." "10.24715592289" ...
## $ 2005Q1 [2005Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2005Q2 [2005Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2005Q3 [2005Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2005Q4 [2005Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2006 [2006] : chr [1:7061] ".." ".." ".." "6.76652570958" ...
## $ 2006Q1 [2006Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2006Q2 [2006Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2006Q3 [2006Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2006Q4 [2006Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2007 [2007] : chr [1:7061] ".." ".." ".." "8.66675778504" ...
## $ 2007Q1 [2007Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2007Q2 [2007Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2007Q3 [2007Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2007Q4 [2007Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2008 [2008] : chr [1:7061] ".." ".." ".." "26.44383832063" ...
## $ 2008Q1 [2008Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2008Q2 [2008Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2008Q3 [2008Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2008Q4 [2008Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2009 [2009] : chr [1:7061] ".." ".." ".." "-6.79140946808" ...
## $ 2009Q1 [2009Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2009Q2 [2009Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2009Q3 [2009Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2009Q4 [2009Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2010 [2010] : chr [1:7061] ".." ".." ".." "2.18628234597" ...
## $ 2010Q1 [2010Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2010Q2 [2010Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2010Q3 [2010Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2010Q4 [2010Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2011 [2011] : chr [1:7061] ".." ".." ".." "11.82724209156" ...
## $ 2011Q1 [2011Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2011Q2 [2011Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2011Q3 [2011Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2011Q4 [2011Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2012 [2012] : chr [1:7061] ".." ".." ".." "6.44138171658" ...
## $ 2012Q1 [2012Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2012Q2 [2012Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2012Q3 [2012Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2012Q4 [2012Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2013 [2013] : chr [1:7061] ".." ".." ".." "7.38136116922" ...
## $ 2013Q1 [2013Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2013Q2 [2013Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2013Q3 [2013Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2013Q4 [2013Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2014 [2014] : chr [1:7061] ".." ".." ".." "4.66945580562" ...
## $ 2014Q1 [2014Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2014Q2 [2014Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2014Q3 [2014Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2014Q4 [2014Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2015 [2015] : chr [1:7061] ".." ".." ".." "-0.6701681989" ...
## $ 2015Q1 [2015Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2015Q2 [2015Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2015Q3 [2015Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2015Q4 [2015Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2016 [2016] : chr [1:7061] ".." ".." ".." "4.39026872486" ...
## $ 2016Q1 [2016Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2016Q2 [2016Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2016Q3 [2016Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2016Q4 [2016Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2017 [2017] : chr [1:7061] ".." ".." ".." "4.97948067594" ...
## $ 2017Q1 [2017Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2017Q2 [2017Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2017Q3 [2017Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2017Q4 [2017Q4]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2018 [2018] : chr [1:7061] ".." ".." ".." "0.62169546733" ...
## $ 2018Q1 [2018Q1]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2018Q2 [2018Q2]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2018Q3 [2018Q3]: chr [1:7061] ".." ".." ".." ".." ...
## $ 2018Q4 [2018Q4]: chr [1:7061] ".." ".." ".." ".." ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. Country = col_character(),
## .. `Country Code` = col_character(),
## .. Series = col_character(),
## .. `Series Code` = col_character(),
## .. `2000 [2000]` = col_character(),
## .. `2000Q1 [2000Q1]` = col_character(),
## .. `2000Q2 [2000Q2]` = col_character(),
## .. `2000Q3 [2000Q3]` = col_character(),
## .. `2000Q4 [2000Q4]` = col_character(),
## .. `2001 [2001]` = col_character(),
## .. `2001Q1 [2001Q1]` = col_character(),
## .. `2001Q2 [2001Q2]` = col_character(),
## .. `2001Q3 [2001Q3]` = col_character(),
## .. `2001Q4 [2001Q4]` = col_character(),
## .. `2002 [2002]` = col_character(),
## .. `2002Q1 [2002Q1]` = col_character(),
## .. `2002Q2 [2002Q2]` = col_character(),
## .. `2002Q3 [2002Q3]` = col_character(),
## .. `2002Q4 [2002Q4]` = col_character(),
## .. `2003 [2003]` = col_character(),
## .. `2003Q1 [2003Q1]` = col_character(),
## .. `2003Q2 [2003Q2]` = col_character(),
## .. `2003Q3 [2003Q3]` = col_character(),
## .. `2003Q4 [2003Q4]` = col_character(),
## .. `2004 [2004]` = col_character(),
## .. `2004Q1 [2004Q1]` = col_character(),
## .. `2004Q2 [2004Q2]` = col_character(),
## .. `2004Q3 [2004Q3]` = col_character(),
## .. `2004Q4 [2004Q4]` = col_character(),
## .. `2005 [2005]` = col_character(),
## .. `2005Q1 [2005Q1]` = col_character(),
## .. `2005Q2 [2005Q2]` = col_character(),
## .. `2005Q3 [2005Q3]` = col_character(),
## .. `2005Q4 [2005Q4]` = col_character(),
## .. `2006 [2006]` = col_character(),
## .. `2006Q1 [2006Q1]` = col_character(),
## .. `2006Q2 [2006Q2]` = col_character(),
## .. `2006Q3 [2006Q3]` = col_character(),
## .. `2006Q4 [2006Q4]` = col_character(),
## .. `2007 [2007]` = col_character(),
## .. `2007Q1 [2007Q1]` = col_character(),
## .. `2007Q2 [2007Q2]` = col_character(),
## .. `2007Q3 [2007Q3]` = col_character(),
## .. `2007Q4 [2007Q4]` = col_character(),
## .. `2008 [2008]` = col_character(),
## .. `2008Q1 [2008Q1]` = col_character(),
## .. `2008Q2 [2008Q2]` = col_character(),
## .. `2008Q3 [2008Q3]` = col_character(),
## .. `2008Q4 [2008Q4]` = col_character(),
## .. `2009 [2009]` = col_character(),
## .. `2009Q1 [2009Q1]` = col_character(),
## .. `2009Q2 [2009Q2]` = col_character(),
## .. `2009Q3 [2009Q3]` = col_character(),
## .. `2009Q4 [2009Q4]` = col_character(),
## .. `2010 [2010]` = col_character(),
## .. `2010Q1 [2010Q1]` = col_character(),
## .. `2010Q2 [2010Q2]` = col_character(),
## .. `2010Q3 [2010Q3]` = col_character(),
## .. `2010Q4 [2010Q4]` = col_character(),
## .. `2011 [2011]` = col_character(),
## .. `2011Q1 [2011Q1]` = col_character(),
## .. `2011Q2 [2011Q2]` = col_character(),
## .. `2011Q3 [2011Q3]` = col_character(),
## .. `2011Q4 [2011Q4]` = col_character(),
## .. `2012 [2012]` = col_character(),
## .. `2012Q1 [2012Q1]` = col_character(),
## .. `2012Q2 [2012Q2]` = col_character(),
## .. `2012Q3 [2012Q3]` = col_character(),
## .. `2012Q4 [2012Q4]` = col_character(),
## .. `2013 [2013]` = col_character(),
## .. `2013Q1 [2013Q1]` = col_character(),
## .. `2013Q2 [2013Q2]` = col_character(),
## .. `2013Q3 [2013Q3]` = col_character(),
## .. `2013Q4 [2013Q4]` = col_character(),
## .. `2014 [2014]` = col_character(),
## .. `2014Q1 [2014Q1]` = col_character(),
## .. `2014Q2 [2014Q2]` = col_character(),
## .. `2014Q3 [2014Q3]` = col_character(),
## .. `2014Q4 [2014Q4]` = col_character(),
## .. `2015 [2015]` = col_character(),
## .. `2015Q1 [2015Q1]` = col_character(),
## .. `2015Q2 [2015Q2]` = col_character(),
## .. `2015Q3 [2015Q3]` = col_character(),
## .. `2015Q4 [2015Q4]` = col_character(),
## .. `2016 [2016]` = col_character(),
## .. `2016Q1 [2016Q1]` = col_character(),
## .. `2016Q2 [2016Q2]` = col_character(),
## .. `2016Q3 [2016Q3]` = col_character(),
## .. `2016Q4 [2016Q4]` = col_character(),
## .. `2017 [2017]` = col_character(),
## .. `2017Q1 [2017Q1]` = col_character(),
## .. `2017Q2 [2017Q2]` = col_character(),
## .. `2017Q3 [2017Q3]` = col_character(),
## .. `2017Q4 [2017Q4]` = col_character(),
## .. `2018 [2018]` = col_character(),
## .. `2018Q1 [2018Q1]` = col_character(),
## .. `2018Q2 [2018Q2]` = col_character(),
## .. `2018Q3 [2018Q3]` = col_character(),
## .. `2018Q4 [2018Q4]` = col_character(),
## .. `2019 [2019]` = col_character(),
## .. `2019Q1 [2019Q1]` = col_character(),
## .. `2019Q2 [2019Q2]` = col_character(),
## .. `2019Q3 [2019Q3]` = col_character(),
## .. `2019Q4 [2019Q4]` = col_character(),
## .. `2020 [2020]` = col_character(),
## .. `2020Q1 [2020Q1]` = col_character(),
## .. `2020Q2 [2020Q2]` = col_character(),
## .. `2020Q3 [2020Q3]` = col_character(),
## .. `2020Q4 [2020Q4]` = col_character(),
## .. `2021 [2021]` = col_character(),
## .. `2021Q1 [2021Q1]` = col_character(),
## .. `2021Q2 [2021Q2]` = col_character(),
## .. `2021Q3 [2021Q3]` = col_character(),
## .. `2021Q4 [2021Q4]` = col_character(),
## .. `2022 [2022]` = col_character(),
## .. `2022Q1 [2022Q1]` = col_character(),
## .. `2022Q2 [2022Q2]` = col_character(),
## .. `2022Q3 [2022Q3]` = col_character(),
## .. `2022Q4 [2022Q4]` = col_character(),
## .. `2023 [2023]` = col_character(),
## .. `2023Q1 [2023Q1]` = col_character(),
## .. `2023Q2 [2023Q2]` = col_character(),
## .. `2023Q3 [2023Q3]` = col_character(),
## .. `2023Q4 [2023Q4]` = col_character(),
## .. `2024 [2024]` = col_character(),
## .. `2024Q1 [2024Q1]` = col_character(),
## .. `2024Q2 [2024Q2]` = col_character(),
## .. `2024Q3 [2024Q3]` = col_character(),
## .. `2024Q4 [2024Q4]` = col_character()
## .. )
## - attr(*, "problems")=<externalptr># Reshape the data from wide to long format
Global_Economic_Monitor_long <- Global_Economic_Monitor %>%
pivot_longer(
cols = matches("^200\\d|^201\\d|^202\\d"), # Matches columns starting with 200, 201, or 202
names_to = "Year",
values_to = "Value"
)
Global_Economic_Monitor <- Global_Economic_Monitor_long
rm(Global_Economic_Monitor_long)# Clean the 'year' column by removing the part in brackets (e.g., "[2000]" or "[2000Q1]")
# Clean the 'Year' column
Global_Economic_Monitor <- Global_Economic_Monitor %>%
mutate(
Year = gsub("\\[.*\\]", "", Year), # Remove anything inside brackets
Year = gsub("Q", "-Q", Year), # Replace 'Q' with '-Q' for better readability
Year = trimws(Year) # Remove any leading or trailing spaces
)
sample_n(Global_Economic_Monitor, 10)## # A tibble: 10 × 6
## Country `Country Code` Series `Series Code` Year Value
## <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Lithuania LTU Imports Merch… DMGSRMRCHNSXD 2011… 1.23…
## 2 Estonia EST Imports Merch… DMGSRMRCHSACD 2005… 2401…
## 3 St. Kitts and Nevis KNA Exports Merch… DXGSRMRCHNSXD 2018… ..
## 4 Cambodia KHM GDP,constant … NYGDPMKTPSAKN 2024 ..
## 5 Haiti HTI Exchange rate… DPANUSSPB 2012 41.6…
## 6 Philippines PHL Nominal Effec… NEER 2014… ..
## 7 Sierra Leone SLE Imports Merch… DMGSRMRCHSACD 2003… 87.1…
## 8 Serbia and Montenegro YUG Exports Merch… DXGSRMRCHSACD 2014… ..
## 9 Georgia GEO Imports Merch… DMGSRMRCHSACD 2008 6328…
## 10 Isle of Man IMN Exports Merch… DXGSRMRCHNSXD 2009 ..# Define the mapping of countries to continents
countries <- c(
"Afghanistan", "Algeria", "Antigua and Barbuda", "Armenia", "Albania",
"Angola", "Argentina", "Australia", "Azerbaijan", "Aruba",
"Austria", "Bahamas The", "Bahrain", "Barbados", "Bangladesh",
"Belarus", "Belize", "Belgium", "Benin", "Bermuda",
"Bhutan", "Bosnia and Herzegovina", "Brazil", "Bulgaria", "Burundi",
"Cameroon", "Cape Verde", "Central African Republic", "Chile", "Colombia",
"Bolivia", "Botswana", "Brunei", "Burkina Faso", "Cambodia",
"Canada", "Cayman Islands", "Chad", "China", "Comoros",
"Congo Rep.", "Congo Dem. Rep.", "Costa Rica", "Croatia", "Cyprus",
"Cote d'Ivoire", "Cuba", "Denmark", "Djibouti", "Dominican Republic",
"Czech Republic", "Dominica", "El Salvador", "Ecuador", "Egypt Arab Rep.",
"Equatorial Guinea", "Eritrea", "Ethiopia", "Faeroe Islands", "Fiji",
"France", "Gabon", "Georgia", "Ghana", "Estonia",
"Finland", "French Polynesia", "Gambia The", "Germany", "Greece",
"Grenada", "Guinea", "Guyana", "Hong Kong China", "Iceland",
"Indonesia", "Iraq", "Isle of Man", "Greenland", "Guatemala",
"Guinea-Bissau", "Haiti", "Honduras", "Hungary", "India",
"Iran Islamic Rep.", "Ireland", "Israel", "Jamaica", "Jordan",
"Kenya", "Korea Rep.", "Kyrgyz Republic", "Lebanon", "Liberia",
"Lithuania", "Libya", "Lesotho", "Latvia", "Lao PDR",
"Kuwait", "Kiribati", "Japan", "Italy", "Kazakhstan",
"Luxembourg", "Macedonia FYR", "Malawi", "Maldives", "Malta",
"Mauritius", "Micronesia Fed. Sts.", "Mongolia", "Moldova", "Mexico",
"Mauritania", "Malaysia", "Madagascar", "Macao China", "Mali",
"Morocco", "Myanmar", "Nepal", "Netherlands Antilles", "New Zealand",
"Niger", "Norway", "Pakistan", "Papua New Guinea", "Peru",
"Mozambique", "Namibia", "Netherlands", "New Caledonia", "Nicaragua",
"Nigeria", "Oman", "Panama", "Paraguay", "Philippines",
"Somalia", "Slovenia", "Singapore", "Seychelles", "Senegal",
"Sao Tome and Principe", "Samoa", "Russian Federation", "Poland", "Qatar",
"Portugal", "Romania", "Rwanda", "San Marino", "Saudi Arabia",
"Serbia and Montenegro", "Sierra Leone", "Slovak Republic", "Solomon Islands", "South Africa",
"Spain", "St. Kitts and Nevis", "St. Vincent and the Grenadines", "Sudan", "Suriname",
"Sweden", "Syrian Arab Republic", "Tajikistan", "Thailand", "Togo",
"Timor-Leste", "Tanzania", "Taiwan China", "Switzerland", "Swaziland",
"St. Lucia", "Sri Lanka", "Tonga", "Tunisia", "Turkmenistan",
"Ukraine", "United Kingdom", "Vanuatu", "Vietnam", "Trinidad and Tobago",
"Turkey", "Uganda", "United Arab Emirates", "United States", "Uruguay",
"Uzbekistan", "Venezuela RB", "Virgin Islands (U.S.)", "Yemen Rep.", "Zimbabwe",
"Zambia"
)
continents <- c(
"Asia", "Africa", "North America", "Asia", "Europe",
"Africa", "South America", "Oceania", "Asia", "North America",
"Europe", "North America", "Asia", "North America", "Asia",
"Europe", "North America", "Europe", "Africa", "North America",
"Asia", "Europe", "South America", "Europe", "Africa",
"Africa", "Africa", "Africa", "South America", "South America",
"South America", "Africa", "Asia", "Africa", "Asia",
"North America", "North America", "Africa", "Asia", "Africa",
"Africa", "Africa", "North America", "Europe", "Europe",
"Africa", "North America", "Europe", "Africa", "North America",
"Europe", "North America", "North America", "South America", "Africa",
"Africa", "Africa", "Africa", "Europe", "Oceania",
"Europe", "Africa", "Asia", "Africa", "Europe",
"Europe", "Oceania", "Africa", "Europe", "Europe",
"North America", "Africa", "South America", "Asia", "Europe",
"Asia", "Asia", "Europe", "North America", "North America",
"Africa", "North America", "North America", "Europe", "Asia",
"Asia", "Europe", "Asia", "North America", "Asia",
"Africa", "Asia", "Asia", "Asia", "Africa",
"Europe", "Africa", "Europe", "Asia", "Africa", "Asia", "Oceania", "Asia", "Europe", "Asia",
"Europe", "Europe", "Africa", "Asia", "Europe",
"Africa", "Oceania", "Asia", "Europe", "North America",
"Africa", "Asia", "Africa", "Asia", "Africa",
"Africa", "Asia", "Asia", "North America", "Oceania",
"Africa", "Europe", "Asia", "Oceania", "South America",
"Africa", "Africa", "Europe", "Oceania", "North America",
"Africa", "Asia", "North America", "South America", "Asia",
"Africa", "Europe", "Asia", "Africa", "Africa",
"Africa", "Oceania", "Europe", "Europe", "Asia",
"Europe", "Europe", "Africa", "Europe", "Asia",
"Europe", "Africa", "Europe", "Oceania", "Africa",
"Europe", "North America", "North America", "Africa", "South America",
"Europe", "Asia", "Asia", "Asia", "Africa",
"Asia", "Africa", "Asia", "Europe", "Africa",
"North America", "Asia", "Oceania", "Africa", "Asia",
"Europe", "Europe", "Oceania", "Asia", "North America",
"Asia", "Africa", "Asia", "North America", "South America",
"Asia", "South America", "Oceania", "Asia", "Africa",
"Africa")
# Create the dataframe
countries_continents <- data.frame(Country = countries, Continent = continents)
#join the continent dataset to the Global Monitor dataset
Global_Economic_Monitor <- Global_Economic_Monitor %>%
left_join(countries_continents, by = "Country")
sample_n(Global_Economic_Monitor, 10)## # A tibble: 10 × 7
## Country `Country Code` Series `Series Code` Year Value Continent
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Portugal PRT CPI P… CPTOTSAXNZGY 2009… .. Europe
## 2 Senegal SEN GDP,c… NYGDPMKTPSAKD 2018… .. Africa
## 3 Gabon GAB Expor… DXGSRMRCHNSXD 2011 .. Africa
## 4 Lithuania LTU Excha… DPANUSSPF 2005… .. Europe
## 5 Italy ITA Expor… DXGSRMRCHSAKD 2024 .. Europe
## 6 Ireland IRL Impor… DMGSRMRCHNSCD 2023… 3709… Europe
## 7 Armenia ARM Expor… DXGSRMRCHNSCD 2000 294 Asia
## 8 Antigua and Barbuda ATG Unemp… UNEMPSA_ 2017… .. North Am…
## 9 United Kingdom GBR GDP,c… NYGDPMKTPSAKD 2015 2756… Europe
## 10 Sierra Leone SLE Excha… DPANUSSPF 2015 4308… Africa# Convert the 'Value' column to numeric, replacing '..' with NA
Global_Economic_Monitor <- Global_Economic_Monitor %>%
mutate(Value = ifelse(Value == "..", NA, as.numeric(Value)))extract only inflation monitors from the Global Monitor dataset and create inflation-data dataset
# Filter Core CPI and YoY CPI
inflation_data <- Global_Economic_Monitor %>%
filter(Series %in% c(
"Core CPI,seas.adj,,,",
"Core CPI,not seas.adj,,,",
"CPI Price, % y-o-y, median weighted, seas. adj.,",
"CPI Price, % y-o-y, not seas. adj.,"
)) %>%
mutate(
# Identify type: Quarterly or Annual
DataType = if_else(grepl("-Q", Year), "Quarterly", "Annual"),
# Create a unified Date column
Date = if_else(
grepl("-Q", Year),
as.Date(as.yearqtr(Year, format = "%Y-Q%q")), # Convert quarterly to Date
as.Date(paste0(Year, "-01-01")) # Convert annual to Date
),
# Extract the year
Year = if_else(DataType == "Quarterly", substr(Year, 1, 4), Year),
# Extract the quarter for quarterly data
Quarter = if_else(DataType == "Quarterly", substr(Year, 6, 7), NA_character_),
# Add a decade column
Decade = paste0(substr(Year, 1, 3), "0s")
)
# remove the NAs in the quarter column
inflation_data <- inflation_data %>%
mutate(
Quarter = if_else(
is.na(Quarter) & DataType == "Annual",
"Annual", # Placeholder for annual data
Quarter # Retain existing quarter values
)
)
# Check a sample of the updated dataset
sample_n(inflation_data, 10)## # A tibble: 10 × 11
## Country `Country Code` Series `Series Code` Year Value Continent DataType
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 Madagascar MDG Core … CORESA 2005 NA Africa Annual
## 2 Antigua a… ATG Core … CORENS 2007 NA North Am… Annual
## 3 French Po… PYF Core … CORENS 2013 NA Oceania Quarter…
## 4 Namibia NAM Core … CORENS 2024 NA Africa Quarter…
## 5 Zimbabwe ZWE Core … CORESA 2021 NA Africa Annual
## 6 Kyrgyz Re… KGZ Core … CORENS 2021 NA Asia Quarter…
## 7 Mali MLI Core … CORENS 2018 NA Africa Annual
## 8 Australia AUS Core … CORENS 2019 NA Oceania Annual
## 9 Iran Isla… IRN Core … CORENS 2010 NA Asia Annual
## 10 Sri Lanka LKA Core … CORESA 2010 NA Asia Annual
## # ℹ 3 more variables: Date <date>, Quarter <chr>, Decade <chr># Check for unique values in key columns
sapply(inflation_data, function(x) length(unique(x)))## Country Country Code Series Series Code Year Value
## 196 196 3 3 25 2983
## Continent DataType Date Quarter Decade
## 6 2 100 2 3# Check for missing values
colSums(is.na(inflation_data))## Country Country Code Series Series Code Year Value
## 0 0 0 0 0 70507
## Continent DataType Date Quarter Decade
## 0 0 0 0 0# Check for unique values in specific columns
unique(inflation_data$Series)## [1] "Core CPI,not seas.adj,,,"
## [2] "Core CPI,seas.adj,,,"
## [3] "CPI Price, % y-o-y, median weighted, seas. adj.,"unique(inflation_data$Continent)## [1] "Asia" "Africa" "North America" "Europe"
## [5] "South America" "Oceania"# Separate annual and quarterly data
annual_data <- inflation_data %>% filter(DataType == "Annual")
quarterly_data <- inflation_data %>% filter(DataType == "Quarterly")
#rename the annual colomn
annual_data <- annual_data %>%
rename("Annualy" = "Quarter") %>% #remove datatype colomn
select(-DataType) %>% #remove date colomn
select(-Date)
inflation_annual_data <- annual_data
#rename a colomn on quartely
quarterly_data <- quarterly_data %>%
rename("Quarterly" = "Quarter") %>% #remove datatype colomn
select(-DataType) %>% #remove date colomn
select(-Date)
inflation_quarterly_data <- quarterly_dataExplotory Data Analysis For Inflation Annual Data
# Visualize the missingness in the dataset
gg_miss_var(inflation_annual_data)
The Value variable seems to have many missing values
#Use a chi-square test to see if missingness depends on other variables.
chisq.test(table(is.na(inflation_annual_data$Value), inflation_annual_data$Country))##
## Pearson's Chi-squared test
##
## data: table(is.na(inflation_annual_data$Value), inflation_annual_data$Country)
## X-squared = 7517.7, df = 195, p-value < 2.2e-16Null Hypothesis : The missingness of the Value variable is independent of the Country variable.
Alternative Hypothesis: The missingness of the Value variable depends on the Country variable.
Since the p-value is much smaller than the common significance level (e.g., 0.05), we reject the null hypothesis. This implies that there is a statistically significant association between the missingness of Value and the Country variable.
#Check which country have higher proportions of missing values
prop.table(table(is.na(inflation_annual_data$Value), inflation_annual_data$Country),
margin = 2)##
## Afghanistan Albania Algeria Angola Antigua and Barbuda Argentina
## FALSE 0.0000000 0.4533333 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 0.5466667 1.0000000 1.0000000 1.0000000 1.0000000
##
## Armenia Aruba Australia Austria Azerbaijan Bahamas The
## FALSE 0.4933333 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 0.5066667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
##
## Bahrain Bangladesh Barbados Belarus Belgium Belize Benin
## FALSE 0.0000000 0.0000000 0.0000000 0.6133333 0.6666667 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 0.3866667 0.3333333 1.0000000 1.0000000
##
## Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
##
## Brazil Brunei Bulgaria Burkina Faso Burundi Cambodia
## FALSE 0.6666667 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 0.3333333 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
##
## Cameroon Canada Cape Verde Cayman Islands Central African Republic
## FALSE 0.0000000 0.6666667 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 0.3333333 1.0000000 1.0000000 1.0000000
##
## Chad Chile China Colombia Comoros Congo Dem. Rep.
## FALSE 0.0000000 0.4266667 0.5333333 0.6666667 0.0000000 0.0000000
## TRUE 1.0000000 0.5733333 0.4666667 0.3333333 1.0000000 1.0000000
##
## Congo Rep. Costa Rica Cote d'Ivoire Croatia Cuba Cyprus
## FALSE 0.0000000 0.5066667 0.0000000 0.5600000 0.0000000 0.6666667
## TRUE 1.0000000 0.4933333 1.0000000 0.4400000 1.0000000 0.3333333
##
## Czech Republic Denmark Djibouti Dominica Dominican Republic
## FALSE 0.6666667 0.6666667 0.0000000 0.0000000 0.6666667
## TRUE 0.3333333 0.3333333 1.0000000 1.0000000 0.3333333
##
## Ecuador Egypt Arab Rep. El Salvador Equatorial Guinea Eritrea
## FALSE 0.0000000 0.5600000 0.4266667 0.0000000 0.0000000
## TRUE 1.0000000 0.4400000 0.5733333 1.0000000 1.0000000
##
## Estonia Ethiopia Faeroe Islands Fiji Finland France
## FALSE 0.0000000 0.0000000 0.0000000 0.2133333 0.0000000 0.6666667
## TRUE 1.0000000 1.0000000 1.0000000 0.7866667 1.0000000 0.3333333
##
## French Polynesia Gabon Gambia The Georgia Germany Ghana
## FALSE 0.0000000 0.0000000 0.0000000 0.2000000 0.6666667 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 0.8000000 0.3333333 1.0000000
##
## Greece Greenland Grenada Guatemala Guinea Guinea-Bissau
## FALSE 0.6666667 0.0000000 0.0000000 0.6400000 0.0000000 0.0000000
## TRUE 0.3333333 1.0000000 1.0000000 0.3600000 1.0000000 1.0000000
##
## Guyana Haiti Honduras Hong Kong China Hungary Iceland
## FALSE 0.0000000 0.0000000 0.6666667 0.5600000 0.6666667 0.6666667
## TRUE 1.0000000 1.0000000 0.3333333 0.4400000 0.3333333 0.3333333
##
## India Indonesia Iran Islamic Rep. Iraq Ireland Isle of Man
## FALSE 0.0000000 0.4800000 0.0000000 0.0000000 0.6666667 0.0000000
## TRUE 1.0000000 0.5200000 1.0000000 1.0000000 0.3333333 1.0000000
##
## Israel Italy Jamaica Japan Jordan Kazakhstan Kenya
## FALSE 0.6666667 0.6666667 0.0000000 0.6666667 0.5066667 0.2400000 0.0000000
## TRUE 0.3333333 0.3333333 1.0000000 0.3333333 0.4933333 0.7600000 1.0000000
##
## Kiribati Korea Rep. Kuwait Kyrgyz Republic Lao PDR Latvia
## FALSE 0.0000000 0.6666667 0.0000000 0.2000000 0.0000000 0.3333333
## TRUE 1.0000000 0.3333333 1.0000000 0.8000000 1.0000000 0.6666667
##
## Lebanon Lesotho Liberia Libya Lithuania Luxembourg
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000 0.6666667 0.6666667
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000 0.3333333 0.3333333
##
## Macao China Macedonia FYR Madagascar Malawi Malaysia Maldives
## FALSE 0.0000000 0.5333333 0.0000000 0.0000000 0.5333333 0.0000000
## TRUE 1.0000000 0.4666667 1.0000000 1.0000000 0.4666667 1.0000000
##
## Mali Malta Mauritania Mauritius Mexico Micronesia Fed. Sts.
## FALSE 0.0000000 0.6666667 0.0000000 0.4800000 0.6666667 0.0000000
## TRUE 1.0000000 0.3333333 1.0000000 0.5200000 0.3333333 1.0000000
##
## Moldova Mongolia Morocco Mozambique Myanmar Namibia Nepal
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
##
## Netherlands Netherlands Antilles New Caledonia New Zealand Nicaragua
## FALSE 0.6666667 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 0.3333333 1.0000000 1.0000000 1.0000000 1.0000000
##
## Niger Nigeria Norway Oman Pakistan Panama
## FALSE 0.0000000 0.6666667 0.6666667 0.0000000 0.2000000 0.0000000
## TRUE 1.0000000 0.3333333 0.3333333 1.0000000 0.8000000 1.0000000
##
## Papua New Guinea Paraguay Peru Philippines Poland Portugal
## FALSE 0.0000000 0.6666667 0.6666667 0.0000000 0.6666667 0.6666667
## TRUE 1.0000000 0.3333333 0.3333333 1.0000000 0.3333333 0.3333333
##
## Qatar Romania Russian Federation Rwanda Samoa San Marino
## FALSE 0.0000000 0.5066667 0.6133333 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 0.4933333 0.3866667 1.0000000 1.0000000 1.0000000
##
## Sao Tome and Principe Saudi Arabia Senegal Serbia and Montenegro
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000
##
## Seychelles Sierra Leone Singapore Slovak Republic Slovenia
## FALSE 0.0000000 0.0000000 0.6666667 0.6133333 0.6666667
## TRUE 1.0000000 1.0000000 0.3333333 0.3866667 0.3333333
##
## Solomon Islands Somalia South Africa Spain Sri Lanka
## FALSE 0.0000000 0.0000000 0.6133333 0.6666667 0.0000000
## TRUE 1.0000000 1.0000000 0.3866667 0.3333333 1.0000000
##
## St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Sudan
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000
##
## Suriname Swaziland Sweden Switzerland Syrian Arab Republic
## FALSE 0.0000000 0.0000000 0.6666667 0.5333333 0.0000000
## TRUE 1.0000000 1.0000000 0.3333333 0.4666667 1.0000000
##
## Taiwan China Tajikistan Tanzania Thailand Timor-Leste Togo
## FALSE 0.6666667 0.0000000 0.0000000 0.6666667 0.0000000 0.0000000
## TRUE 0.3333333 1.0000000 1.0000000 0.3333333 1.0000000 1.0000000
##
## Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan
## FALSE 0.0000000 0.6666667 0.0000000 0.5866667 0.0000000
## TRUE 1.0000000 0.3333333 1.0000000 0.4133333 1.0000000
##
## Uganda Ukraine United Arab Emirates United Kingdom United States
## FALSE 0.5333333 0.0000000 0.0000000 0.6666667 0.6666667
## TRUE 0.4666667 1.0000000 1.0000000 0.3333333 0.3333333
##
## Uruguay Uzbekistan Vanuatu Venezuela RB Vietnam
## FALSE 0.0000000 0.0000000 0.0000000 0.2133333 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 0.7866667 1.0000000
##
## Virgin Islands (U.S.) Yemen Rep. Zambia Zimbabwe
## FALSE 0.0000000 0.0000000 0.0000000 0.0000000
## TRUE 1.0000000 1.0000000 1.0000000 1.0000000#visualize which country has completely missing data and which ones has partial missing data
ggplot(inflation_annual_data, aes(x = Country, fill = is.na(Value))) +
geom_bar(position = "fill") +
labs(y = "Proportion", fill = "Missing Value") +
theme_minimal() +
ggtitle("Proportion of Missing Values by Country")
# Filter out countries with 100% missing data
missing_annual_summary <- inflation_annual_data %>%
group_by(Country) %>%
summarize(total_missing = sum(is.na(Value)), total_records = n()) %>%
mutate(missing_percentage = total_missing / total_records)
# remove countries with <100% missing and leave countries with partial missing data
inflation_annual_data <- inflation_annual_data %>%
filter(Country %in% missing_annual_summary$Country[missing_annual_summary$missing_percentage < 1])
# Convert `Year` to numeric
inflation_annual_data$Year <- as.numeric(inflation_annual_data$Year)# Clean column names to make them syntactically valid
inflation_annual_data <- inflation_annual_data %>%
clean_names()#check how many percent of missing data do we have
aggr(inflation_annual_data)
we have more than 40% of the missing values on the Value variable which is alot of data, the best way to go about is to imputate data than removing it
#lets use the CART method to imputate data
predictor_matrix_annual <- make.predictorMatrix(inflation_annual_data)
#CART imputation
set.seed(123)
annual_imputation_cart <- mice(inflation_annual_data, m = 5, method = "cart")##
## iter imp variable
## 1 1 value
## 1 2 value
## 1 3 value
## 1 4 value
## 1 5 value
## 2 1 value
## 2 2 value
## 2 3 value
## 2 4 value
## 2 5 value
## 3 1 value
## 3 2 value
## 3 3 value
## 3 4 value
## 3 5 value
## 4 1 value
## 4 2 value
## 4 3 value
## 4 4 value
## 4 5 value
## 5 1 value
## 5 2 value
## 5 3 value
## 5 4 value
## 5 5 value#check the imputations CART
annual_imputation_cart$imp$value## 1 2 3 4 5
## 1 84.15973 88.93484 54.27991 91.29758 69.44062
## 2 81.69164 86.59202 83.45336 87.33405 86.93202
## 3 88.96352 83.06365 72.74734 86.72466 76.81953
## 4 89.34008 86.73150 82.68579 91.50520 92.43986
## 5 90.64730 90.20065 90.74173 94.70750 68.72902
## 6 85.13210 101.08586 92.22074 79.68046 90.38887
## 26 84.31220 91.29758 84.05367 48.49429 77.81180
## 27 86.50740 76.38382 81.93143 60.65947 105.36680
## 28 87.79150 97.43460 93.66601 84.35955 83.06365
## 29 78.11804 79.00953 64.89455 89.34008 90.74173
## 30 76.93336 63.88924 92.13118 87.73624 64.21370
## 31 84.04155 75.51250 94.13769 85.46920 81.36691
## 50 129.92929 130.04505 222.04033 127.11430 265.24127
## 51 74.49210 87.53040 80.80704 79.54693 92.01060
## 52 79.62324 69.44062 76.81953 80.88184 54.07719
## 53 103.13131 60.65947 88.74066 90.63433 55.25180
## 54 61.05559 92.43986 95.73724 95.99940 70.34297
## 55 70.47614 53.45668 51.42515 59.30769 94.96576
## 56 101.08586 81.36691 97.36059 74.58034 97.04058
## 57 90.42765 99.33319 92.72501 94.04443 95.08687
## 58 83.82353 76.31949 57.27377 93.54729 101.77485
## 59 99.50617 91.33018 92.99362 97.53524 84.39734
## 60 102.33326 97.46060 100.35603 98.47596 101.16487
## 61 99.09979 101.00116 96.37148 97.49152 100.56819
## 62 104.64498 143.70360 104.98080 101.19407 104.18608
## 63 102.86540 108.71738 176.17889 105.96919 108.71738
## 64 103.93445 104.51977 113.20401 115.81716 111.17116
## 65 106.69451 105.80979 104.04960 120.33720 107.00910
## 66 104.13499 111.55464 106.01707 103.10562 338.64140
## 67 125.63590 107.90621 101.13796 171.38298 114.84006
## 68 125.17527 163.02863 103.32460 120.27348 113.70289
## 69 169.43669 105.68000 175.39744 111.90085 138.61044
## 70 115.63598 154.44546 136.74067 136.42447 105.61515
## 71 110.92591 136.66000 153.95683 269.03839 139.84188
## 72 225.69219 106.92900 118.33059 100.20202 193.74717
## 73 147.99678 131.22664 126.42586 164.06744 126.98589
## 74 298.66443 121.29630 132.01165 155.95655 103.33840
## 75 694.06484 139.11052 121.51767 188.21775 161.41631
## 76 89.20607 39.70148 74.82542 63.88756 85.54361
## 77 80.42268 103.13131 83.00664 79.51430 84.31220
## 78 60.03765 88.52320 88.98440 91.12610 84.86304
## 79 64.21370 82.88179 83.29147 75.65387 88.95131
## 80 79.31961 87.77418 64.83246 59.30769 62.68866
## 81 95.08687 99.01804 95.08687 82.70288 84.65185
## 82 88.75352 94.49291 91.27871 76.71611 81.19623
## 83 73.98322 91.12778 98.10208 84.21325 94.49291
## 101 76.81953 85.21347 82.76681 85.73580 60.57741
## 102 65.23464 87.17744 82.16888 85.15462 85.15462
## 103 92.17841 79.11190 81.93143 89.62937 76.45865
## 104 92.03260 79.31961 63.14192 70.11448 73.29890
## 105 91.35638 92.64793 89.91046 86.73150 92.72349
## 106 92.30889 88.10025 89.08903 69.46001 89.40975
## 107 79.46693 69.46001 95.82647 92.99346 93.51373
## 108 96.47162 82.74496 93.60593 81.36691 101.49594
## 126 84.15973 66.61348 77.84784 81.40956 89.62937
## 127 84.13422 84.16547 79.47932 91.95899 70.92046
## 128 57.40160 63.88756 50.76591 62.21331 92.38744
## 129 102.22222 92.13118 91.87575 85.50989 102.22222
## 130 92.43411 82.68579 92.75428 63.88924 90.48983
## 131 87.31443 95.86473 93.25658 81.95826 92.07572
## 132 96.59448 81.34965 87.22890 93.20396 97.48628
## 133 82.59775 69.57160 79.43407 95.24657 93.51373
## 134 98.34969 95.08520 88.01447 98.67298 98.72624
## 135 98.93923 99.10218 99.87192 98.78481 100.57566
## 136 98.93923 102.07249 100.64034 99.09979 101.05372
## 137 104.18608 100.81512 102.12298 101.35203 102.02099
## 138 104.38677 109.90148 176.17889 108.71738 105.63353
## 139 107.23509 111.22638 121.87968 106.04066 116.59730
## 140 109.72657 113.14683 104.58508 163.72257 122.76389
## 141 123.31008 104.58508 105.80979 119.29708 114.52303
## 142 120.80224 114.57763 124.61969 144.16979 445.05135
## 143 124.92263 173.10494 98.76626 111.88497 162.59319
## 144 110.11771 263.32021 175.68796 102.49127 116.94994
## 145 136.26939 118.41038 123.39230 116.84843 105.37942
## 146 117.26530 504.61556 99.45656 113.64010 126.04950
## 147 115.23936 131.62354 225.69219 118.33059 117.87264
## 148 146.18218 197.99311 130.73546 159.22729 106.95611
## 149 180.95638 179.23003 298.66443 168.40951 111.39208
## 150 136.08946 694.06484 144.07929 107.80223 112.80796
## 151 103.13131 76.78643 55.25180 87.17744 48.57916
## 152 91.29758 92.38744 82.63836 60.57741 37.81895
## 176 41.91126 85.65931 74.07757 84.43896 83.04769
## 177 95.58136 86.93202 76.43678 84.43896 86.30683
## 201 84.86304 62.27695 76.45865 84.99905 79.51359
## 202 81.93143 75.04270 103.54970 68.22153 68.22153
## 203 36.84186 76.45865 91.23674 88.96352 86.30683
## 204 70.83933 91.65234 94.78517 76.96681 76.00061
## 205 90.11586 93.06302 76.96681 91.27376 92.64793
## 206 83.40701 72.84820 97.40585 79.43407 84.65185
## 207 95.12441 95.58712 93.82111 84.34966 88.80377
## 208 93.45691 73.98322 73.98322 89.08083 81.62803
## 209 86.69231 94.08903 89.81541 95.33453 89.81541
## 210 103.99275 100.18869 99.27417 101.38605 100.66260
## 211 100.82659 98.27250 99.11064 100.14811 99.73802
## 212 101.82865 101.30346 124.71287 112.94194 111.49880
## 213 110.10368 127.01718 101.95037 103.83656 110.38870
## 214 107.23509 104.63845 146.30854 110.83466 110.43121
## 215 106.19343 104.32306 106.19239 157.19087 117.47672
## 216 109.38277 107.46319 110.40044 111.65145 107.72406
## 217 443.99619 113.94137 100.98039 108.81487 107.03457
## 218 108.81487 110.73692 118.79881 114.64604 107.68255
## 219 115.99308 149.15188 123.39230 118.15403 119.67466
## 220 116.94994 118.35876 119.22506 106.49688 110.33821
## 221 173.03177 161.96111 129.18871 281.71217 99.45656
## 222 160.95923 281.25637 117.18814 133.41070 164.53501
## 223 152.26835 121.63828 120.94487 162.66276 100.97778
## 224 180.75454 144.42358 162.55739 120.94736 131.79459
## 225 113.97262 264.08547 121.51767 123.84166 139.05238
## 276 60.03765 95.99851 87.39805 87.17744 95.99851
## 277 82.18296 85.57782 55.46753 54.27991 91.23674
## 278 89.20607 98.00046 86.17021 87.17744 54.23804
## 279 68.22087 91.44621 89.12743 87.84210 92.43411
## 280 87.74308 89.18316 91.02956 92.14181 79.07915
## 281 85.51540 69.57160 97.06889 88.66208 70.82698
## 282 97.79130 98.73440 85.50475 81.14328 93.55041
## 283 65.73624 96.35980 81.34965 94.18004 93.44149
## 284 96.36786 80.81964 91.49750 94.67902 91.38998
## 285 102.33326 100.50041 100.72108 101.50892 99.62979
## 286 100.11052 104.63885 95.71676 100.37677 97.93814
## 287 102.31760 98.62238 101.33319 106.15890 102.35425
## 288 103.14673 103.14673 108.22561 103.28180 105.45455
## 289 104.82608 110.24742 102.18154 106.29270 115.49029
## 290 99.26165 119.29708 112.66109 113.36324 109.72657
## 291 150.95828 124.49640 112.72938 133.20341 128.58474
## 292 107.03457 114.28594 111.56177 108.26279 102.72149
## 293 111.63016 108.67903 131.96356 128.31784 105.50042
## 294 137.21213 117.94491 185.25808 178.11105 118.24087
## 295 101.10717 109.74483 111.88715 127.86717 459.90827
## 296 110.19345 134.55432 141.46502 159.63346 101.02694
## 297 148.00592 136.02601 135.68230 117.37784 201.07692
## 298 113.55850 113.55850 100.97778 113.60385 116.51684
## 299 155.95655 169.95212 139.77087 144.57935 156.68950
## 300 130.28090 146.34694 136.95795 129.84296 162.00465
## 351 89.62937 79.47932 60.03765 89.35444 87.22372
## 352 88.03280 97.69301 86.17021 86.83467 82.24845
## 353 89.49587 86.53435 54.07719 95.62290 86.87865
## 354 89.91046 81.19130 75.74804 68.71703 79.07915
## 355 86.40904 82.68579 89.34008 71.60730 51.42515
## 356 91.06227 92.87873 81.36691 95.40466 81.56326
## 357 81.02273 89.08083 94.13769 88.04751 93.63286
## 358 88.66208 97.18258 93.41064 65.86113 85.46920
## 359 100.57932 96.80581 83.11151 101.52975 94.67902
## 360 100.05960 103.18209 98.53717 101.42751 101.38605
## 361 99.73802 97.64966 105.05805 100.50041 101.50892
## 362 101.41742 98.94547 104.24491 103.28508 109.08737
## 363 110.07958 97.65152 103.14673 128.88305 104.11632
## 364 106.79215 107.78203 110.83466 111.17116 107.23509
## 365 108.26402 105.18351 107.06580 108.02726 106.15231
## 366 99.26165 107.70150 103.95877 106.94296 111.82715
## 367 163.02863 110.77789 109.17025 105.34240 116.01471
## 368 111.17364 226.14411 136.54922 103.32460 113.61904
## 369 169.43669 142.76316 109.77954 110.35033 119.59454
## 370 149.90408 138.90371 112.23080 175.39744 102.82229
## 371 146.31352 109.61199 252.64430 139.51296 141.39107
## 372 117.18814 100.20202 280.86383 117.18814 300.11344
## 373 107.00071 146.12892 147.99678 146.12892 100.97778
## 374 155.62040 161.42447 121.29630 131.94135 194.26159
## 375 141.84511 142.73615 136.08946 138.72838 265.24127
## 376 41.56536 103.13131 89.67846 91.12610 88.96352
## 377 86.69055 82.16888 62.27695 104.95791 56.17321
## 378 79.72385 80.88184 79.88844 69.44062 87.96539
## 379 51.50991 82.68579 90.71247 91.44621 65.48751
## 380 90.41604 89.23611 92.16046 92.56843 92.52781
## 381 90.84525 95.50932 94.30423 76.36179 90.42765
## 382 87.94957 97.54756 93.45691 84.65185 97.49668
## 383 81.59536 94.67286 97.48628 92.87515 85.03254
## 384 94.85522 95.99433 96.31460 98.79779 91.29703
## 401 92.38744 91.95899 77.28096 45.05271 89.35444
## 402 76.38382 50.76591 89.76592 95.99851 76.43678
## 403 54.23804 87.53040 56.17321 84.57300 87.80122
## 404 76.00061 93.31453 92.40642 103.24544 75.91254
## 405 91.46595 93.31453 79.31961 85.01096 85.47067
## 406 96.28103 95.50932 81.44024 93.30245 96.84995
## 407 68.03409 96.46720 77.71553 94.20589 97.43797
## 408 95.58712 94.20589 85.50475 95.06454 65.73624
## 409 91.29703 98.59710 96.36786 97.89099 96.28845
## 426 87.51037 85.35007 87.80122 90.63433 91.29758
## 427 80.42268 104.02357 50.76591 87.68738 72.67589
## 428 74.10659 80.32086 75.04270 85.58530 60.67146
## 429 86.61646 53.67943 53.96413 83.29147 94.96020
## 430 92.43411 92.56843 81.19130 82.68579 89.58921
## 431 101.49594 92.72501 93.08193 93.20106 97.61660
## 432 57.27377 84.00430 94.20244 65.86113 81.56326
## 433 96.43636 96.56863 72.84820 87.22890 96.46720
## 434 97.91838 96.30855 98.67298 94.67902 95.08520
## 435 98.86450 99.36712 93.82459 99.75840 99.55544
## 436 100.82826 99.09979 101.19430 99.75840 100.82826
## 437 104.24491 100.99355 106.57789 106.56742 100.99107
## 438 110.38870 103.53723 105.82849 104.06422 111.40121
## 439 111.51049 114.26689 112.62179 105.11212 120.59903
## 440 106.40525 107.32870 106.44959 105.81129 108.04976
## 441 120.63251 154.11132 99.10803 111.06992 106.01707
## 442 108.81487 128.27916 127.07225 111.17364 421.37245
## 443 141.53477 108.18084 108.64252 123.02067 109.81380
## 444 106.27945 105.68665 101.06061 110.16721 133.42357
## 445 123.60592 99.95210 111.00068 141.09160 110.16721
## 446 253.72386 126.34791 139.51296 121.11444 139.10761
## 447 127.71320 107.26161 118.31005 134.16978 113.53051
## 448 100.74375 121.45621 145.60938 323.38504 371.76418
## 449 180.95638 144.42358 131.94135 134.46696 127.89719
## 450 138.58183 130.08440 130.08440 145.84562 149.47301
## 501 39.64751 74.07757 87.68738 87.11872 89.92114
## 502 89.07701 92.01060 80.42268 67.53835 57.46010
## 503 58.63290 90.62159 89.46176 89.62937 86.93202
## 504 94.38345 97.01757 89.12614 76.00061 91.35638
## 505 89.12614 89.34008 103.24544 102.63692 85.84441
## 506 82.36210 81.56326 92.99346 96.90043 100.69083
## 507 91.62297 97.95033 94.31004 72.84820 99.88840
## 508 85.10674 92.93456 94.63581 73.79112 89.08083
## 509 99.79936 91.23322 95.48108 99.83715 93.37227
## 510 100.51467 101.16487 99.98555 97.49152 99.73802
## 511 99.38790 99.12850 96.83548 99.27417 98.64589
## 512 104.30985 106.57789 102.12298 107.63287 102.93651
## 513 99.22778 104.01260 101.90236 104.42528 98.87984
## 514 137.90473 105.62299 115.81716 108.25918 104.63845
## 515 105.34078 140.79868 112.77843 123.45897 106.44959
## 516 104.98236 133.86898 106.44959 100.67826 104.98236
## 517 114.56494 173.10494 124.92916 104.69508 105.34240
## 518 117.52451 107.13503 99.10555 120.90543 110.65562
## 519 118.35876 113.34587 120.17103 149.13311 132.73306
## 520 231.53177 105.04348 129.13351 114.94452 117.56930
## 521 123.79254 107.86507 131.98531 124.28462 119.21853
## 522 133.41070 147.34885 127.98187 125.55251 101.07600
## 523 119.61698 133.39165 126.33907 133.39165 126.30539
## 524 247.11923 123.89558 183.04556 123.89558 161.07717
## 525 127.11430 552.45355 135.65222 123.84166 113.97262
## 576 89.46176 65.23464 86.31604 69.00621 62.21331
## 577 84.16547 87.19847 87.79150 60.45340 49.97188
## 578 91.22254 74.07757 85.65931 80.32086 70.92046
## 579 54.27027 88.95131 79.00953 91.76680 81.19130
## 580 91.46595 93.17554 102.81987 87.77418 53.96413
## 581 94.30423 96.90043 93.82111 76.94358 88.90840
## 582 95.14822 95.24657 96.99243 95.50927 98.35328
## 583 83.82353 96.35391 94.95988 95.09096 95.50927
## 584 91.38998 88.01447 98.21780 98.72624 97.25576
## 585 96.69969 102.72571 102.07249 101.84448 100.41002
## 586 101.27397 101.27936 100.05478 104.30909 99.64087
## 587 101.49203 101.88398 102.23617 104.18450 102.91591
## 588 114.83120 106.16602 104.09200 117.06788 109.57484
## 589 105.97367 112.37245 297.32487 107.23509 107.37269
## 590 104.39012 118.32347 112.66109 120.56460 105.31331
## 591 107.65160 133.41114 158.28096 163.72257 108.59176
## 592 115.48170 105.29668 109.17025 100.39817 102.72149
## 593 114.96845 103.32460 105.41559 111.20249 104.83339
## 594 120.41282 148.29801 128.64430 120.19411 132.04917
## 595 126.30030 116.14556 263.32021 105.68000 120.19411
## 596 114.82032 112.70218 126.65652 503.42939 123.00981
## 597 106.92900 117.92223 121.26949 106.92900 121.11605
## 598 117.70833 147.99678 146.18218 323.38504 122.30426
## 599 123.01758 247.11923 125.83247 155.69308 107.59025
## 600 136.95795 130.04505 123.52662 113.17387 146.90039
## 601 82.16888 87.80122 89.59827 76.38382 87.17744
## 602 84.86304 82.47773 65.10216 86.69055 41.91126
## 603 84.15973 93.96598 81.40956 104.95791 87.06867
## 604 92.14181 91.71909 91.70512 73.45181 88.88347
## 605 79.87391 73.45181 93.80379 80.66573 63.88924
## 626 65.23464 80.80704 54.29599 87.06867 41.68769
## 627 58.63290 86.87865 83.26203 81.69164 77.73452
## 628 90.98065 90.06059 86.50740 87.51037 76.54764
## 629 76.00061 67.96218 79.07915 92.72349 89.58921
## 630 80.66573 79.00953 85.01780 70.34297 91.78669
## 651 83.26203 85.84290 76.81953 74.07757 57.46010
## 652 85.54361 66.58774 39.70148 95.62290 84.35955
## 653 85.54361 95.62290 87.80122 45.05271 87.17744
## 654 77.60870 87.73624 68.22087 69.59554 79.39866
## 655 96.38065 95.78239 78.11804 85.41806 92.75428
## 656 83.47741 74.58034 79.43407 98.37955 92.27130
## 657 93.41064 95.52910 95.06454 95.70887 94.31041
## 658 97.06502 90.71457 93.48537 92.87873 92.43007
## 659 98.03552 97.51408 92.99362 96.28845 98.03552
## 660 103.99275 97.46060 98.83097 96.34616 102.70549
## 661 99.61333 99.36712 100.71676 103.49682 99.38790
## 662 104.44993 116.80319 103.27481 105.61495 101.35203
## 663 104.26492 104.25807 116.41026 104.01260 102.10524
## 664 99.28725 297.32487 105.97255 110.08035 106.56318
## 665 111.10829 112.30907 148.10349 105.12161 105.33277
## 666 114.57970 113.14683 105.80979 106.86974 108.13149
## 667 117.21355 105.34240 112.42533 113.96239 103.20968
## 668 110.54282 99.10555 116.13119 130.86886 171.10256
## 669 109.88743 103.63813 121.15220 109.40244 122.56407
## 670 105.36666 117.88098 116.77544 109.88743 102.82229
## 671 124.30313 112.70218 114.46560 115.73388 106.62682
## 672 125.83242 111.59231 127.71320 164.53501 127.09092
## 673 234.31872 159.31357 100.33670 115.92970 121.63828
## 674 115.24018 122.70613 140.19862 155.56938 137.91349
## 675 172.26418 125.10588 104.05021 203.22596 146.34694
## 676 82.76681 48.49429 79.51359 92.01060 86.83467
## 677 63.96155 55.25180 68.22153 82.18296 94.43840
## 678 86.69055 77.84784 86.24029 84.16547 50.76923
## 679 93.06302 94.46961 89.34008 93.17554 53.45668
## 680 88.95131 76.00061 92.35943 91.33736 75.51253
## 681 96.90043 86.12273 88.66208 97.49668 92.38893
## 701 49.97188 55.25180 70.89381 85.65931 94.96854
## 702 89.76592 86.31604 84.15973 75.04270 84.18221
## 703 80.42268 84.31220 69.07252 82.16888 87.11872
## 704 65.97222 90.40701 91.78669 87.60438 92.64793
## 705 84.62964 91.65234 91.78296 95.99940 88.67350
## 706 89.40975 88.39372 81.95826 95.68366 91.81731
## 726 90.82841 97.99331 83.06365 96.04111 89.29480
## 727 82.18296 88.03280 76.81953 93.66601 93.92426
## 728 89.62937 83.04769 85.65931 76.45865 87.17744
## 729 92.43986 80.66573 91.50520 80.47166 84.59334
## 730 89.23611 77.44490 92.92963 64.83246 97.01757
## 731 96.48148 95.74852 88.96866 96.59448 94.24815
## 732 69.95766 100.72745 99.46346 90.44223 95.29390
## 733 88.96866 80.12444 75.51250 85.46920 81.93557
## 734 96.61032 96.03599 97.48328 93.53041 98.92178
## 735 97.96420 97.93814 106.21088 101.44396 102.59875
## 736 101.00116 100.83501 101.52984 97.57504 100.16114
## 737 106.82686 105.05959 102.21498 105.05959 104.64498
## 738 105.86541 104.67952 110.69491 99.85425 104.67952
## 739 106.04066 106.79215 110.43121 108.37045 141.25711
## 740 107.19246 120.33720 108.46440 107.45388 105.81129
## 741 107.66647 110.42647 163.72257 107.66647 101.38032
## 742 110.76400 115.02123 106.70624 171.10256 106.26664
## 743 106.83120 114.70431 142.27293 114.09806 106.20255
## 744 109.52637 119.77823 99.61235 109.16456 130.64683
## 745 169.43669 101.64838 107.54896 117.22208 148.29801
## 746 112.05781 116.81906 188.53177 106.62682 110.92591
## 747 119.04110 118.33059 298.75559 118.87613 160.95923
## 748 119.52787 124.46827 140.40671 131.03259 323.38504
## 749 132.59326 183.04556 110.81273 132.40925 121.40603
## 750 204.07685 264.45420 222.04033 117.42197 136.81357
## 751 37.61926 90.62159 80.88184 45.05271 88.52178
## 752 86.69055 85.58530 83.45336 84.57300 60.67146
## 753 76.43678 68.22153 76.96728 83.45336 56.17321
## 754 91.73298 65.48751 70.34297 92.13118 85.84441
## 776 82.16888 86.59730 82.16888 72.74734 70.22650
## 777 77.34781 63.12598 77.84784 81.96664 87.79150
## 778 72.67589 75.67416 66.56782 91.95899 37.61926
## 779 90.14350 92.14181 94.96576 71.43483 87.74308
## 801 84.86304 55.25180 69.07252 62.27695 87.20973
## 802 84.16547 49.97188 77.26177 104.44557 97.99331
## 803 88.98440 87.17744 86.83467 64.04461 90.06059
## 804 79.91192 97.33569 64.21370 69.59554 89.91046
## 805 82.88179 64.89455 91.51116 54.27027 93.80379
## 806 88.66208 96.47162 100.24863 81.44024 88.02860
## 807 87.22890 67.14593 95.01535 65.86113 93.19046
## 808 99.46346 72.44891 100.24863 92.03243 81.34965
## 809 89.81541 99.11068 101.52975 93.89692 95.99433
## 810 99.89300 99.56051 100.83501 99.00218 97.82476
## 811 99.41273 102.33326 99.11660 101.05028 97.59288
## 812 101.78127 101.19407 103.33463 103.72201 104.54117
## 813 131.62010 105.82849 111.19878 102.11598 104.13530
## 814 106.44586 102.84426 146.43261 105.73638 108.05007
## 815 106.52577 103.95877 133.41114 129.29251 107.32870
## 816 155.66885 104.27782 102.84663 100.70149 112.72938
## 817 138.15359 114.81305 115.60556 125.63590 166.15643
## 818 105.57382 101.05646 107.69149 144.16979 111.42145
## 819 121.43812 109.22285 128.64430 201.13498 149.89209
## 820 117.27757 105.36666 119.77823 119.67466 175.68796
## 821 136.66000 269.03839 124.26543 139.51296 106.35650
## 822 119.04110 116.78040 113.34752 113.34752 117.92223
## 823 124.85976 152.11439 150.27818 182.98188 641.17255
## 824 144.24659 180.75454 120.74235 155.47134 183.04556
## 825 131.58517 132.68267 161.41631 136.95795 161.41631
## 876 87.24826 95.99851 67.53835 90.62159 52.63789
## 877 87.06867 86.83467 60.67146 68.38764 87.49543
## 878 104.44557 76.45865 88.52320 39.64751 85.65931
## 879 93.17554 92.56843 91.02956 65.48751 89.16226
## 880 96.04200 94.38345 77.44490 92.64793 86.40904
## 881 87.31443 95.52910 81.02273 94.01651 96.38773
## 882 95.14822 69.57160 95.01535 82.13950 94.20244
## 883 93.56191 95.31668 78.89058 76.84017 80.53962
## 884 87.92436 102.16049 91.23322 98.04965 99.43150
## 885 100.05478 87.53341 100.82826 101.77396 101.77396
## 886 98.47596 100.56819 101.97148 100.75329 98.37455
## 887 164.68155 103.21992 105.59849 101.98221 103.12208
## 888 105.82849 137.64899 104.23632 110.93115 114.50839
## 889 103.29765 104.85046 105.77540 106.79215 105.58494
## 890 107.82718 107.19246 109.80112 118.32347 109.88184
## 891 150.83178 106.65586 339.67420 103.29518 102.84663
## 892 443.99619 120.27348 112.14252 114.28594 111.90901
## 893 118.77751 138.15359 101.12053 101.05646 102.72149
## 894 183.98454 102.82229 263.32021 119.22506 101.47904
## 895 121.15220 113.99420 110.11968 117.60652 117.56930
## 896 131.76436 117.26530 101.38071 110.92591 129.18871
## 897 120.01017 116.98047 136.02601 109.59070 193.74717
## 898 147.78778 140.05422 152.11439 118.14756 126.67551
## 899 166.21369 144.42358 118.87561 162.55739 143.27033
## 900 165.32223 123.52662 222.04033 161.41631 133.98900
## 951 82.16888 80.05830 84.18221 90.82841 87.26977
## 952 60.57741 96.04111 85.54361 78.65690 89.35444
## 953 85.95653 74.07757 76.43678 87.13276 97.73589
## 954 51.50991 70.34297 66.86484 89.34008 97.63935
## 955 70.34297 91.35638 85.38321 93.80379 75.51253
## 956 75.63255 92.47394 101.30804 93.20106 89.32484
## 957 76.31949 88.96609 91.86796 83.82353 82.13950
## 958 95.40466 97.97282 82.59775 95.40466 91.04108
## 959 97.55989 98.34969 93.24469 86.57403 98.79779
## 960 102.77447 98.35841 101.00116 101.78553 104.63885
## 961 101.53984 100.14188 101.14326 100.65492 100.69733
## 962 104.69265 105.05959 106.70248 124.71287 104.24491
## 963 100.90090 260.74334 109.90148 104.99403 110.69491
## 964 108.52239 115.49029 103.35298 100.39438 116.59730
## 965 112.72046 109.05125 103.07126 113.14683 102.37739
## 966 107.82718 128.58474 132.51117 102.41684 112.55576
## 967 111.20249 105.38898 166.15643 121.33839 172.70436
## 968 109.25246 107.59355 125.96739 173.80710 116.22330
## 969 105.61515 108.26185 117.45057 117.27757 119.89695
## 970 105.61515 182.71795 105.68665 111.40073 120.42606
## 971 111.41676 117.26530 101.02694 116.11264 188.46154
## 972 143.70353 147.53804 148.02632 133.75443 120.49509
## 973 130.19223 118.14450 126.42586 132.05508 322.99700
## 974 136.96747 132.59326 131.79459 140.11829 155.62316
## 975 162.00465 125.25585 117.74875 694.06484 129.60765
## 1026 49.97188 69.44062 83.11619 79.11190 58.84880
## 1027 65.23464 37.61926 59.26252 84.31220 82.14150
## 1028 50.76923 87.53040 89.35444 94.43840 89.20607
## 1029 79.31961 87.77418 78.95833 85.41806 78.11804
## 1030 85.38321 94.78517 87.73624 79.07915 91.70512
## 1031 91.04108 73.59809 95.68366 99.01258 91.54061
## 1032 90.82499 94.31041 93.30245 81.34965 95.50927
## 1033 88.75352 79.41074 92.71848 94.36870 89.33854
## 1034 99.11068 96.80581 91.23322 101.11111 92.99362
## 1035 99.83780 100.64270 104.30909 101.14849 95.14710
## 1036 97.54033 103.30935 101.49803 101.82777 98.86450
## 1037 100.81512 101.03491 107.49766 102.23617 101.19407
## 1038 104.13554 105.38233 116.41026 104.23632 104.32099
## 1039 104.90277 111.51049 106.44586 109.15415 107.37269
## 1040 108.80848 121.95756 102.37739 102.75253 114.52303
## 1041 112.72046 103.64503 103.29518 103.37193 110.84700
## 1042 172.79024 135.20384 100.58081 103.62654 108.26279
## 1043 108.81487 138.67541 115.08633 108.18084 143.61967
## 1044 263.32021 113.40656 263.32021 127.86717 141.37796
## 1045 113.34587 118.35876 111.00068 149.71884 109.89934
## 1046 131.79286 503.42939 119.31776 123.54022 114.46560
## 1047 113.34752 114.28304 100.20202 160.98321 193.57454
## 1048 129.88913 173.05755 144.79541 115.76390 112.59431
## 1049 132.78361 138.41941 130.87151 120.94736 156.67740
## 1050 141.93627 136.81357 558.52660 138.56469 148.70509
## 1101 86.53435 50.76923 83.26203 87.79150 95.62290
## 1102 85.35007 84.13422 87.80122 67.53835 81.88533
## 1103 74.49210 89.86462 80.07062 54.27991 54.07719
## 1104 93.63502 53.58974 89.70028 92.43986 91.76680
## 1105 85.01780 80.92282 80.48137 90.71247 84.62964
## 1106 84.30664 92.03243 94.95988 94.49291 76.36179
## 1107 101.08586 98.10208 86.07052 75.51250 98.10208
## 1108 96.90043 96.35391 96.59448 76.94358 76.24359
## 1109 98.73339 92.99362 99.11068 100.72271 97.54853
## 1110 100.12157 102.33326 99.75840 97.43512 99.69388
## 1111 100.05960 100.69733 99.71523 100.14811 101.52495
## 1112 103.27481 98.62238 103.03193 102.59787 112.73950
## 1113 104.09200 104.06422 260.74334 106.10577 118.19123
## 1114 111.22638 115.59832 101.96945 114.26689 108.52239
## 1115 112.77843 114.70735 103.64503 107.60348 122.51773
## 1116 112.72938 142.50793 106.19239 380.88026 99.10803
## 1117 136.54922 124.92916 174.29516 121.48413 105.38898
## 1118 107.90621 120.80224 113.17658 124.92916 134.81898
## 1119 120.17103 117.56930 105.72636 118.56621 120.42606
## 1120 168.95985 117.94491 113.83465 102.82229 148.29801
## 1121 119.31776 119.21853 123.54022 504.61556 111.87852
## 1122 113.53051 148.34477 112.31792 117.87264 280.86383
## 1123 122.01325 146.12892 126.98589 120.94487 119.52787
## 1124 123.77131 136.73844 246.70513 143.27033 118.87561
## 1125 123.11356 1158.86006 160.34981 171.68524 129.60765
## 1126 69.00621 87.96539 78.65690 86.69055 82.37310
## 1127 96.04111 89.76592 75.04270 79.47972 80.21945
## 1128 86.53435 86.84609 57.46010 68.38764 82.14150
## 1129 78.52077 89.58921 93.75936 90.40701 91.27376
## 1130 63.37863 89.79684 85.38321 87.60438 90.41604
## 1131 69.57160 98.30169 89.33854 89.18050 94.24815
## 1132 97.40585 95.58712 68.03409 94.63581 96.99243
## 1133 95.06454 95.01535 74.04314 92.27130 85.50475
## 1134 100.57932 96.73800 93.70331 88.39189 101.52975
## 1151 65.10216 54.27991 89.35444 76.81953 79.54693
## 1152 79.62324 88.71275 79.51430 69.07252 91.23674
## 1153 37.61926 79.51430 75.04270 85.10968 50.76591
## 1154 92.72349 90.74173 91.71909 97.31578 91.65234
## 1155 92.14181 96.38065 89.12614 73.45181 62.53634
## 1156 93.82111 91.80362 95.01535 98.73440 97.68078
## 1157 82.25437 84.21325 67.80351 97.10315 94.08517
## 1158 97.62545 98.98092 97.43797 91.10648 94.13769
## 1159 97.54853 97.48328 96.36786 100.36361 97.48328
## 1176 79.51359 79.88844 76.78643 54.23804 82.63836
## 1177 74.10659 93.66601 78.65690 87.13276 48.57916
## 1178 77.34781 89.59827 83.06365 82.38095 67.95676
## 1179 51.42515 102.22222 80.92282 70.83933 64.89455
## 1180 68.72902 88.16347 53.96413 88.15822 85.50989
## 1181 84.40052 97.04058 85.35279 95.75119 94.16257
## 1182 85.84962 84.30664 88.10025 95.50932 81.62223
## 1183 93.82111 98.95550 88.29308 85.50475 90.73737
## 1184 101.01189 96.73800 84.39734 80.81964 93.24469
## 1185 98.93923 98.27656 101.00116 101.25268 100.55374
## 1186 100.15878 98.96999 101.50892 101.05028 101.38662
## 1187 110.77857 99.86831 103.03193 100.99355 104.54117
## 1188 102.10524 114.49640 97.65152 104.32099 116.61568
## 1189 122.62570 104.63845 116.59730 124.85189 105.21949
## 1190 112.77843 108.22281 148.10349 107.78501 98.76626
## 1191 109.05125 99.75145 107.93066 107.31383 124.49640
## 1192 116.88010 142.27293 116.01471 103.85802 107.74817
## 1193 162.59319 108.05597 117.21355 100.65657 108.38241
## 1194 163.68244 109.36204 123.11031 109.52637 105.36666
## 1195 120.41282 136.26939 482.34621 106.78980 114.13370
## 1196 104.76930 126.65652 153.94484 121.02656 209.26474
## 1197 117.03804 136.02601 143.12299 143.12299 127.71320
## 1198 642.56618 173.05755 369.82714 115.81266 117.63545
## 1199 110.00582 157.06490 143.37168 120.74235 130.87151
## 1200 161.15159 107.80223 264.08547 125.56559 159.70753
## 1201 74.10659 79.51430 66.62354 37.81895 85.46386
## 1202 41.62287 85.15462 41.91126 84.57300 86.59730
## 1203 87.17744 78.65690 84.43896 85.46386 76.78643
## 1204 91.35638 82.88179 51.42515 62.53634 63.14192
## 1226 80.42268 75.04270 81.93143 87.20973 84.16547
## 1227 58.63290 92.38744 80.89354 90.87033 84.99905
## 1228 82.24845 41.68769 77.73452 76.78643 81.69164
## 1229 53.67943 90.11586 87.73624 92.16046 61.05559
## 1251 95.58136 85.21347 89.76592 84.43896 80.32086
## 1252 41.56536 85.10968 87.06867 83.04769 76.38382
## 1253 62.27695 36.84186 85.57589 56.17321 82.18981
## 1254 82.88179 91.50520 85.95374 90.74173 75.69345
## 1255 70.85132 89.70028 77.44490 95.53165 91.50520
## 1256 76.36179 91.80362 68.83437 85.25325 95.29390
## 1257 81.14328 93.19046 74.55635 88.66208 94.24815
## 1258 85.84962 92.71848 85.03254 95.52910 85.50475
## 1259 100.48347 96.31460 97.73416 99.37149 91.38998
## 1260 101.40897 100.14407 101.27397 100.11052 100.69733
## 1261 102.41009 97.34595 87.53341 100.24537 100.54435
## 1262 109.08737 101.81424 101.41742 112.94194 104.24491
## 1263 106.10577 104.36852 103.69813 105.63353 105.03229
## 1264 115.49029 108.52239 108.05007 102.84426 123.20209
## 1265 108.16236 113.14683 144.62638 109.78778 107.03475
## 1266 105.15247 118.11594 124.48441 127.12370 114.60203
## 1267 111.71435 100.58081 121.52324 138.15359 125.63590
## 1268 107.74817 108.64252 126.56843 162.89176 111.88497
## 1269 121.15220 119.89695 132.44916 112.36902 117.45057
## 1270 112.15884 109.89934 103.24642 117.94491 110.72814
## 1271 113.64010 119.31776 131.76436 118.46197 126.65652
## 1272 127.71320 113.84924 117.72698 121.15786 123.57199
## 1273 122.30426 123.20902 150.27818 127.06840 205.16297
## 1274 131.79459 446.58161 123.73880 123.77131 121.40603
## 1275 264.08547 1158.86006 192.28893 129.92929 104.05021
## 1276 87.39805 89.76592 68.22153 86.30683 39.70148
## 1277 76.45865 88.74066 50.76591 89.07701 67.53835
## 1278 86.59202 87.96539 90.06059 58.84880 85.10968
## 1279 80.66573 91.65234 87.73624 65.48751 59.30769
## 1280 70.42076 71.43483 86.40904 93.17554 89.12614
## 1281 92.21888 69.00003 99.01258 92.87515 94.31004
## 1282 93.88938 92.71848 94.13769 94.67286 81.56326
## 1283 97.75885 88.80377 73.59809 84.65185 89.16429
## 1284 95.99433 88.01447 96.30855 97.25576 92.29034
## 1301 88.52320 98.00046 74.82542 66.58774 63.96155
## 1302 86.53435 66.61348 85.57782 83.06365 79.88844
## 1303 72.74734 90.98065 90.40317 67.95676 104.02357
## 1304 75.74804 86.88603 87.77418 91.76680 53.58974
## 1305 51.50991 59.41362 63.88924 86.46140 91.46595
## 1306 79.36409 90.70701 93.51373 98.98092 92.72501
## 1307 85.46920 88.62501 97.04058 90.82499 93.49512
## 1308 57.27377 96.38773 91.12778 82.13950 89.18050
## 1309 98.59710 95.82734 101.11111 95.98072 86.43389
## 1310 101.81193 101.50892 100.37677 99.75840 100.32598
## 1311 101.96822 99.09051 100.12157 100.18869 100.63553
## 1312 101.98221 98.22391 108.96594 106.43074 124.71287
## 1313 117.06788 114.83120 104.13554 104.11632 110.07958
## 1314 146.43261 98.93909 104.55332 108.05007 108.25918
## 1315 105.35619 107.31383 110.40044 124.48441 129.12470
## 1316 140.96680 109.40672 112.43048 102.94933 123.45897
## 1317 100.39817 125.88797 114.70431 108.64252 106.26664
## 1318 111.90901 135.10290 104.18835 138.67541 111.90901
## 1319 127.86717 185.25808 110.38324 122.56407 228.18339
## 1320 131.30663 109.40244 132.04917 119.22506 117.45057
## 1321 105.58526 133.32345 112.72984 125.15530 110.46473
## 1322 101.07600 148.00592 100.61697 138.74334 148.08496
## 1323 115.17637 129.88913 133.39165 109.17176 126.45552
## 1324 157.06490 140.19862 138.41941 120.27834 110.00582
## 1325 1164.81025 222.04033 694.06484 172.02019 171.68524
## 1326 86.17021 84.57300 60.03765 94.37539 70.89381
## 1327 76.45865 76.81953 96.04111 58.84880 84.43896
## 1328 89.76592 52.63789 95.99851 57.40160 95.62290
## 1329 92.92963 86.45978 86.73150 78.03115 82.68579
## 1330 83.29147 92.64793 79.07915 90.71247 80.47166
## 1331 93.88938 88.62501 93.20396 101.30804 85.50475
## 1332 88.80377 81.36691 83.82353 96.48763 94.49291
## 1333 96.69225 94.80933 92.43007 70.48157 88.39372
## 1334 92.48857 99.37149 99.37149 83.11151 86.57403
## 1335 101.33226 98.93923 100.95731 99.81995 100.32598
## 1336 100.23257 100.24116 99.99093 99.61333 100.14407
## 1337 107.55417 103.33463 104.69265 102.49331 103.16092
## 1338 104.89362 97.65152 104.85359 103.40427 108.71738
## 1339 106.29270 106.16497 101.96257 104.59202 105.62299
## 1340 105.52295 105.80979 112.55568 133.86898 107.35540
## 1341 120.82444 108.59176 108.59176 126.46410 108.46440
## 1342 109.29728 123.62344 107.90621 108.54908 125.17527
## 1343 116.13950 113.64498 110.54282 104.92897 120.80224
## 1344 108.83104 118.02153 227.16294 112.36902 110.95365
## 1345 182.62523 103.24642 123.57135 117.22208 109.52637
## 1346 110.19345 118.49745 118.46938 119.17671 161.35232
## 1347 147.53804 120.26897 168.76751 114.09905 160.95923
## 1348 121.45621 469.84718 115.17637 128.97574 133.39165
## 1349 132.78361 138.84010 143.82307 125.86420 127.42447
## 1350 136.08946 150.12676 125.69222 218.14585 142.78418
## 1401 86.30683 87.39805 69.00621 90.62159 86.69055
## 1402 79.11190 94.96854 45.05271 84.31220 65.23464
## 1403 85.03723 72.67589 90.82841 41.62287 65.23464
## 1404 82.88179 61.37006 93.33426 91.46595 53.45668
## 1405 91.27376 89.16226 97.31578 82.80109 92.64793
## 1406 93.09747 96.84995 88.29308 94.04443 93.32995
## 1407 95.06454 92.21888 93.49512 72.27934 94.63581
## 1408 74.04314 92.71848 96.59448 98.35328 97.68078
## 1409 94.96702 96.28845 94.96702 99.11068 91.38998
## 1410 99.73802 102.33326 100.44107 98.54870 99.50709
## 1411 100.62028 94.57160 102.64242 102.41009 99.99093
## 1412 107.49766 101.30591 105.61495 106.70248 103.27372
## 1413 127.01718 128.88305 114.83120 104.23632 101.63512
## 1414 105.74991 138.18694 140.53637 106.47748 296.34216
## 1415 102.75253 116.68623 102.84663 103.39979 142.50793
## 1416 148.10349 106.80057 104.98236 105.34078 106.44959
## 1417 113.61904 115.48170 166.15643 105.41559 121.33839
## 1418 121.33839 106.47021 108.05597 106.86067 113.18423
## 1419 115.65967 101.06061 123.57135 105.36249 459.90827
## 1420 149.13311 109.88743 128.06191 117.27757 169.43669
## 1421 116.11264 124.28462 110.76649 119.21853 111.87852
## 1422 114.09905 112.31792 120.01017 551.74526 120.40953
## 1423 133.72371 124.46827 115.44542 152.14427 122.01325
## 1424 153.54877 166.21369 125.86420 136.57597 134.46696
## 1425 135.65222 120.14175 131.58517 123.11356 142.73615
## 1426 87.79150 87.53040 79.54693 87.80122 63.88756
## 1427 89.49587 36.79145 85.15462 69.00621 82.37310
## 1428 89.67846 78.65690 55.46753 80.32086 57.40160
## 1429 85.41806 97.01757 70.34297 94.96020 89.34008
## 1430 91.27376 85.95374 79.39866 91.27376 97.33569
## 1431 78.89058 94.36870 79.68046 67.14593 94.01651
## 1432 76.24359 94.21084 95.70887 74.58034 96.48148
## 1433 94.08517 94.95988 95.12441 96.69225 97.75885
## 1434 81.86683 94.97735 93.24388 97.65652 91.29703
## 1435 98.02852 99.93156 99.42365 100.54435 100.35603
## 1451 79.51359 70.92046 85.54361 88.93484 87.90980
## 1452 91.95899 80.88184 84.57300 83.11619 87.90980
## 1453 52.63789 91.22254 85.46386 63.96155 81.93143
## 1454 82.88179 95.53165 93.63502 76.00061 75.65387
## 1455 96.38065 97.31578 54.27027 67.96218 91.78669
## 1456 74.04314 95.50927 97.61660 87.31443 84.30664
## 1457 88.75352 69.46001 93.63286 95.11866 81.36691
## 1458 92.30889 70.48718 98.92117 89.32484 88.21839
## 1459 99.50617 95.82734 98.04965 91.29703 96.80581
## 1460 102.41009 96.37148 99.73802 97.59288 100.61728
## 1461 99.09051 101.33226 99.27417 98.86450 98.02852
## 1462 101.98221 103.27372 98.94547 104.51553 104.54117
## 1463 261.53239 103.69813 104.36852 107.80484 97.65152
## 1464 110.08035 251.21835 106.44943 146.43261 105.64624
## 1465 107.93066 144.62638 117.87121 103.95877 143.88818
## 1466 100.70149 116.60229 109.85664 107.70150 104.27782
## 1467 129.20194 98.68517 108.54908 108.54908 121.48413
## 1468 116.48510 109.25246 108.00357 117.82492 108.38241
## 1469 142.76316 163.68244 116.94994 131.40097 200.09478
## 1470 117.94491 123.60592 228.18339 102.49127 108.13923
## 1471 115.16107 118.49745 139.28538 107.86507 119.04881
## 1472 126.42660 118.92416 199.02722 118.87613 134.12285
## 1473 100.97778 115.76390 171.64470 124.85976 115.81266
## 1474 140.19862 162.17781 155.95655 210.95186 247.11923
## 1475 113.97262 123.52662 129.84296 105.01465 142.88328
## 1476 84.18221 80.05830 84.05367 78.65690 84.57300
## 1477 88.93484 83.06365 91.22254 72.74734 68.38764
## 1478 48.49429 80.42268 89.67846 80.21945 91.22254
## 1479 62.53634 71.60730 90.41604 86.88603 102.22222
## 1480 75.51253 96.38065 61.37006 86.88603 85.47067
## 1481 97.61660 57.27377 96.90043 90.37662 95.09096
## 1482 86.59472 85.10674 89.40975 92.71848 73.98322
## 1483 93.44149 92.21888 86.12273 97.95033 96.38773
## 1484 100.76289 99.83715 97.89099 99.50617 97.91254
## 1485 100.31855 99.63279 99.11064 101.30359 93.82459
## 1486 100.92643 97.34595 100.92643 87.53341 102.64242
## 1487 106.37830 103.21992 110.28205 103.33463 107.55417
## 1488 115.39095 107.83794 132.29157 105.45455 101.90236
## 1489 130.50308 106.44943 146.30854 107.23509 102.73968
## 1490 111.10829 148.87401 141.68112 116.28912 115.40892
## 1491 115.26170 142.50793 116.62201 107.78501 110.06902
## 1492 116.13950 113.61904 104.99610 172.70436 125.96739
## 1493 127.07225 114.85910 151.70141 107.12818 108.32327
## 1494 109.52637 109.21517 101.10717 109.74483 163.49441
## 1495 131.13302 146.05516 106.49688 118.41038 129.47695
## 1496 119.25739 504.61556 161.35232 101.43678 109.61199
## 1497 120.40953 115.23936 112.31792 147.53804 117.72698
## 1498 119.52787 131.27959 472.19814 135.07640 162.66276
## 1499 157.06490 132.01165 122.18326 115.24018 143.27033
## 1500 558.52660 172.26418 132.72405 549.88519 187.06083
## 1551 81.69164 82.18981 85.54361 87.68738 97.69924
## 1552 90.63433 54.29599 86.53435 39.70148 104.95791
## 1553 68.22153 86.72466 56.17321 91.23674 94.43840
## 1554 89.70028 87.74308 69.59554 91.27376 96.04200
## 1555 86.65280 80.66573 92.56843 70.11448 102.81987
## 1556 94.49291 87.78115 90.84525 95.50927 65.73624
## 1557 90.82499 88.29308 93.48537 70.60962 79.46693
## 1558 101.77485 95.90900 95.36379 70.48718 97.36059
## 1559 98.73339 95.08520 94.88037 99.79936 101.01189
## 1560 99.10475 101.86310 98.96999 99.87192 100.41002
## 1561 101.90641 100.06442 100.21679 97.59288 99.50709
## 1562 100.36611 144.11512 106.70248 104.70148 102.23617
## 1563 107.75036 110.93115 107.63745 104.06422 110.07958
## 1564 101.44664 114.77675 116.12136 106.23389 122.62570
## 1565 106.15231 107.88385 106.52577 111.55464 100.70149
## 1566 105.18351 122.51773 114.60203 107.03475 114.48351
## 1567 160.60608 123.43232 113.18423 131.96356 105.33872
## 1568 135.20384 165.59228 163.02863 104.66835 125.38679
## 1569 130.64683 109.22285 112.15884 228.18339 149.13311
## 1570 129.81371 120.17103 123.13061 141.09160 105.37942
## 1571 106.62682 209.70686 101.43678 112.05781 111.36527
## 1572 132.30845 116.78040 117.18814 149.56982 113.53051
## 1573 135.07640 113.79071 131.12663 107.00071 259.83518
## 1574 120.27834 143.37168 132.44122 132.34528 110.00582
## 1575 131.13682 190.30216 139.11052 161.63692 133.98900
## 1626 58.63290 81.40956 48.49429 60.45340 89.76592
## 1627 83.11619 69.00621 54.07719 74.82542 76.45865
## 1628 91.29758 90.98065 86.53435 80.05830 88.93484
## 1629 75.69345 86.46140 84.59334 87.95048 92.43411
## 1630 82.80109 103.24544 77.44490 89.67338 85.41806
## 1631 97.54756 95.03849 95.82647 81.19623 92.27130
## 1632 95.01535 68.83437 93.55041 88.66208 88.62501
## 1633 97.40585 101.81818 101.77485 73.12163 94.35900
## 1634 100.57932 86.69231 89.63739 89.81541 97.89099
## 1635 99.87192 104.30909 102.32023 98.00079 99.65537
## 1636 101.86862 99.87192 101.27936 100.12157 101.78553
## 1637 101.82865 111.49880 101.81424 116.21552 144.11512
## 1638 114.10497 111.19878 103.69813 104.89362 128.88305
## 1639 115.52511 112.37245 110.24742 115.59832 130.50308
## 1640 108.22281 113.36324 119.79731 107.65160 105.33277
## 1641 103.64503 98.91687 108.80848 99.95012 117.88453
## 1642 112.63187 110.65562 98.34403 226.14411 141.54676
## 1643 115.08633 107.61321 107.61321 106.28720 105.56697
## 1644 114.59080 132.04917 105.60676 116.29395 105.89684
## 1645 201.13498 104.77194 131.30663 254.13701 107.93025
## 1646 160.52852 111.00177 110.44112 139.84188 126.34791
## 1647 120.26897 147.53804 107.26161 117.03804 148.02632
## 1648 131.03259 126.33907 131.25519 164.06744 144.79541
## 1649 107.59025 179.23003 102.90404 139.77087 136.72429
## 1650 125.69222 172.09421 176.53174 128.53692 139.11052
## 1651 63.88756 86.53435 88.52178 87.39805 76.54764
## 1652 83.26203 69.07252 84.31220 86.69055 87.80122
## 1653 97.73589 87.06867 76.78643 82.76681 80.76570
## 1654 90.33507 91.76680 90.74173 53.67943 68.71703
## 1676 85.46386 72.74734 85.10968 82.47773 52.63789
## 1677 86.59730 84.99905 54.07719 80.21945 79.88844
## 1678 85.46386 84.35955 80.07062 88.03280 79.54693
## 1679 90.48983 87.77418 70.85132 86.45978 92.13118
## 1701 89.29480 63.96155 63.96155 85.21347 55.25180
## 1702 88.52178 89.92114 85.35007 59.26252 80.21945
## 1703 85.58530 86.59202 86.59202 86.53435 82.47773
## 1704 75.69345 96.04200 76.00061 75.51253 96.01923
## 1705 91.02956 90.11586 102.63692 91.87575 91.46595
## 1706 85.09039 95.70887 88.21839 85.13210 65.34469
## 1707 96.47162 98.98092 90.42765 93.60593 86.60671
## 1708 97.62545 93.56191 85.46920 93.88938 74.04314
## 1709 100.48347 100.76289 99.43150 81.86683 95.33453
## 1710 101.38605 112.35484 100.93304 98.71030 99.73802
## 1711 94.27515 98.71030 101.71123 100.63553 99.00218
## 1712 104.18450 124.71287 101.34497 101.88398 103.63267
## 1713 105.86541 110.38870 106.69830 100.90090 99.22778
## 1714 106.48477 107.91788 97.50000 106.44943 115.07238
## 1715 106.02534 108.14570 108.22281 112.95271 132.51117
## 1716 338.64140 107.00910 106.27126 112.95271 109.96946
## 1717 101.12053 125.93080 110.76400 128.89845 171.79073
## 1718 119.06632 111.90901 207.17797 135.20384 113.04051
## 1719 119.89695 112.60621 105.37942 125.82905 110.11771
## 1720 108.93227 115.99308 104.77194 110.33369 134.23700
## 1721 144.82208 111.41676 106.35650 124.28462 153.95683
## 1722 300.11344 116.98047 119.47703 106.92900 136.02601
## 1723 105.44614 371.76418 126.84464 117.63545 109.17176
## 1724 155.62040 155.56938 125.83247 143.62978 144.24659
## 1725 552.45355 204.07685 161.41631 142.78418 129.60765
## 1776 63.88756 63.12598 87.24826 89.29480 83.00664
## 1777 81.96664 97.73589 92.38744 82.38095 63.88756
## 1778 79.72385 85.57782 81.40956 89.35444 66.61348
## 1779 75.74804 88.66636 91.73298 92.92963 84.62964
## 1780 72.67259 63.14192 92.92963 76.96681 91.62935
## 1781 95.14822 83.40701 72.84820 93.49512 97.79130
## 1782 99.01804 94.31041 99.01804 82.25437 91.62297
## 1783 81.65959 81.36691 79.43407 94.18004 73.12163
## 1784 80.81964 94.67902 98.79779 94.08903 93.53041
## 1785 99.04384 97.22840 98.00079 101.82777 100.35603
## 1786 99.04384 100.24116 104.82981 99.71523 101.14326
## 1787 106.82686 102.97982 103.33463 103.21992 104.24491
## 1788 104.67952 108.71738 110.16345 101.95037 108.22561
## 1789 296.34216 105.00669 118.86091 116.83474 108.37045
## 1790 100.70149 108.36584 119.66948 120.82444 155.66885
## 1791 106.69451 107.46319 120.82444 104.27782 380.88026
## 1792 445.05135 111.42145 114.70431 107.90621 103.32460
## 1793 106.20642 106.86067 171.79073 112.92240 135.20384
## 1794 109.88743 183.98454 113.42653 131.58368 123.57135
## 1795 110.72814 105.89684 99.49455 116.94994 105.68000
## 1796 173.21939 503.42939 116.81906 119.31776 132.60389
## 1797 108.27679 125.83242 121.26949 193.74717 100.20202
## 1798 131.25519 152.14427 104.67249 121.74531 145.29628
## 1799 127.89719 131.79459 143.62978 158.06623 157.06490
## 1800 145.45282 552.45355 134.47319 172.26418 129.84296
## 1801 70.22650 87.53040 77.81180 36.79145 84.43896
## 1802 103.54970 52.64988 86.69055 85.54361 77.26177
## 1803 58.63290 92.38744 90.40317 39.70148 58.63290
## 1804 72.67259 54.27027 89.91046 95.78239 87.84210
## 1805 89.79684 86.88603 91.73298 87.77418 92.35943
## 1806 65.86113 83.30883 89.08903 88.21839 96.35391
## 1807 95.11866 91.12778 88.75876 81.34965 83.40701
## 1826 37.61926 67.95676 41.62287 90.63433 86.24029
## 1827 85.46386 89.29480 67.53835 86.69055 97.99331
## 1828 63.88756 91.22254 90.63433 86.24029 36.84186
## 1829 94.78517 91.76680 88.16347 92.72349 91.02956
## 1830 89.12614 92.69186 79.31961 78.11804 90.20065
## 1831 101.81818 82.82758 90.73737 77.10697 88.96866
## 1832 93.82111 92.87873 95.70887 87.94957 65.73624
## 1851 79.72385 87.22372 83.00664 80.80704 82.76681
## 1852 85.84290 89.46176 76.96728 82.14150 82.14150
## 1853 79.88844 55.25180 65.10216 89.29480 104.95791
## 1854 95.73724 93.33426 92.03260 75.74804 93.80144
## 1855 84.59334 88.16347 79.07915 87.84210 92.40642
## 1856 101.08586 88.62501 94.08517 94.20589 76.36179
## 1857 98.92117 91.62297 83.40701 87.88031 98.30169
## 1858 91.54061 97.10315 96.48763 91.14090 94.80933
## 1859 96.24320 91.37769 90.73224 90.73224 91.38998
## 1860 99.75840 98.27656 101.14849 100.50736 99.98555
## 1861 99.71523 101.00116 101.14326 100.37677 100.14188
## 1862 100.21886 103.23319 104.54117 100.99107 99.97401
## 1863 111.54449 114.83120 115.19239 103.88052 98.87984
## 1864 106.62398 99.28725 105.64624 118.86091 108.25918
## 1865 107.45388 105.34785 110.40044 99.68729 114.76495
## 1866 105.31331 339.67420 108.46440 112.30907 120.76648
## 1867 116.47105 103.20968 111.90901 116.75936 100.65657
## 1868 162.89176 107.12818 114.81305 113.64498 101.12053
## 1869 262.87131 155.17818 117.27757 109.89934 136.26939
## 1870 117.92879 113.82010 113.34587 118.24087 136.26939
## 1871 106.40085 161.35232 139.84188 115.73388 139.10761
## 1872 143.70353 550.46210 160.95923 149.28719 118.92416
## 1873 322.99700 128.97574 147.99678 234.31872 122.01325
## 1874 121.95838 103.33840 120.97504 118.87561 744.15548
## 1875 561.63418 149.47301 125.68050 160.54959 135.65222
## 1876 86.69055 54.23804 41.62287 87.13276 85.15462
## 1901 84.05367 56.17321 93.66601 85.15462 67.95676
## 1926 78.65690 90.82841 41.91126 84.31220 86.69055
## 1927 89.29480 55.46753 68.38764 86.50740 45.05271
## 1928 88.93484 82.18296 98.00046 56.17321 87.90980
## 1929 75.91254 85.41806 91.44621 88.66636 91.62935
## 1930 67.96218 85.84441 90.71247 95.53165 86.61646
## 1931 95.68366 94.41715 100.72745 91.10648 69.00003
## 1932 97.97282 93.48537 93.09747 90.42765 97.40585
## 1933 90.45155 93.45691 88.75326 95.06454 89.40975
## 1934 86.57403 98.21780 96.31460 95.08520 90.73224
## 1935 100.60372 100.79759 99.65537 102.33326 100.42671
## 1936 100.35603 95.14710 101.77396 101.41693 100.12346
## 1937 101.03491 164.68155 103.33463 102.74182 99.86831
## 1938 105.23602 110.38870 110.10368 102.11598 106.69830
## 1939 118.86091 106.29270 105.73638 104.59202 103.29765
## 1940 108.59176 106.27126 116.11344 107.73630 114.52303
## 1941 109.78778 103.35406 105.35619 133.91909 107.45388
## 1942 113.61904 104.92897 101.12053 107.74817 108.26279
## 1943 120.80224 98.68517 116.48510 183.38993 106.18512
## 1944 112.15884 108.93227 99.15473 114.59080 113.42653
## 1945 117.22208 134.40526 169.43669 155.17818 131.58368
## 1946 106.03173 136.83834 123.79254 117.31404 115.73388
## 1947 111.12892 120.70979 127.98187 117.72698 121.26949
## 1948 162.66276 234.31872 121.62320 120.26701 146.12892
## 1949 136.96747 136.73844 132.97959 134.46696 298.66443
## 1950 124.18577 549.88519 172.26418 132.24449 117.42197
## 2001 87.24826 91.22254 87.96539 94.37539 92.38744
## 2002 91.32519 50.76591 80.89354 88.98440 88.98440
## 2003 68.86478 104.44557 81.88533 48.57916 80.05830
## 2004 90.20065 78.11804 63.37863 59.41362 84.59334
## 2005 93.63502 81.19130 71.43483 68.22087 92.56843
## 2006 97.58905 72.27934 88.75326 69.57160 87.76968
## 2007 79.46693 96.81781 99.92734 95.90900 82.13950
## 2008 85.28735 95.86473 95.29390 94.18004 81.36691
## 2009 98.04965 93.24388 96.24320 99.79936 93.70331
## 2010 100.16114 101.19430 100.42671 100.90765 100.44107
## 2011 101.72575 100.42671 100.82659 101.14849 99.04384
## 2012 103.72201 101.33319 124.71287 106.37830 112.94194
## 2013 104.25807 176.17889 105.03229 115.39095 110.38870
## 2014 104.63845 146.30854 106.23389 115.07092 104.82608
## 2015 108.02726 132.21463 111.65145 108.02726 119.82356
## 2016 106.94296 117.41760 104.13499 107.93066 148.87401
## 2017 156.88157 101.13796 106.18512 107.59355 114.42351
## 2018 116.75936 101.86646 166.01565 109.42068 173.80710
## 2019 106.27945 138.80017 163.68244 178.26917 116.75754
## 2020 117.88098 110.11968 108.26185 183.03745 113.82010
## 2021 118.49745 111.41676 102.11599 106.07573 281.71217
## 2022 120.26897 119.47703 149.56982 160.95923 320.44206
## 2023 173.03357 100.74375 472.19814 145.13791 205.34826
## 2024 144.42358 127.89719 155.62316 156.47473 127.89719
## 2025 160.34981 123.52662 141.93627 129.92929 121.51767
## 2076 87.49543 82.18981 63.96155 58.63290 86.59202
## 2077 82.63836 78.65690 91.32519 92.01060 67.95676
## 2078 79.88844 39.70148 57.40160 88.52178 79.47972
## 2079 53.32295 91.72003 89.70028 61.05559 75.74804
## 2080 92.75428 95.78239 69.59554 102.81987 70.11448
## 2081 72.84820 100.69083 81.95826 84.30664 95.82647
## 2082 89.08903 84.34966 99.01804 77.71553 90.42765
## 2083 96.43636 93.19046 84.11676 85.84962 94.24815
## 2084 96.70003 100.36361 86.84800 96.21200 94.67902
## 2085 106.21088 102.72571 99.04384 100.14407 99.10475
## 2086 87.28822 101.53984 100.42671 100.24917 97.96079
## 2087 108.96594 106.37830 104.02852 103.53782 103.16092
## 2088 101.78633 104.06422 116.41026 111.56361 108.22561
## 2089 108.25918 116.98133 104.88907 105.00669 105.32322
## 2090 107.32870 112.95271 119.29708 128.58474 150.95828
## 2091 109.85664 105.31331 100.70149 107.32870 108.80848
## 2092 207.17797 120.23951 107.03457 107.59355 103.62725
## 2093 116.48510 108.18084 109.70550 118.71088 114.70431
## 2094 201.13498 175.39744 108.26185 136.68277 129.47695
## 2095 107.97407 110.35033 262.87131 105.94276 115.65967
## 2096 281.71217 121.02656 119.21853 140.58533 113.64010
## 2097 193.57454 123.85819 143.70353 136.02601 106.35207
## 2098 126.84464 113.79071 133.39165 144.79541 173.05755
## 2099 157.06490 132.59326 132.34528 446.58161 132.78361
## 2100 139.11052 104.60157 129.60765 146.68127 150.12676
## 2151 86.59730 81.69164 80.89354 91.22254 82.47773
## 2152 79.51359 97.73589 41.91126 92.38744 97.69924
## 2153 57.40160 84.98210 81.13119 79.51359 90.63433
## 2154 54.27027 82.88179 91.02956 91.87575 54.27027
## 2155 63.37863 102.81987 94.70750 92.43986 87.74308
## 2156 93.44638 90.58389 92.22074 97.68078 84.11676
## 2157 92.38893 94.16257 94.49291 92.71848 92.87873
## 2158 88.62501 95.86473 69.82193 94.31004 95.75119
## 2159 93.63993 97.25576 97.91254 97.18961 98.04965
## 2160 96.69969 101.86862 104.30909 98.83097 100.65897
## 2161 100.60372 100.16114 105.05805 97.32866 100.82826
## 2162 105.90647 104.10156 102.59787 104.98080 114.94785
## 2163 113.69048 106.69830 108.33239 104.06422 105.96625
## 2164 104.74776 106.44586 113.99777 116.59730 101.99868
## 2165 110.01897 119.66948 111.06928 106.02534 104.27782
## 2166 109.72657 112.72046 105.52295 106.01707 117.41760
## 2167 105.04076 125.88797 111.17364 162.89176 173.66124
## 2168 114.84006 108.26279 109.81380 111.88497 116.75936
## 2169 118.15403 111.88715 111.40073 110.35033 105.36666
## 2170 122.56407 117.22208 118.15403 112.60621 133.08417
## 2171 153.95683 133.32345 504.61556 105.91190 131.76436
## 2172 114.09905 147.34885 112.69286 298.75559 182.04600
## 2173 152.11439 131.01205 122.01325 100.74375 123.15992
## 2174 111.04844 102.60741 155.62316 161.07717 447.30782
## 2175 132.72405 131.13682 160.38801 160.38801 165.70582
## 2226 88.98440 87.96539 89.35444 80.05830 88.97681
## 2227 81.13119 91.32519 86.83467 80.80704 63.96155
## 2228 87.51037 86.83467 91.29758 82.18296 66.61348
## 2229 91.46595 81.19130 53.96413 86.77549 89.12743
## 2230 78.95833 91.33736 93.33426 63.88924 91.78669
## 2231 89.33854 95.11866 87.88031 93.88938 94.95988
## 2232 88.80377 97.54756 84.34966 95.75119 97.68078
## 2233 88.02860 88.66208 69.82193 91.62297 92.30889
## 2234 95.98072 99.50617 98.21780 100.81566 99.79936
## 2235 97.46060 97.46060 100.79741 99.89300 99.36712
## 2236 97.34595 100.50041 97.15385 98.98914 97.34595
## 2237 100.21886 110.47760 106.43074 102.74182 105.56488
## 2238 114.83120 106.69773 137.64899 110.16345 108.71738
## 2239 110.40473 120.74558 109.15415 104.55332 107.23509
## 2240 105.81129 114.52303 112.47647 105.72252 107.07546
## 2241 133.91909 123.45897 103.39979 105.12161 126.87452
## 2242 121.48413 105.46017 165.32051 117.82492 113.64498
## 2243 106.83120 118.71088 184.33610 111.56177 135.20384
## 2244 99.15473 112.23080 110.33821 113.42653 110.36058
## 2245 119.67466 123.57135 108.13923 163.49441 182.46342
## 2246 131.98531 110.44112 139.28538 131.98531 124.28462
## 2247 181.84691 134.16978 148.00592 135.68230 111.59231
## 2248 116.51684 371.76418 205.34826 323.38504 128.50156
## 2249 143.62978 194.98985 125.82266 122.70613 102.60741
## 2250 127.11430 148.70509 144.49301 145.45282 149.47301
## 2251 83.45336 54.23804 88.03280 49.97188 86.53435
## 2252 86.59202 85.95653 64.04461 45.30148 82.75869
## 2253 87.19251 86.72466 76.96728 72.67589 82.14150
## 2254 92.35943 93.33426 85.01096 86.00700 86.40904
## 2255 86.77549 92.75428 91.51116 97.33569 94.96020
## 2256 76.24359 96.59448 81.65959 97.99241 91.04108
## 2276 86.50740 87.20973 82.75869 91.22254 54.27991
## 2277 36.84186 37.61926 87.26977 80.21945 90.63433
## 2278 39.70148 85.57782 82.37310 50.76591 55.25180
## 2279 63.14192 59.30769 90.64730 86.40904 85.84441
## 2280 76.93336 78.52077 92.03260 81.00761 76.00061
## 2281 85.13210 93.45691 74.55635 82.13950 100.69083
## 2301 60.45340 95.58136 58.84880 88.03280 97.69301
## 2302 55.46753 88.52178 87.20973 82.38095 93.96598
## 2303 83.26203 41.91126 89.49587 39.64751 86.59202
## 2304 86.45978 92.03260 89.34008 91.87575 87.95048
## 2305 86.77549 82.80109 76.00061 70.11448 53.67943
## 2306 84.42922 94.74379 95.70887 101.36364 95.06403
## 2307 90.18384 95.70887 89.81119 88.90840 88.29308
## 2308 96.48148 93.51373 81.62803 79.08784 65.73624
## 2309 97.51408 98.03552 94.96702 99.50617 66.97690
## 2310 102.17427 99.11660 100.14188 101.27936 97.35783
## 2311 100.12346 100.31855 100.95731 101.52984 97.63176
## 2312 101.78127 103.03193 104.51553 104.02718 112.73950
## 2313 100.72350 105.45455 137.77702 117.06788 110.69491
## 2314 109.87936 141.25711 104.90277 110.30481 105.77540
## 2315 109.40672 107.66647 100.48315 117.47672 152.66391
## 2316 122.51773 116.62201 99.75145 113.73976 117.47672
## 2317 173.66124 114.81305 142.27293 123.62344 110.21956
## 2318 114.42351 108.32327 128.31784 114.64604 104.92897
## 2319 119.67466 108.04980 118.04719 118.02153 263.32021
## 2320 106.49688 109.78499 131.46856 105.37942 114.59080
## 2321 141.39107 101.43678 131.98531 105.91190 118.46938
## 2322 281.25637 298.75559 125.55251 121.11605 193.74717
## 2323 147.97806 182.98188 126.49420 113.79071 259.83518
## 2324 111.39208 215.03420 210.95186 140.11829 132.40925
## 2325 218.14585 1158.86006 128.48153 142.88328 136.81357
## 2376 57.40160 89.46176 86.84609 84.16547 93.96598
## 2377 63.96155 94.37539 82.18296 83.45336 55.25180
## 2378 76.52190 85.84290 104.95791 77.84784 79.11190
## 2379 89.91046 86.46140 53.96413 89.16226 82.80109
## 2380 89.23611 86.00700 71.43483 92.03260 89.79684
## 2381 92.22074 91.29655 77.71553 84.11676 101.36364
## 2382 96.47162 97.62545 74.55635 88.10025 97.18258
## 2383 99.84694 94.63581 94.67286 89.08903 97.97282
## 2384 97.65004 97.45069 97.73416 97.55989 95.98072
## 2385 99.93156 100.54435 97.43512 96.69969 99.00218
## 2386 102.72571 100.15878 98.87331 98.78481 101.72575
## 2387 102.74791 106.57789 102.27588 103.89341 109.08737
## 2388 104.09200 102.10524 109.81291 104.13530 107.76005
## 2389 106.47748 116.83474 104.51977 107.37269 109.30307
## 2390 132.29487 106.15231 111.04849 99.95012 339.67420
## 2391 155.66885 110.88988 105.18351 106.01707 110.42647
## 2392 105.36204 104.66835 166.15643 118.77751 105.57382
## 2393 105.56697 123.43232 114.81305 106.70624 110.21956
## 2394 109.29769 106.27945 117.94491 128.46265 116.41392
## 2395 120.17103 114.13370 107.93025 121.15220 136.26939
## 2396 133.32345 115.73388 113.64695 99.45656 188.36214
## 2397 199.02722 112.95776 320.44206 125.97572 117.03804
## 2398 115.92970 128.50156 148.98291 124.85976 126.33907
## 2399 126.91669 161.42447 183.04556 132.40925 214.85200
## 2400 144.04091 130.08440 105.01465 112.80796 149.70657
## 2401 87.20973 67.95676 97.43460 79.62324 83.00664
## 2402 74.82542 80.05830 87.02385 45.05271 86.83467
## 2403 84.31220 90.87033 85.57782 54.23804 77.84784
## 2404 93.31453 65.48751 79.07915 59.30769 93.63502
## 2405 59.30769 63.37863 84.59334 79.07915 90.71247
## 2406 90.19618 92.98638 69.57160 97.36059 98.45387
## 2407 95.01535 89.32484 81.02273 97.61660 96.99243
## 2408 91.29655 94.13769 92.67817 91.04108 77.10697
## 2409 96.59170 86.84800 98.03552 96.03599 93.37227
## 2410 101.72575 102.77447 102.29349 100.15878 100.26036
## 2426 104.95791 68.38764 89.35444 76.45865 84.15973
## 2427 87.90980 77.28096 85.03723 77.84784 87.33405
## 2428 79.51359 83.04769 55.25180 68.22153 67.53835
## 2429 73.29890 75.69345 53.32295 79.31961 97.63935
## 2430 64.83246 67.96218 97.01757 97.63935 77.44490
## 2431 93.55041 84.30664 69.00003 92.67817 77.22249
## 2432 95.08687 89.40975 92.80954 94.16257 95.31668
## 2433 82.74496 94.16257 97.79130 94.63581 90.90446
## 2434 91.29703 97.54853 81.86683 91.29703 100.48347
## 2435 98.49700 100.14188 98.47596 102.44425 98.86450
## 2436 97.32866 103.30935 87.53341 96.83548 101.27936
## 2437 109.08737 103.72201 102.27588 103.27481 100.36611
## 2438 104.01260 104.04441 109.57484 110.07958 104.24219
## 2439 103.35298 102.73968 104.59839 115.49029 105.11212
## 2440 338.64140 102.67333 142.75597 110.34835 103.79710
## 2441 105.72252 105.45406 106.10489 107.93066 106.00929
## 2442 158.47743 107.05697 143.06488 120.96710 107.12818
## 2443 103.32460 107.13503 138.67541 116.22330 107.67523
## 2444 105.04348 120.41282 141.09160 102.39567 115.95317
## 2445 117.88098 116.04334 105.68000 169.43669 105.68000
## 2446 139.28538 121.33702 119.17671 111.23670 145.10697
## 2447 136.02601 133.41070 116.98047 280.86383 101.07600
## 2448 135.07640 140.40671 472.19814 117.70833 116.63109
## 2449 448.92817 122.70613 136.73844 132.44122 102.90404
## 2450 204.07685 203.22596 161.15159 186.58759 549.88519
## 2451 85.10968 84.57300 87.68738 52.64988 57.46010
## 2452 60.65947 76.78643 89.86462 86.59730 88.97681
## 2453 87.49543 88.97681 82.18296 97.69301 97.43460
## 2454 87.74308 96.04200 79.07915 80.92282 90.41604
## 2455 86.40904 78.95833 69.59554 86.73150 87.77418
## 2456 99.33319 98.73440 90.71457 72.44891 95.09096
## 2457 89.33854 93.48537 88.27120 94.18004 82.36210
## 2458 96.47162 96.99243 73.31933 87.88031 99.01804
## 2459 97.32737 93.63993 83.11151 98.03552 66.97690
## 2460 99.00218 101.33226 99.09979 100.54435 100.37677
## 2461 99.89300 99.11660 102.77447 97.34595 100.79741
## 2462 101.19407 101.32439 100.21886 120.11251 104.70148
## 2463 98.87984 109.57484 103.46585 104.20592 107.76005
## 2464 102.48273 137.90473 110.83466 107.97734 110.40473
## 2465 110.88988 107.19246 110.52339 116.11344 120.61749
## 2466 109.78778 106.52577 110.06902 154.11132 110.06902
## 2467 120.27348 112.14252 107.14378 110.16206 120.53017
## 2468 165.32051 111.90901 105.04076 114.83924 124.92916
## 2469 111.79653 129.47695 101.10717 99.61235 110.33369
## 2470 119.77823 108.60014 105.36666 111.97003 166.79235
## 2471 113.25841 101.02694 118.49745 123.54022 123.79254
## 2472 160.98321 106.92900 113.53051 112.23078 112.31792
## 2473 134.68528 131.27959 115.17637 229.18128 128.97574
## 2474 126.82551 131.58996 123.00804 127.37305 126.91669
## 2475 265.24127 190.33094 107.75534 158.67835 320.94026
## 2526 80.80704 85.84290 90.82841 88.52178 41.56536
## 2527 41.68769 66.56782 85.46386 67.53835 60.67146
## 2528 76.43678 87.17744 84.15973 96.04111 45.05271
## 2529 63.37863 75.51253 62.53634 81.00761 102.81987
## 2530 61.05559 91.27376 86.65280 79.07915 91.87575
## 2531 91.29655 76.31949 88.27120 98.64865 80.53962
## 2532 94.31041 93.60593 85.50475 84.30664 81.02273
## 2533 92.71848 88.96609 57.28155 97.04058 98.10208
## 2534 94.88037 96.73800 87.92436 101.11111 91.23322
## 2535 97.93814 101.14849 100.99320 99.12850 97.64966
## 2536 99.16667 101.42751 99.87306 100.64034 98.53717
## 2537 101.82865 101.32439 104.64498 104.24491 102.35425
## 2538 113.69048 104.85359 101.95037 107.41162 104.11632
## 2539 106.62398 106.16497 97.88709 115.07238 108.52239
## 2540 129.12470 105.45406 102.94933 112.30907 102.37739
## 2541 100.48315 107.88385 110.52339 129.79219 129.13669
## 2542 144.16979 108.93434 162.59319 111.63016 114.96845
## 2543 105.04076 108.20070 108.01316 108.67903 114.85910
## 2544 136.33958 116.42214 118.02153 196.36738 119.29898
## 2545 109.39168 134.40526 108.83104 227.16294 123.11031
## 2546 123.00981 109.61199 104.76930 133.66675 121.33702
## 2547 108.32169 120.01017 118.33059 300.11344 121.11605
## 2548 117.64437 147.99678 131.01205 157.64839 152.40741
## 2549 102.60741 123.89558 138.41941 117.63546 136.72429
## 2550 171.68524 159.64830 1164.81025 188.21775 125.25585
## 2576 84.15973 87.96539 82.14150 52.64988 89.92114
## 2577 85.84290 105.36680 81.69164 76.54764 88.96352
## 2578 66.61348 82.47773 88.97681 77.81180 41.68769
## 2579 91.51116 63.88924 86.65280 88.66636 91.02956
## 2580 80.66573 79.31961 97.33569 79.00953 96.04200
## 2581 76.94358 95.11866 90.73737 93.48537 79.41074
## 2582 77.71553 97.04058 67.45901 77.10697 92.75237
## 2583 92.30889 93.44149 90.19618 97.58905 97.06502
## 2584 94.85522 97.91254 99.78021 95.81535 92.63786
## 2585 96.83548 96.92005 101.82777 94.57160 100.24917
## 2586 98.78481 99.83780 101.24048 102.77447 100.69733
## 2587 103.27372 107.63287 102.23617 100.21886 101.32439
## 2588 104.01260 110.91719 108.71738 102.11598 105.23602
## 2589 106.44586 108.05007 109.30307 105.73638 109.23746
## 2590 100.48315 100.70149 102.94933 107.60348 99.29293
## 2591 117.36111 112.55568 109.85664 109.64912 150.59021
## 2592 106.18512 112.14252 98.34403 101.86646 111.06092
## 2593 112.14252 114.57763 106.70624 108.38241 103.62725
## 2594 114.33968 163.49441 254.13701 117.02929 117.34249
## 2595 116.14556 196.36738 106.49688 138.90371 120.17103
## 2596 136.83834 134.55432 119.65444 99.45656 269.03839
## 2597 112.69286 121.48569 99.42058 100.61697 106.39642
## 2598 127.97053 322.99700 113.55850 148.98291 108.83501
## 2599 156.47473 161.42447 183.04556 127.37305 127.16439
## 2600 159.70753 132.33709 145.45282 125.25585 144.49301
## 2601 88.97681 56.17321 87.17744 77.73452 86.59730
## 2602 54.27991 57.40160 57.46010 84.16547 49.78525
## 2603 79.47932 86.59202 79.47972 94.96854 87.96539
## 2604 91.76680 94.96576 71.60730 53.32295 89.91046
## 2605 53.96413 62.53634 75.74804 87.60438 85.38321
## 2606 90.84525 94.31004 88.39372 86.43748 93.20106
## 2607 75.36743 82.70288 83.47741 96.90043 95.14822
## 2608 94.35900 70.48157 70.74005 96.48763 92.87873
## 2609 80.81964 97.25576 92.93899 97.88507 97.25576
## 2610 100.11052 98.86147 99.90523 99.81995 101.24048
## 2611 102.29349 100.12346 100.41002 112.68137 100.99320
## 2612 101.49203 101.24287 104.98915 104.54931 112.42372
## 2613 106.16602 103.28180 101.85638 106.16602 107.56032
## 2614 104.63845 106.15365 104.51977 105.00669 122.96154
## 2615 103.35406 107.65160 107.93969 107.73630 132.29487
## 2616 121.63392 132.51117 118.92588 110.01897 109.49226
## 2617 111.84694 118.77751 126.23905 118.79881 108.81487
## 2618 100.39817 445.05135 113.04051 124.61969 108.44376
## 2619 116.73749 136.74067 108.13923 111.98145 108.13923
## 2620 118.02153 178.26917 117.56930 132.04917 109.78499
## 2621 209.70686 110.76649 110.37718 119.04881 111.87852
## 2622 147.53804 121.15786 125.97572 551.74526 113.84924
## 2623 131.12663 173.05755 371.76418 157.64839 130.19223
## 2624 144.42358 194.98985 254.51475 179.39371 166.21369
## 2625 117.36180 131.58517 144.49301 138.72838 132.24449
## 2676 91.22254 76.54764 88.59921 87.26977 69.44062
## 2677 86.59202 81.40956 91.95899 76.52190 77.26177
## 2678 86.30683 65.23464 87.26977 81.40956 86.84609
## 2679 64.21370 92.40642 97.31578 85.47067 71.43483
## 2680 92.75428 92.64793 76.00061 86.40904 86.40904
## 2681 90.38887 70.82698 97.06889 96.46720 98.30169
## 2682 91.12778 69.00003 84.00430 93.54731 94.20589
## 2683 73.98322 98.35328 95.74852 84.40052 69.57160
## 2684 97.45069 91.33018 97.54853 91.33018 91.33018
## 2685 98.27250 100.60372 99.63279 101.96489 100.57566
## 2686 101.96822 100.60372 95.14710 102.46154 98.02852
## 2687 106.37830 104.44993 109.08737 100.81512 105.56488
## 2688 110.36126 104.38677 105.88496 114.49640 101.95037
## 2689 105.58494 112.78478 103.89858 114.77675 120.59903
## 2690 105.07883 107.00910 128.58474 109.72657 111.10829
## 2691 107.03475 144.62638 99.26165 120.61749 108.26402
## 2692 131.98691 166.01565 173.10494 105.34240 130.97942
## 2693 109.42068 112.50254 111.06092 104.83339 171.79073
## 2694 132.44916 108.04980 109.88743 117.59139 127.89172
## 2695 120.19411 128.06191 120.40397 99.15473 114.33968
## 2696 109.00806 132.96764 188.53177 141.39107 280.27231
## 2697 149.56982 108.32169 118.33059 125.97572 125.83242
## 2698 641.17255 123.20902 172.59508 122.01325 116.40820
## 2699 123.77131 162.17781 155.62316 102.90404 132.34528
## 2700 139.71409 117.74875 107.75534 124.18577 150.12676
## 2751 80.05830 76.43678 95.58136 66.58774 64.04461
## 2752 87.33405 89.49587 80.05830 81.93143 76.96728
## 2753 88.97681 70.22650 55.25180 85.95653 85.46386
## 2754 90.33507 90.64730 89.58921 86.65280 96.01923
## 2755 77.44490 73.29890 78.95833 80.47166 70.47614
## 2756 98.30169 85.51540 84.34966 75.36743 94.41715
## 2757 85.09039 95.36544 81.36691 85.35279 75.36743
## 2758 95.31668 67.80351 90.42765 65.86113 96.90043
## 2759 84.80091 94.67902 97.18961 100.60156 96.20178
## 2760 100.63553 100.91489 102.33326 100.62028 98.86450
## 2761 100.62028 99.63279 87.28822 101.40897 95.71676
## 2762 101.30346 102.58764 103.89341 114.94785 103.53782
## 2763 106.10577 104.11632 108.33239 103.83656 119.68387
## 2764 123.20209 105.64624 110.30481 106.47748 124.85189
## 2765 150.95828 104.32306 115.40892 105.34785 115.26170
## 2766 112.95271 105.15247 109.40672 124.49640 133.91909
## 2767 107.74817 158.47743 111.06092 100.65657 115.02123
## 2768 225.73809 130.70175 144.16979 115.35073 108.20070
## 2769 115.95317 134.44921 116.96535 116.14556 132.79034
## 2770 99.49455 182.62523 111.79653 110.38324 110.33821
## 2771 115.01951 106.62682 113.31907 119.04881 159.63346
## 2772 150.69379 144.04249 281.25637 199.02722 127.98187
## 2773 128.97574 131.22664 133.39165 107.00071 171.64470
## 2774 161.07717 125.82266 122.70613 126.82551 156.47473
## 2775 146.68127 105.01465 191.44475 124.17258 121.51767
## 2776 68.38764 80.42268 48.57916 86.24029 94.37539
## 2777 74.10659 57.40160 85.54361 85.35007 89.62937
## 2778 77.34781 97.99331 87.02385 81.88533 88.52320
## 2779 93.75936 75.51253 90.41604 79.91192 91.72003
## 2780 70.47614 87.74308 102.63692 86.00700 73.45181
## 2781 95.25656 81.59536 98.35328 96.59448 95.50927
## 2782 88.90840 91.86796 89.16429 92.27130 72.84820
## 2783 85.84962 92.71862 98.45387 85.09039 96.56863
## 2784 99.78021 94.96702 101.01189 96.21200 97.76746
## 2785 104.11347 102.44425 99.73544 101.27936 98.86147
## 2786 101.05028 100.42671 98.49700 99.65537 100.32598
## 2787 101.78127 110.47760 104.69265 104.64498 119.98249
## 2788 98.06457 101.90236 106.30195 176.17889 103.03508
## 2789 105.11212 104.59202 112.62179 101.23358 130.50308
## 2790 133.86898 126.87452 102.37739 126.87452 119.66948
## 2791 142.75597 106.52577 150.95828 106.12299 122.51773
## 2792 163.02863 102.88259 173.80710 114.70431 142.27293
## 2793 116.01471 111.17364 117.21355 114.42351 108.81487
## 2794 105.61515 112.23080 111.97003 131.72418 114.03509
## 2795 105.89684 109.22285 107.97407 133.42357 116.75754
## 2796 110.92591 124.30313 114.83612 119.31776 161.35232
## 2797 125.66086 140.12399 100.34580 107.26161 122.96619
## 2798 118.14756 150.27818 126.30539 131.22664 157.85270
## 2799 118.87561 130.87151 179.23003 179.95995 180.75454
## 2800 136.81357 129.92929 131.58517 160.54959 123.44480
## 2801 85.65931 104.44557 66.56782 97.99331 88.59921
## 2802 85.35007 105.36680 41.62287 89.35444 95.62290
## 2803 87.80122 89.29480 97.43460 39.64751 76.43678
## 2804 88.16347 77.60870 63.14192 70.85132 92.69186
## 2805 70.11448 92.16046 79.31961 92.92963 64.83246
## 2806 74.58034 73.31933 70.48718 96.48763 86.12273
## 2807 101.08586 88.39372 81.36691 75.63255 81.65959
## 2826 69.00621 97.69301 83.00664 69.44062 104.95791
## 2827 45.05271 90.40317 60.65947 86.69055 62.27695
## 2828 60.67146 89.29480 81.40956 85.58530 50.76591
## 2829 97.01757 88.67350 92.43986 75.69345 87.95048
## 2830 91.71909 64.83246 89.16226 91.02956 73.45181
## 2831 84.00430 91.72060 95.25656 101.49594 72.27934
## 2832 84.11676 100.69083 81.65959 87.78115 97.61660
## 2833 101.77485 97.49668 83.40701 91.62297 97.62545
## 2834 97.00814 98.72624 100.81566 97.91838 97.76746
## 2835 100.12157 95.71676 98.71030 95.14710 100.90765
## 2836 100.51467 101.77396 100.56819 97.93814 99.09979
## 2837 104.54931 101.82713 104.64498 103.72201 103.46780
## 2838 114.83120 175.67105 104.42528 104.67952 101.85638
## 2839 100.39438 106.48477 98.93909 105.77540 105.73638
## 2840 107.03475 110.88988 110.06902 99.68729 107.88385
## 2841 105.45406 109.37046 102.72838 107.35540 99.10803
## 2842 110.21956 110.77789 105.34240 119.10727 108.64252
## 2843 162.59319 120.96710 116.48510 111.17364 126.70203
## 2844 113.82010 110.95365 110.35033 119.59946 120.41282
## 2845 123.11031 115.65967 127.89172 109.89934 111.88715
## 2846 121.91524 124.30313 123.80165 153.95683 119.25739
## 2847 126.42660 193.74717 121.11605 149.56982 137.70471
## 2848 121.63828 234.31872 124.46827 126.16537 134.36864
## 2849 139.77087 132.78361 161.42447 138.84010 120.27834
## 2850 144.49301 142.01975 130.04505 144.07929 1158.86006
## 2901 74.49210 89.86462 85.21347 103.13131 79.11190
## 2902 87.17744 85.65931 87.68738 41.68769 54.07719
## 2903 84.43896 49.97188 84.16547 81.13119 74.07757
## 2904 53.58974 64.61268 76.93336 80.47166 84.59334
## 2905 82.80109 93.75936 63.88924 95.53165 90.90331
## 2906 90.45155 97.54756 92.87873 81.95826 79.41074
## 2907 97.75885 93.51373 97.75885 84.34966 91.80362
## 2908 86.43748 80.12444 95.36544 92.75237 94.20244
## 2909 99.38990 100.48347 100.48347 97.91838 100.36361
## 2910 102.71888 101.43779 101.49803 104.11347 98.78481
## 2911 100.24116 103.30935 100.05478 98.64589 101.33226
## 2912 101.34497 112.73950 105.56488 112.42372 106.56742
## 2913 107.41162 114.50839 104.11632 111.19878 104.39336
## 2914 106.23389 113.20401 104.85046 296.34216 104.51539
## 2915 105.12161 120.76648 103.07126 110.52339 104.39012
## 2916 129.64517 107.35540 105.80979 104.27782 117.36111
## 2917 124.92263 130.70175 112.50254 104.92897 125.17527
## 2918 142.27293 130.70175 173.80710 126.23905 129.20194
## 2919 105.72636 105.04348 117.88098 113.56284 134.27156
## 2920 120.43349 119.59454 100.69865 105.84461 121.15220
## 2921 173.03177 121.33702 102.82315 123.79254 111.23670
## 2922 318.79224 118.87613 111.59231 127.98187 134.16978
## 2923 107.00071 120.23951 108.83501 130.19223 148.98291
## 2924 131.79459 666.96867 137.91349 179.39371 194.26159
## 2925 218.14585 165.32223 123.11356 141.84511 190.30216
## 2926 82.37310 97.73589 36.79145 86.17021 80.05830
## 2927 60.03765 41.56536 68.38764 49.78525 75.67416
## 2928 48.57916 59.26252 77.81180 84.31220 88.03280
## 2929 91.78669 93.17554 91.87575 89.79684 86.40904
## 2930 92.35943 95.73724 84.62964 61.37006 89.91046
## 2951 84.13422 94.96854 91.29758 85.03723 77.28096
## 2952 87.33405 54.23804 62.21331 89.35444 85.54361
## 2953 86.53435 80.89354 86.53435 86.93202 79.11190
## 2954 78.11804 93.33426 93.80144 77.44490 53.96413
## 2955 92.43411 91.02956 51.42515 94.96020 71.60730
## 2976 82.76681 80.05830 90.63433 88.71275 60.03765
## 2977 85.95653 58.84880 80.07062 80.80704 49.97188
## 2978 77.34781 58.63290 63.96155 81.40956 87.53040
## 2979 53.45668 95.78239 86.73150 92.13118 93.63502
## 2980 53.67943 94.96576 85.38321 91.78296 87.57756
## 2981 78.96031 82.25437 96.59448 91.86796 95.52910
## 2982 97.58905 81.59536 88.80377 94.31041 93.32995
## 2983 94.74379 92.79609 94.95988 88.75352 91.04108
## 2984 94.97735 96.03599 86.43389 94.88037 98.73339
## 2985 97.57504 99.09979 101.77396 100.11052 100.61728
## 2986 99.55544 98.49700 99.11660 97.82476 100.05478
## 2987 103.53782 101.82865 103.21992 102.28962 106.37830
## 2988 104.66425 114.83120 110.07958 101.63512 104.66425
## 2989 102.84426 123.20209 102.31685 296.34216 106.44943
## 2990 116.68623 120.76648 109.64912 103.39979 106.27126
## 2991 104.98236 108.42367 380.88026 120.82444 124.49640
## 2992 128.89845 102.72149 113.94137 123.02067 102.88259
## 2993 118.71088 114.81305 105.29668 118.01427 112.63187
## 2994 128.46265 117.56930 118.35876 125.63096 101.47904
## 2995 182.46342 120.45371 183.03745 146.05516 106.32642
## 2996 159.63346 121.63101 188.77562 188.77562 129.18871
## 2997 111.53629 120.26897 193.57454 120.96510 106.00618
## 2998 145.13791 133.72371 130.73546 119.52787 259.83518
## 2999 194.26159 102.60741 116.65668 183.04556 121.95838
## 3000 125.90079 115.57830 204.07685 694.24147 176.53174
## 3051 52.64988 58.63290 103.13131 80.42268 81.13119
## 3052 87.22372 85.03723 84.16547 89.29480 84.18221
## 3053 89.20607 76.45865 45.05271 45.30148 86.26102
## 3054 86.77549 91.73298 91.76680 92.92963 63.88924
## 3055 87.74308 87.57756 86.46140 70.85132 92.72349
## 3056 94.36870 90.44223 90.82499 90.58389 95.11866
## 3057 83.30883 74.55635 74.58034 70.60962 85.50475
## 3058 93.44149 91.04108 78.00297 67.45901 90.82462
## 3059 84.39734 96.70003 96.36786 100.36361 100.57932
## 3060 99.89300 99.27417 99.10475 101.93226 98.83097
## 3061 103.49682 100.60372 99.41273 98.98863 101.43557
## 3062 107.41470 103.03193 102.40672 103.89341 111.49880
## 3063 111.40121 104.04441 106.10577 104.66425 104.04441
## 3064 108.37045 102.31685 101.44664 116.59730 107.91788
## 3065 124.49640 105.35619 105.33277 109.40839 114.57970
## 3066 107.19246 116.62201 133.20341 105.80979 152.66391
## 3067 114.07154 103.62654 105.57382 116.13119 100.98039
## 3068 112.63187 106.83120 115.48170 107.13503 110.33443
## 3069 128.06191 127.89172 119.29898 111.98145 130.64683
## 3070 109.39168 109.89934 105.94276 138.80017 143.06911
## 3071 133.32345 161.35232 113.64695 118.46197 173.21939
## 3072 111.72181 121.11605 112.31792 138.74334 125.18827
## 3073 147.77804 113.79071 119.52787 133.72371 112.59431
## 3074 748.29994 127.37305 143.36483 136.96747 666.96867
## 3075 552.45355 123.11356 160.34981 120.14175 121.42321
## 3076 86.24029 86.26102 85.21347 105.36680 76.52190
## 3077 93.96598 75.67416 84.13422 91.22254 92.17841
## 3078 88.52320 86.59202 86.24029 77.26177 87.19251
## 3079 92.35943 91.73298 92.72349 91.51116 70.83933
## 3080 80.47166 85.47067 70.47614 83.29147 89.58921
## 3081 91.10648 91.29655 81.44024 57.28155 87.88031
## 3082 92.71862 88.80377 79.46693 67.80351 89.40975
## 3101 87.53040 78.65690 82.38095 86.31604 95.99851
## 3102 85.58530 90.62159 81.93143 79.72385 85.21347
## 3103 85.73580 92.17841 41.62287 97.69301 41.56536
## 3104 53.58974 85.01780 86.00700 68.71703 70.11448
## 3105 80.48137 85.84441 89.67338 93.17554 88.88347
## 3106 88.62501 93.19046 96.56863 95.50927 91.14090
## 3107 92.75237 95.86473 69.00003 73.12163 93.44638
## 3126 94.37539 80.21945 88.97681 90.98065 63.88756
## 3127 54.23804 90.98065 76.45865 85.73580 92.38744
## 3128 86.59202 85.10968 81.13119 90.40317 50.76923
## 3129 53.45668 86.77549 86.73150 89.58921 75.74804
## 3130 70.83933 79.00953 75.69345 89.34008 75.74804
## 3131 90.45155 81.19623 99.88840 88.90840 94.96528
## 3132 65.34469 84.84667 95.24657 98.92117 83.40701
## 3133 94.96528 90.45155 97.68078 92.38225 94.24815
## 3134 93.24388 91.18015 98.73339 96.24320 100.72271
## 3135 95.14710 99.62979 99.27417 100.72108 100.64270
## 3136 101.97148 98.98914 101.78696 100.91489 98.99128
## 3137 103.23319 102.00745 111.52278 102.12298 101.41742
## 3138 104.42528 108.28144 110.07958 103.88052 106.92872
## 3139 102.31685 104.55332 104.54255 106.62398 106.44943
## 3140 113.47576 107.45388 106.44959 154.11132 105.52295
## 3141 132.21463 110.01897 109.85664 110.94952 107.78501
## 3142 166.01565 112.22418 124.92916 158.28209 125.63590
## 3143 104.95144 131.98691 158.28209 106.47021 105.56697
## 3144 116.04334 118.15403 113.91920 110.36058 117.63440
## 3145 109.88743 102.82229 125.82905 105.41838 155.76773
## 3146 116.81906 106.35650 109.61199 131.79286 121.02656
## 3147 111.12892 106.00618 118.87613 121.48569 550.46210
## 3148 126.30539 115.76390 120.26701 233.93590 164.06744
## 3149 180.40634 144.57935 446.72370 138.84010 170.04323
## 3150 320.94026 161.41631 158.36933 125.56559 136.81357
## 3201 85.84290 69.07252 95.62290 93.92426 82.37310
## 3202 79.88844 57.46010 77.34781 54.27991 86.84609
## 3203 79.62324 90.82841 78.65690 87.39805 60.57741
## 3204 89.58921 75.65387 91.71909 87.84210 89.12614
## 3205 90.33507 91.65131 89.91046 76.96681 86.00700
## 3206 94.24815 57.27377 85.98439 95.74852 92.72501
## 3207 81.44024 102.22279 92.71862 94.20589 90.44223
## 3208 88.11056 85.50475 94.69594 97.18258 92.43007
## 3209 99.02634 93.70331 94.97735 100.76289 100.81566
## 3210 99.27417 101.00116 100.44107 87.28822 101.38662
## 3211 99.90523 98.00079 100.12157 99.11064 97.64966
## 3212 105.05959 107.11129 102.28962 102.40672 119.98249
## 3213 104.23632 98.06457 98.87984 115.39095 105.86541
## 3214 100.39438 112.62179 251.21835 103.93445 122.96154
## 3215 112.30907 120.56460 105.18351 143.88818 106.44959
## 3216 143.88818 105.15247 102.67333 107.07546 122.76389
## 3217 109.25246 138.67541 115.60556 173.80710 118.01427
## 3218 103.20968 138.15359 166.15643 111.84694 114.57763
## 3219 117.52653 109.21517 117.34249 136.33958 115.99308
## 3220 116.89807 110.33369 141.37796 136.26939 129.81371
## 3221 109.00806 113.25841 123.54022 118.26464 129.18871
## 3222 137.90285 117.72698 134.16978 181.84691 119.44971
## 3223 145.13791 229.18128 172.59508 124.85976 152.11439
## 3224 143.37168 123.01758 447.30782 125.86420 133.97964
## 3225 121.48754 125.90079 132.33709 112.80796 139.05238
## 3226 76.96728 49.78525 80.80704 92.17841 83.26203
## 3227 89.46176 41.56536 78.65690 79.72385 36.84186
## 3228 82.24845 79.68953 76.81953 77.84784 87.19847
## 3229 91.46595 85.50989 93.33426 85.41806 69.59554
## 3230 82.80109 61.37006 75.51253 89.12614 91.44621
## 3251 41.56536 67.95676 45.05271 89.62937 87.79150
## 3252 82.63836 56.17321 90.40317 80.05830 74.07757
## 3253 97.69301 76.38382 86.17021 37.81895 69.44062
## 3254 89.91046 91.51116 92.69186 54.27027 79.31961
## 3255 97.33569 92.03260 91.33736 94.70750 66.86484
## 3276 55.25180 82.24845 41.62287 77.34781 93.96598
## 3277 85.54361 76.52190 49.78525 85.57589 66.61348
## 3278 50.76923 83.45336 77.73452 72.67589 81.40956
## 3279 77.60870 73.45181 88.66636 92.43986 92.52781
## 3280 89.58921 91.62935 67.96218 91.46595 93.33426
## 3281 95.29390 80.73125 95.86473 81.36691 93.63286
## 3282 101.77485 92.93456 84.00430 93.45691 83.82353
## 3283 88.66208 97.62545 90.70701 90.19618 76.84017
## 3284 99.54432 93.37227 92.63786 84.80091 86.57403
## 3285 101.44396 102.70549 101.14849 99.61333 98.99128
## 3286 97.15385 97.58014 99.12850 100.03001 98.98914
## 3287 105.56488 101.82713 106.15890 104.02718 102.40672
## 3288 101.90236 103.88052 103.28180 102.11598 104.99403
## 3289 106.04066 104.59202 104.85046 130.72207 113.99777
## 3290 108.09263 108.13149 108.60962 109.80112 98.76626
## 3291 102.67333 133.91909 128.58474 110.88988 110.06902
## 3292 111.63016 108.44376 113.04051 108.05597 143.61967
## 3293 109.81380 104.95144 105.34240 98.34403 101.86646
## 3294 116.73749 116.94994 163.68244 113.56284 105.65981
## 3295 134.23700 138.80017 106.49688 166.79235 134.27156
## 3296 117.26530 139.10761 110.76649 136.66000 124.26543
## 3297 201.41034 111.59231 133.41070 225.69219 100.20202
## 3298 157.64839 126.45552 130.19223 116.90080 147.77804
## 3299 132.40925 666.96867 194.26159 121.95838 125.77025
## 3300 105.01465 186.82564 222.16757 142.78418 121.42321
## 3351 76.38382 80.07062 89.07701 76.43678 66.58774
## 3352 84.98210 90.63433 93.92426 60.67146 85.57589
## 3353 86.30683 80.42268 79.47972 54.07719 79.68953
## 3354 89.79684 97.63935 88.66636 86.46140 95.78239
## 3355 96.01923 91.71909 78.03115 89.18316 89.12743
## 3356 94.20589 97.54756 91.96508 98.64865 90.84525
## 3357 101.81818 97.95033 69.57160 94.20244 96.81781
## 3358 79.68046 98.98092 92.47394 88.11056 75.36743
## 3359 95.99733 96.28845 96.20178 95.48108 96.30855
## 3360 101.42751 100.93304 100.12128 100.16114 99.66950
## 3361 97.49152 100.51467 98.86147 99.97309 103.18209
## 3362 102.62943 164.68155 103.12208 120.11251 102.15226
## 3363 110.93115 117.06788 98.87984 103.62028 107.75036
## 3364 107.70094 102.69600 130.50308 104.74776 113.20401
## 3365 132.21463 118.11594 105.18351 113.74127 109.72657
## 3366 123.45897 118.92588 119.79731 102.97737 121.63392
## 3367 100.65657 421.37245 102.72149 106.04627 125.88797
## 3368 116.75936 114.42351 118.79881 151.51287 116.13950
## 3369 114.33968 109.21517 117.88098 131.30663 155.31320
## 3370 122.18671 136.74067 119.89695 119.33927 105.36249
## 3371 101.43678 123.61300 119.21853 105.58526 136.66000
## 3372 106.35207 121.11605 109.59070 120.40953 109.90300
## 3373 323.38504 116.40820 152.11439 122.08329 233.93590
## 3374 127.37305 102.60741 126.82551 123.89558 180.75454
## 3375 549.88519 117.36180 694.06484 130.41287 162.00465
## 3376 84.86304 95.62290 84.86304 76.81953 78.65690
## 3377 86.83467 88.03280 94.43840 86.69055 81.96664
## 3378 79.51430 80.21945 54.27991 79.47932 80.05830
## 3379 67.96218 103.24544 91.72003 78.95833 90.20065
## 3380 92.64793 92.72349 79.00953 93.63502 92.75428
## 3381 92.13474 94.28239 87.22890 85.50475 95.01535
## 3397 551.74526 114.09905 121.11605 109.90300 113.01538
## 3398 100.97778 173.05755 126.67551 472.19814 132.05508
## 3399 668.54464 155.62040 121.95838 120.94736 210.71568
## 3400 104.05021 124.17258 132.72405 104.05021 146.68127
## 3401 65.10216 86.30683 68.38764 84.13422 95.58136
## 3402 87.33405 62.21331 89.59827 86.30683 72.74734
## 3403 91.29758 88.71275 84.98210 89.59827 91.23674
## 3404 73.45181 62.68866 93.06302 97.33569 87.77418
## 3405 62.53634 82.68579 91.72003 88.16347 92.72349
## 3406 86.12273 94.69594 88.80377 93.41064 92.75237
## 3407 84.42922 65.73624 93.82111 81.36691 88.96866
## 3408 80.12444 81.36691 94.01651 97.06889 92.80954
## 3409 98.21780 94.08903 98.03552 94.67902 94.08903
## 3410 101.02647 101.43557 101.74162 102.29349 102.33326
## 3411 97.43512 98.98914 99.62979 96.34616 100.04108
## 3412 102.27588 103.23319 104.02718 107.15872 100.21886
## 3413 260.74334 107.75036 175.67105 109.83401 98.06457
## 3414 138.18694 296.34216 123.20209 106.44586 106.04066
## 3415 113.19995 108.26402 98.76626 104.13499 143.88818
## 3416 104.32306 103.64503 106.38121 107.32870 107.35540
## 3417 107.13503 113.43226 111.42145 111.17364 141.53477
## 3418 116.69633 111.88497 107.72305 112.96915 125.93080
## 3419 113.42653 136.33958 125.63096 117.22208 116.29395
## 3420 149.89209 103.63813 109.16456 110.80547 149.15188
## 3421 188.36214 102.82315 139.51296 145.99686 101.02694
## 3422 132.30845 111.72181 120.26897 131.99689 135.68230
## 3423 105.44614 126.30539 129.88913 323.38504 126.16537
## 3424 116.65668 132.01165 131.58996 120.97504 143.82307
## 3425 125.56559 136.95795 172.26418 105.01465 162.00465
## 3426 75.04270 89.67846 79.68953 91.23674 86.72466
## 3427 81.88533 85.54361 54.29599 87.26977 45.05271
## 3428 55.46753 41.56536 55.25180 49.97188 84.98210
## 3429 81.19130 85.41806 64.21370 96.04200 53.96413
## 3430 64.21370 92.03260 90.40701 64.61268 68.72902
## 3431 95.25656 68.83437 94.95988 88.75326 88.02860
## 3432 93.19046 88.96866 77.10697 97.54756 97.18258
## 3433 95.58712 95.36379 100.69083 93.60593 84.21325
## 3434 99.02634 97.89099 92.93899 97.45069 95.82734
## 3435 100.04108 98.71030 101.16487 102.64242 100.35603
## 3436 97.31931 102.41009 98.53717 98.00079 98.54870
## 3437 102.31760 102.59787 102.91591 109.50161 98.62238
## 3438 106.69773 110.91719 115.06283 114.49640 116.41026
## 3439 100.39438 107.78203 104.74776 113.99777 124.85189
## 3440 111.82715 103.35406 101.38032 106.27126 110.94952
## 3441 109.49226 106.86974 112.72046 121.95756 99.10803
## 3442 114.09806 103.32460 108.38241 106.20255 113.96239
## 3443 101.05646 114.83924 445.05135 106.26664 120.96710
## 3444 125.82905 196.36738 178.11105 118.52359 201.13498
## 3445 109.88743 114.33968 163.68244 101.06061 109.04699
## 3446 504.61556 118.26464 121.02656 161.96111 161.35232
## 3447 119.47703 119.44971 121.15786 182.04600 107.46742
## 3448 108.93122 130.73546 113.60385 120.23951 121.45621
## 3449 136.73844 155.62316 136.72429 668.54464 107.63601
## 3450 149.47301 125.69222 172.09421 148.70509 320.94026
## 3501 91.32519 60.03765 86.24029 104.02357 60.45340
## 3502 80.89354 82.18296 87.49543 39.70148 56.17321
## 3503 89.35444 91.32519 74.82542 91.95899 67.53835
## 3504 79.39866 88.67350 87.60438 88.16347 89.67338
## 3505 86.77549 88.66636 90.33507 90.14350 78.11804
## 3506 65.86113 79.36409 87.31443 88.75876 84.11676
## 3507 95.03849 95.50932 85.09039 81.14328 87.78115
## 3508 99.84694 90.18384 68.03409 84.34966 98.37955
## 3509 97.91254 99.54432 96.24320 97.32737 99.43150
## 3510 99.04384 100.56819 100.16114 101.97148 99.98555
## 3511 102.64242 100.50736 100.31855 100.34017 102.46154
## 3512 102.77120 101.33319 107.15872 101.30591 103.28508
## 3513 110.69491 105.96625 137.77702 102.92406 104.09200
## 3514 115.07238 146.43261 115.81716 106.44943 109.23746
## 3515 109.40672 120.63251 117.36111 115.40892 129.13669
## 3516 103.64503 155.66885 107.06727 105.34785 129.79219
## 3517 108.45682 128.89845 156.06070 110.21956 125.53942
## 3518 128.31784 113.69280 104.05528 100.39817 101.05646
## 3519 175.39744 111.98145 107.93025 119.67466 133.08417
## 3520 136.33958 482.34621 113.82010 149.90408 134.44921
## 3521 146.31352 119.31776 107.86507 119.21853 131.79286
## 3522 134.16978 225.69219 125.53161 107.26161 117.03804
## 3523 145.29628 323.38504 134.68528 133.72371 107.00071
## 3524 215.03420 448.92817 121.40603 214.85200 120.94736
## 3525 124.18577 121.51767 146.90039 187.06083 264.45420
## 3576 97.73589 75.04270 64.04461 55.46753 90.98065
## 3577 88.03280 89.92114 87.19251 69.07252 64.04461
## 3578 63.96155 60.67146 79.11190 69.07252 77.81180
## 3579 90.41604 64.61268 72.67259 90.90331 81.00761
## 3580 88.88347 53.58974 92.14181 87.84210 79.87391
## 3581 82.25437 86.43748 84.00430 86.65654 95.90900
## 3582 96.38773 94.20589 96.38773 94.31004 94.01651
## 3583 92.11196 81.56326 88.75876 99.33319 79.68046
## 3584 100.81566 95.48108 95.98072 99.11068 86.69231
## 3585 99.09051 98.36124 101.54046 96.34616 95.71676
## 3586 100.12346 100.18869 99.16667 100.35603 97.49152
## 3587 103.27481 98.62238 105.90647 107.11129 105.61450
## 3588 107.63745 104.06422 107.41162 103.69813 105.71932
## 3589 100.39438 101.96945 97.88709 104.90277 104.74776
## 3590 120.33720 100.48315 111.06992 105.18351 121.95756
## 3591 106.69451 116.28912 103.64503 102.41684 105.15247
## 3592 108.54908 107.74817 110.16206 107.05697 104.18835
## 3593 104.83339 114.56494 109.17025 114.09806 106.47021
## 3594 112.23080 182.46342 106.49688 132.79034 113.40656
## 3595 119.29898 115.09044 116.42214 136.26939 126.33702
## 3596 153.95683 269.03839 111.87852 104.76930 126.65652
## 3597 281.25637 125.97572 280.86383 114.28304 298.75559
## 3598 126.67551 322.99700 109.17176 121.62320 173.03357
## 3599 748.29994 155.95655 120.97504 161.07717 120.27834
## 3600 132.23335 131.58517 141.84511 176.53174 320.94026
## 3651 86.59202 90.87033 91.12610 98.00046 88.74066
## 3652 82.16888 86.24029 68.22153 55.25180 83.06365
## 3653 89.59827 74.82542 87.13276 87.02385 97.43460
## 3654 89.67338 96.01923 89.79684 85.38321 93.75936
## 3655 91.73298 78.03115 92.03260 92.56843 91.27376
## 3656 100.24863 84.30664 89.16429 75.81067 72.27934
## 3657 95.24657 83.40701 81.56326 81.36691 85.28735
## 3658 96.43636 74.04314 94.16257 85.25325 93.81893
## 3659 96.28845 93.24388 97.94876 92.93899 99.43150
## 3660 101.77396 99.98555 104.11347 101.96489 101.54046
## 3661 101.53984 100.52245 99.09051 98.54870 100.56819
## 3662 102.58764 102.59787 99.97401 106.03844 102.74791
## 3663 103.14673 104.66425 103.46585 110.32772 102.92240
## 3664 115.52511 105.62299 120.59903 146.30854 120.74558
## 3665 132.29487 107.66647 104.58508 117.88453 107.00910
## 3666 105.33277 326.24215 108.22281 107.06727 111.65145
## 3667 102.88259 131.96356 130.70175 112.22418 120.27348
## 3668 108.64252 151.51287 123.02067 162.89176 116.22330
## 3669 105.41838 130.64683 109.36204 131.13302 111.98145
## 3670 123.39230 109.40244 117.60652 105.37942 163.49441
## 3671 117.26530 110.46473 111.41676 121.63101 101.38071
## 3672 115.23936 100.34580 127.98187 118.87613 113.84924
## 3673 122.01325 116.40820 182.31015 101.32392 205.16297
## 3674 102.60741 126.91669 121.95838 134.46696 127.07919
## 3675 149.47301 186.82564 125.56559 161.15159 142.78418
## 3726 89.49587 86.59202 89.62937 89.29480 80.76570
## 3727 70.22650 88.74066 105.36680 89.67846 70.89381
## 3728 77.26177 88.98440 95.99851 81.93143 85.65931
## 3729 67.96218 85.01096 91.02956 76.93336 90.41604
## 3730 92.14181 75.74804 87.57756 86.77549 88.67350
## 3731 88.96866 101.30804 70.48157 96.35980 98.64865
## 3732 90.18384 81.36691 96.43636 70.48718 81.56326
## 3733 96.35391 84.42922 95.08687 92.22074 93.41064
## 3734 95.33453 92.29034 97.54853 93.63993 96.80581
## 3735 95.79865 100.37677 97.96420 97.49152 98.93923
## 3736 100.48467 100.31855 97.73434 100.79741 97.64966
## 3737 102.77120 116.80319 104.54117 103.72201 103.46780
## 3738 104.23632 102.92406 109.96419 110.07958 104.97123
## 3739 106.79215 105.97367 110.40473 111.36948 108.37045
## 3740 107.88385 116.11344 120.63251 107.03475 113.14683
## 3741 102.41684 113.47576 110.84700 104.98236 380.88026
## 3742 138.67541 114.85910 113.18423 109.29728 105.50042
## 3743 108.00357 113.94137 101.13796 125.17527 114.64604
## 3744 104.30384 125.63096 119.67466 130.64683 122.18671
## 3745 119.67466 231.53177 110.36058 118.56621 123.13061
## 3746 113.64695 105.91190 110.44112 132.60389 117.71484
## 3747 160.98321 199.02722 118.92416 117.37784 112.69286
## 3748 119.52787 162.66276 371.76418 157.64839 197.99311
## 3749 120.27834 116.65668 138.41941 102.60741 143.37168
## 3750 125.68050 142.88328 264.45420 159.64830 218.14585
## 3801 87.79150 86.59202 103.54970 74.10659 85.10968
## 3802 86.53435 72.74734 77.84784 77.84784 86.26102
## 3803 36.79145 87.80122 93.96598 90.62159 82.76681
## 3804 89.79684 91.65131 81.19130 79.31961 67.96218
## 3805 59.41362 93.06302 91.78296 90.74173 102.63692
## 3806 94.04152 73.79112 65.23077 76.94358 98.98092
## 3807 84.34966 94.63581 92.38225 88.10025 77.71553
## 3808 95.31668 75.51250 94.35900 97.79130 95.68366
## 3809 94.67902 98.92178 93.63993 99.02634 98.72624
## 3810 100.02075 100.48408 96.69969 101.43557 101.43779
## 3811 101.27397 101.44396 99.62495 98.53717 99.93156
## 3812 104.30985 164.68155 98.94547 104.55716 103.12208
## 3813 97.65152 127.01718 104.36852 114.83120 104.01260
## 3814 112.63745 104.59202 100.88040 108.25918 109.87936
## 3815 106.15231 105.62608 112.72938 119.66948 107.73630
## 3816 124.49640 157.31993 128.58474 105.81129 102.97737
## 3817 114.83924 108.32327 123.02067 103.64332 135.20384
## 3818 126.63299 118.71088 108.18084 101.13796 130.97942
## 3819 113.82010 136.33958 109.79184 142.76316 143.06911
## 3820 183.98454 148.29801 120.40397 109.74483 116.29395
## 3821 209.26474 126.04950 123.80165 111.36527 126.34791
## 3822 122.96619 134.16978 124.99091 168.76751 114.09905
## 3823 322.99700 131.01205 111.01689 233.93590 126.33907
## 3824 668.54464 127.50841 121.95838 138.84010 138.41941
## 3825 134.47319 190.33094 138.58183 218.51238 694.24147
## 3876 89.29480 57.46010 52.63789 87.51037 89.67846
## 3877 94.43840 77.28096 98.00046 68.22153 74.10659
## 3878 83.11619 54.29599 90.62159 88.74066 80.88184
## 3879 63.37863 73.45181 64.83246 88.16347 92.75428
## 3880 93.17554 96.04200 90.40701 67.96218 68.72902
## 3881 96.48148 78.96031 94.24815 94.49291 96.46720
## 3882 77.22249 98.35328 95.35421 88.21839 94.95789
## 3883 88.90840 88.10025 95.31668 93.89796 88.29308
## 3884 98.21780 100.60156 97.89099 98.21780 91.49750
## 3885 101.31817 100.31597 104.82981 101.90641 100.12157
## 3886 98.83097 101.05028 101.16487 99.66950 101.97148
## 3887 98.94547 98.62238 100.99355 102.27588 100.99107
## 3888 103.62028 99.85425 98.87984 104.67952 101.78633
## 3889 104.54255 110.43121 110.43121 106.02560 115.34707
## 3890 117.88453 120.33720 105.34785 105.52295 98.91687
## 3891 105.72252 118.92588 153.83333 104.04960 105.07883
## 3892 116.88010 110.77789 116.13950 138.67541 163.02863
## 3893 174.29516 135.17986 130.70175 116.47105 109.42068
## 3894 123.11031 134.27156 117.60652 131.30663 117.02929
## 3895 108.60014 105.68000 105.68665 113.83465 149.90408
## 3896 123.54022 111.41676 112.70218 106.35650 111.23670
## 3897 112.81294 113.84924 111.59231 112.81294 112.69286
## 3898 116.90080 130.19223 147.77804 127.06840 197.77564
## 3899 446.58161 127.16439 744.15548 127.07919 103.33840
## 3900 264.08547 128.53692 103.63347 113.17387 135.13134
## 3901 68.22153 77.26177 86.59202 62.27695 88.98440
## 3902 77.84784 79.54693 56.17321 70.22650 91.32519
## 3926 85.54361 84.13422 76.78643 37.61926 90.98065
## 3927 64.04461 86.69055 95.99851 92.17841 86.72466
## 3951 97.43460 76.52190 91.29758 76.81953 77.26177
## 3952 84.31220 85.84290 57.46010 89.67846 72.74734
## 3953 41.62287 76.52190 87.33405 77.84784 87.24826
## 3954 89.12743 97.33569 53.45668 91.76680 88.95131
## 3955 94.96576 97.63935 89.16226 91.02956 91.51116
## 3956 82.70288 90.70701 85.35279 89.16429 79.43407
## 3957 91.27871 86.60671 99.88840 89.32484 76.94358
## 3958 91.10648 97.06502 95.08687 91.04108 57.28155
## 3959 83.11151 95.99733 97.53524 96.59170 100.48347
## 3960 100.12157 104.30909 99.09051 99.69388 100.11052
## 3961 101.74162 97.82476 99.27417 100.48408 99.63279
## 3962 103.27481 109.08737 102.58764 105.05959 107.55417
## 3963 101.96463 104.97123 104.97123 103.69813 115.19239
## 3964 122.02396 109.87936 107.91788 105.21949 115.34707
## 3965 111.65145 117.41760 110.84700 107.00910 112.52256
## 3966 132.51117 106.00929 107.88385 127.75305 109.72657
## 3967 184.33610 110.33443 116.48510 123.43232 108.20070
## 3968 104.95144 120.27348 106.26664 171.60518 130.97942
## 3969 123.60592 119.33927 146.06715 119.77823 105.65981
## 3970 115.09044 120.41282 141.37796 128.06191 263.32021
## 3971 124.26543 112.05781 281.71217 105.52984 116.81906
## 3972 201.41034 193.57454 100.61697 127.98187 108.58455
## 3973 116.40820 205.16297 116.40820 173.03357 369.82714
## 3974 121.29630 247.11923 127.37305 131.94135 132.44122
## 3975 172.26418 123.84166 187.06083 168.06688 121.42321
## 4026 39.70148 85.54361 81.69164 62.21331 79.72385
## 4027 85.57589 62.21331 92.38744 81.69164 88.59921
## 4028 85.21347 54.07719 80.07062 89.46176 66.58774
## 4029 97.33569 90.33507 96.01923 62.68866 59.41362
## 4030 76.00061 75.69345 86.73150 63.14192 81.00761
## 4031 81.95826 82.70288 85.09039 88.75326 83.47741
## 4032 86.65654 97.43797 93.45691 91.12778 98.35328
## 4033 91.54061 89.08083 90.82462 89.81119 95.52910
## 4034 97.51408 96.73800 97.55989 96.61032 97.54853
## 4035 99.27417 99.27417 98.86147 100.79741 105.57289
## 4036 97.63176 99.71523 100.63553 101.93226 104.30909
## 4037 102.02099 101.33319 104.98915 107.10395 98.62238
## 4038 103.69813 104.38677 109.90148 115.39095 104.23632
## 4039 111.51049 106.44586 113.20401 118.86091 122.96154
## 4040 108.42367 151.36925 103.95877 111.10829 113.13935
## 4041 126.73526 122.20380 120.63251 107.07546 103.64503
## 4042 136.54922 116.13119 123.43232 109.81380 123.43232
## 4043 103.62725 143.06488 126.63299 114.09806 113.18423
## 4044 116.04334 131.40097 116.77544 117.92879 110.26906
## 4045 131.58368 138.80017 134.44921 107.93025 114.13370
## 4046 139.28538 139.51296 101.43678 145.10697 114.82032
## 4047 182.04600 116.78040 112.27077 119.04110 193.57454
## 4048 118.14756 322.99700 116.63109 126.49420 469.84718
## 4049 157.06490 125.77025 180.40634 180.75454 132.01165
## 4050 142.88328 142.01975 117.36180 165.70582 121.48754
## 4101 89.59827 84.18221 89.62937 60.57741 86.53435
## 4102 41.56536 76.52190 84.31220 77.28096 98.00046
## 4103 74.10659 81.69164 96.04111 87.33405 97.73589
## 4104 78.11804 66.86484 77.44490 70.85132 92.03260
## 4105 87.95048 69.59554 73.29890 90.90331 76.93336
## 4106 87.88031 94.80933 82.70288 91.04108 95.86473
## 4107 95.31668 70.82698 95.09096 88.75876 92.71862
## 4108 96.43636 88.11056 94.74379 90.19618 101.81818
## 4109 97.65004 100.57932 93.70331 100.36361 101.11111
## 4110 101.40897 104.11347 101.43557 99.11064 101.86862
## 4111 99.61333 97.59288 99.97309 99.64087 96.83548
## 4112 104.30985 100.81512 103.46780 102.21498 106.37830
## 4113 100.90090 105.71932 110.91719 104.01260 103.83656
## 4114 109.98478 104.51977 110.40473 107.78203 106.02560
## 4115 104.98236 122.76389 141.68112 110.88988 118.21245
## 4116 114.70735 114.70735 116.68623 112.95271 124.49640
## 4117 160.60608 115.48170 171.60518 111.84694 151.70141
## 4118 135.20384 106.20642 98.34403 124.61969 114.65548
## 4119 109.79184 117.60652 110.82173 108.91792 108.26185
## 4120 113.56284 105.89684 104.77194 119.33927 108.26185
## 4121 99.11934 126.04950 123.80165 160.52852 161.35232
## 4122 121.26949 120.26897 120.40953 123.57199 100.20202
## 4123 109.17176 135.07640 641.17255 115.81266 117.64437
## 4124 153.54877 143.36483 179.95995 161.07717 143.37168
## 4125 146.68127 218.51238 128.48153 123.65758 161.63692
## 4126 81.69164 105.36680 89.92114 85.95653 57.40160
## 4127 82.18981 48.49429 89.07701 87.22372 85.15462
## 4128 87.51037 80.76570 70.22650 90.63433 85.03723
## 4129 86.61646 92.56843 87.60438 84.59334 70.47614
## 4130 94.96020 85.01096 79.39866 86.77549 86.00700
## 4131 96.43636 88.29308 96.59448 97.06502 102.22279
## 4151 79.11190 87.79150 84.31220 82.38095 70.89381
## 4152 57.40160 88.03280 87.96539 41.56536 85.10968
## 4153 82.38095 80.32086 79.62324 76.78643 88.96352
## 4154 93.06302 85.84441 89.12743 95.73724 91.44621
## 4155 79.91192 76.00061 79.91192 66.86484 85.47067
## 4156 75.81067 92.75237 84.84667 95.29390 84.00430
## 4176 85.57589 80.05830 94.43840 87.90980 68.22153
## 4177 85.57589 89.49587 58.84880 52.64988 63.96155
## 4178 94.43840 86.69055 72.67589 92.01060 87.90980
## 4179 95.73724 79.07915 64.89455 81.00761 86.40904
## 4180 97.01757 102.22222 70.83933 87.74308 67.96218
## 4181 91.86796 99.33319 83.82353 85.35279 79.43407
## 4182 73.59809 91.06227 92.80954 99.84694 90.45155
## 4183 93.08193 79.08784 94.36870 86.12273 98.95550
## 4184 99.02634 93.46696 86.57403 99.65553 98.72624
## 4185 99.97309 101.96822 99.00218 100.50736 96.92005
## 4186 101.26409 99.10475 101.25268 95.14710 95.71676
## 4187 103.27372 144.11512 104.98080 124.71287 101.34497
## 4188 111.40121 110.32772 110.91719 175.67105 103.88052
## 4189 104.85046 102.69600 105.97367 105.32322 106.44943
## 4190 129.13669 117.41760 108.04976 103.35406 110.84700
## 4191 105.34785 103.29518 107.07546 142.50793 109.85664
## 4192 116.88010 173.10494 107.61321 120.98189 100.39817
## 4193 132.25443 109.70550 128.27916 116.24810 111.20249
## 4194 110.82173 116.42214 109.36204 109.74483 110.66111
## 4195 116.65831 132.44916 122.56407 109.04699 262.87131
## 4196 173.03177 139.51296 111.94593 109.74024 104.76930
## 4197 144.04249 300.11344 150.69379 118.33059 106.92900
## 4198 157.64839 115.44542 126.67551 119.61698 147.97806
## 4199 179.95995 157.06490 210.95186 143.36483 128.56307
## 4200 121.48754 130.08440 125.69222 125.56559 123.65758
## 4201 77.34781 86.72466 86.30683 66.56782 66.58774
## 4202 87.49543 58.84880 85.84290 77.28096 48.57916
## 4226 85.65931 68.38764 49.78525 62.21331 104.02357
## 4227 84.98210 80.07062 81.40956 74.49210 88.03280
## 4251 74.82542 87.80122 36.79145 89.07701 56.17321
## 4252 88.59921 89.62937 67.53835 80.76570 72.74734
## 4253 91.29758 82.47773 85.15462 86.17021 91.22254
## 4254 64.83246 86.73150 88.95131 92.92963 68.72902
## 4255 102.22222 76.93336 92.40642 79.31961 61.37006
## 4256 73.98322 78.96031 68.03409 70.48157 81.44024
## 4257 86.43748 98.73440 72.27934 83.30883 84.21325
## 4258 57.27377 92.22074 92.11196 78.96031 91.10648
## 4259 103.22245 96.24320 91.33018 95.82734 95.33453
## 4260 101.14849 100.79741 101.96822 100.23257 100.27494
## 4261 99.71523 101.97148 100.82659 100.31597 100.24917
## 4262 111.52278 101.30346 102.77120 101.24287 101.30591
## 4263 105.63353 97.65152 102.92240 108.71738 116.41026
## 4264 120.16029 110.24742 100.39438 108.05007 106.08537
## 4265 123.31008 113.47576 111.04849 103.79710 99.68729
## 4266 327.17640 105.31331 140.96680 121.95756 112.95271
## 4267 127.07225 116.13950 108.26279 131.98691 105.46017
## 4268 107.72305 144.16979 111.42145 126.70203 156.88157
## 4269 109.04699 102.82229 132.79034 131.72418 133.42357
## 4270 123.60592 108.91792 121.43812 118.02153 113.40656
## 4271 188.46154 110.44112 188.46154 115.73388 114.83612
## 4272 125.66086 160.95923 99.75966 112.69286 201.41034
## 4273 140.05422 147.97806 121.63828 120.94487 115.81266
## 4274 748.29994 132.01165 153.54877 136.73844 143.62978
## 4275 142.88328 124.18577 145.45282 107.75534 132.24449
## 4276 84.18221 54.07719 90.87033 87.51037 87.80122
## 4277 103.54970 81.88533 81.69164 49.78525 91.29758
## 4301 66.58774 79.88844 85.95653 68.38764 48.49429
## 4302 55.25180 86.93202 58.84880 66.61348 80.42268
## 4326 66.61348 80.88184 88.52178 66.58774 67.53835
## 4327 92.17841 104.95791 87.90980 85.95653 79.62324
## 4328 85.35007 57.40160 65.23464 62.27695 85.35007
## 4329 61.05559 54.27027 66.86484 91.50520 91.78296
## 4330 71.60730 62.53634 89.16226 90.74173 73.45181
## 4331 81.36691 96.46720 87.76968 94.95988 97.62545
## 4332 95.08687 96.84995 93.81893 75.51250 80.12444
## 4333 92.75237 90.70701 76.71611 97.36059 77.22249
## 4334 95.33453 103.22245 92.99362 97.91254 98.04965
## 4335 98.87331 101.82777 100.26036 98.49700 100.24537
## 4336 100.71676 101.44396 104.11347 100.48408 100.42671
## 4337 101.03491 119.98249 106.03844 98.22391 107.11129
## 4338 109.83401 105.82849 104.24219 110.16345 103.88052
## 4339 101.44664 104.63845 102.31685 106.00146 105.97255
## 4340 121.34056 106.38121 103.95877 107.66647 153.83333
## 4341 120.33720 106.15231 103.79710 338.64140 99.29293
## 4342 421.37245 109.17025 123.43232 125.63590 172.79024
## 4343 125.38679 100.65657 107.05697 108.01316 106.18512
## 4344 107.97407 117.94491 109.52637 105.68665 114.03509
## 4345 117.52653 155.76773 121.15220 118.52359 109.21517
## 4346 114.82032 118.46197 124.30313 111.36527 503.42939
## 4347 137.70471 120.49509 112.27077 107.26161 181.84691
## 4348 134.35013 119.08348 116.40820 116.90080 323.38504
## 4349 247.11923 136.37268 126.82551 447.30782 132.97959
## 4350 694.24147 129.84296 186.58759 113.17387 128.53692
## 4401 77.26177 86.83467 77.81180 83.26203 79.88844
## 4402 86.59202 37.81895 103.54970 91.29758 50.76923
## 4403 103.54970 76.96728 81.69164 86.69055 86.59730
## 4404 65.97222 85.38321 88.67350 89.34008 70.47614
## 4405 86.46140 85.41806 82.88179 90.14350 92.14181
## 4406 87.76968 87.76968 85.35279 100.72745 88.75352
## 4407 98.37955 89.32484 95.36379 80.53962 88.96866
## 4408 95.06454 101.49594 94.67286 94.21084 82.36210
## 4409 95.99433 97.55989 100.60156 86.84800 97.91838
## 4410 98.86147 100.14811 101.96822 100.90765 97.34595
## 4411 98.99128 100.26036 101.50892 100.37677 98.00079
## 4412 101.49203 102.31760 102.97982 104.70148 104.18608
## 4413 101.78633 103.14673 100.90090 108.71738 101.95037
## 4414 115.52511 110.24742 106.79215 122.96154 105.74991
## 4415 102.88066 109.37046 107.03475 107.03475 133.63401
## 4416 99.26165 107.93969 109.38277 109.40672 108.16236
## 4417 125.47516 111.17364 109.42068 116.24810 103.32460
## 4418 112.42533 117.21355 114.70431 162.89176 107.14378
## 4419 123.60592 111.79653 131.46856 129.13351 105.60676
## 4420 143.06911 115.65967 120.17103 103.63813 110.80547
## 4421 110.37718 115.59516 121.11444 121.91524 269.45487
## 4422 101.07600 119.70382 225.69219 201.07692 144.04249
## 4423 164.06744 108.93122 115.76390 115.17637 127.97053
## 4424 215.03420 210.71568 143.37168 133.97964 140.19862
## 4425 203.22596 144.04091 117.42197 264.45420 165.32223
## 4476 56.17321 90.62159 86.59202 57.40160 93.66601
## 4477 63.12598 96.04111 82.76681 75.04270 91.22254
## 4478 81.88533 88.52178 83.45336 87.02385 89.29480
## 4479 90.20065 65.48751 66.86484 53.67943 89.58921
## 4480 97.33569 85.38321 89.58921 94.70750 68.71703
## 4481 88.27120 84.21325 94.13769 88.08301 92.99346
## 4482 96.28103 99.84694 92.67817 94.74379 70.74005
## 4483 93.54729 92.87515 76.24359 101.30804 93.30245
## 4484 99.54432 100.48347 94.96702 66.79041 94.96702
## 4485 99.67026 100.42671 100.41819 101.72575 101.43557
## 4486 101.95012 98.83097 101.05372 102.44425 101.38605
## 4487 102.97982 101.35203 106.15890 110.28205 103.96579
## 4488 110.93115 101.96463 102.47183 104.03054 105.84633
## 4489 105.21949 105.62299 111.51049 296.34216 104.85592
## 4490 99.10803 117.59082 103.29518 102.72838 111.10829
## 4491 111.55464 108.46440 103.29518 120.61749 102.97737
## 4492 113.64498 130.86886 113.61904 117.21355 114.85910
## 4493 105.41559 445.05135 114.81305 104.83339 108.38241
## 4494 108.26185 99.61235 149.15188 109.36204 112.36902
## 4495 119.77823 110.72814 109.77954 254.13701 109.16456
## 4496 161.35232 146.31352 115.59516 109.61199 188.36214
## 4497 122.96619 113.01538 112.95776 106.92900 148.34477
## 4498 119.52787 113.60385 145.13791 197.77564 162.66276
## 4499 131.37534 121.40603 125.86420 122.70613 110.81273
## 4500 172.09421 132.72405 138.56469 117.74875 204.07685
## 4551 86.59730 89.59827 83.45336 82.47773 54.07719
## 4552 70.89381 91.23674 82.16888 81.69164 85.65931
## 4553 79.47972 65.23464 39.70148 82.16888 76.38382
## 4554 103.24544 86.73150 92.69186 89.34008 91.65234
## 4555 102.63692 70.34297 69.59554 89.23611 89.18316
## 4556 91.54061 85.51540 92.72501 81.65959 92.71848
## 4557 86.60671 72.27934 86.07052 93.88938 78.89058
## 4558 88.04751 94.31004 88.75326 87.22890 91.27871
## 4559 98.34969 99.43150 93.89692 94.08903 100.72271
## 4560 100.14811 102.70549 103.82921 97.43512 98.35841
## 4561 102.33326 100.23257 100.31855 100.82826 99.27417
## 4562 101.33319 105.56488 106.43074 102.12298 104.55716
## 4563 99.22778 104.01260 128.88305 107.75036 110.36126
## 4564 141.25711 103.89858 105.62299 101.96257 122.96154
## 4565 121.63392 116.62201 116.60229 106.12299 104.27782
## 4566 121.34056 129.64517 100.67826 154.11132 100.70149
## 4567 125.27704 114.42351 105.34240 107.74817 117.21355
## 4568 107.12818 116.13119 114.85910 112.42533 113.61904
## 4569 103.24642 106.32642 106.49688 132.73306 111.88715
## 4570 111.97003 108.91792 122.18671 227.16294 123.60592
## 4571 121.33702 121.02656 110.44112 252.64430 139.51296
## 4572 134.16978 133.75443 112.27077 112.81294 199.02722
## 4573 122.30426 162.66276 118.14450 152.11439 145.13791
## 4574 117.63546 298.66443 107.63601 102.96003 131.42284
## 4575 104.05021 265.24127 171.68524 117.42197 107.80223
## 4626 78.65690 80.32086 74.82542 88.52178 74.10659
## 4627 89.29480 89.46176 58.63290 81.96664 95.58136
## 4628 86.93202 88.52320 98.00046 48.49429 69.00621
## 4629 64.83246 89.16226 70.34297 93.33426 91.87575
## 4630 94.38345 93.17554 70.11448 86.46140 86.77549
## 4631 92.67817 92.27130 81.36691 69.46001 95.03849
## 4632 86.12273 81.14328 94.28239 78.96031 88.96866
## 4633 98.92117 91.14090 94.41715 85.28735 80.12444
## 4634 97.55989 86.43389 97.70301 96.20178 66.79041
## 4635 99.04384 101.53984 99.62979 103.84216 98.27656
## 4636 98.86147 96.37148 100.56819 100.14188 100.26036
## 4637 107.41470 107.55417 107.63287 103.27372 102.27588
## 4638 107.63745 103.88052 105.03229 105.84633 104.99403
## 4639 104.54255 104.19667 101.96945 108.52239 115.81716
## 4640 112.55576 142.75597 339.67420 105.81129 101.38032
## 4641 109.88184 111.04849 119.79731 105.18351 106.86974
## 4642 108.18084 111.71435 106.83120 171.60518 109.25246
## 4643 108.18084 106.26664 103.64332 113.43226 113.61904
## 4644 163.49441 117.63440 110.33821 110.26906 115.99308
## 4645 461.00847 134.27156 131.30663 117.56930 120.17103
## 4646 115.01951 106.40085 269.03839 114.83612 119.46401
## 4647 143.70353 121.48569 119.44971 134.12285 169.39628
## 4648 111.01689 121.63828 147.99678 115.76390 149.91768
## 4649 153.54877 744.15548 126.91669 153.54877 132.97959
## 4650 549.88519 113.22582 141.93627 1158.86006 141.84511
## 4651 82.47773 94.43840 81.13119 54.07719 41.91126
## 4652 80.05830 52.63789 90.87033 87.49543 69.07252
## 4653 77.84784 95.62290 89.46176 93.96598 82.75869
## 4654 90.41604 88.66636 95.53165 89.70028 91.65234
## 4655 89.23611 90.40701 94.96020 92.40642 93.75936
## 4676 76.45865 85.84290 54.07719 82.14150 69.07252
## 4677 105.36680 50.76591 87.22372 80.21945 87.68738
## 4678 75.67416 90.87033 69.00621 70.92046 75.04270
## 4679 71.60730 90.20065 92.03260 73.45181 92.16046
## 4680 91.78296 96.04200 93.06302 89.91046 89.12743
## 4701 90.82841 55.46753 76.45865 85.10968 75.04270
## 4702 41.91126 76.81953 86.30683 74.49210 82.47773
## 4703 76.52190 76.96728 86.83467 80.76570 86.83467
## 4704 94.38345 89.12614 75.69345 90.71247 95.53165
## 4705 53.32295 87.73624 59.30769 65.97222 79.39866
## 4706 76.36179 92.07572 94.96528 87.19216 88.75352
## 4707 94.35900 88.75326 92.72501 93.63286 95.52910
## 4708 92.67817 87.94957 97.04058 80.12444 67.14593
## 4709 100.79177 91.33018 86.43389 99.83715 91.29703
## 4710 101.78696 100.55374 100.48467 100.82826 87.53341
## 4711 101.54046 100.61728 100.42671 103.49682 102.66218
## 4712 104.02852 101.33319 112.42372 116.21552 106.56742
## 4713 101.85638 103.83656 114.50839 105.23602 107.83794
## 4714 109.98478 105.73638 106.08537 104.51977 107.70094
## 4715 112.52256 110.94952 107.00910 99.26165 112.77843
## 4716 116.11344 116.62201 112.95606 127.27591 114.76495
## 4717 105.56697 98.68517 111.90901 110.65562 208.23501
## 4718 109.17025 102.88259 106.70624 126.23905 114.96845
## 4719 109.21517 108.04980 141.37796 108.60014 149.71884
## 4720 116.42214 155.17818 129.81371 115.63598 114.13370
## 4721 146.31352 111.23670 106.35650 123.54022 115.16107
## 4722 138.74334 115.23936 125.66086 116.78040 113.34752
## 4723 126.84464 164.06744 128.50156 115.44542 101.32392
## 4724 448.92817 158.06623 180.95638 143.37168 120.74235
## 4725 136.08946 136.81357 123.65758 130.28090 139.05238
## 4776 84.35955 88.59921 91.23674 66.61348 74.82542
## 4777 57.46010 48.57916 74.49210 60.03765 74.49210
## 4778 91.23674 79.51359 82.76681 83.00664 69.07252
## 4779 64.21370 70.11448 78.95833 91.76680 83.29147
## 4780 68.72902 91.65234 97.33569 91.78669 91.78669
## 4781 81.65959 77.71553 74.58034 94.95789 86.12273
## 4782 102.22279 85.98439 65.23077 95.86473 95.03849
## 4783 85.28735 73.12163 95.11866 94.31004 94.20589
## 4784 93.63993 93.63993 97.70301 95.99733 93.24388
## 4785 102.29349 99.50709 106.21088 101.84448 100.04916
## 4786 101.78553 95.71676 99.89300 101.71123 97.32866
## 4787 100.99355 101.19407 112.73950 102.91591 107.15872
## 4788 104.24219 115.19239 104.26492 100.72350 102.47183
## 4789 105.11212 102.18154 110.30481 137.90473 100.88040
## 4790 109.85664 124.49640 115.40892 106.86974 127.75305
## 4791 124.49640 111.04849 112.55568 112.55576 110.06902
## 4792 130.97942 166.15643 114.96845 110.77789 111.20249
## 4793 108.44376 116.13950 141.53477 172.79024 106.83120
## 4794 461.00847 131.13302 117.63440 122.18671 99.95210
## 4795 118.24087 132.79034 111.97003 105.61515 196.36738
## 4796 131.79286 111.87852 124.30313 139.84188 114.83612
## 4797 116.78040 113.34752 127.98187 300.11344 119.04110
## 4798 159.31357 121.63828 173.05755 106.95611 134.36864
## 4799 143.27033 136.11654 120.94736 137.91349 138.41941
## 4800 1164.81025 144.49301 128.48153 107.75534 135.65222
## 4851 86.87865 39.70148 83.06365 88.74066 81.13119
## 4852 94.96854 74.49210 94.37539 87.96539 80.76570
## 4853 104.95791 84.15973 60.45340 57.46010 86.72466
## 4854 68.72902 75.74804 53.58974 53.96413 51.42515
## 4855 94.38345 78.03115 86.88603 85.95374 53.58974
## 4856 94.21084 84.84667 99.33319 94.31041 91.62297
## 4857 93.82111 81.95826 81.44024 98.35328 87.31443
## 4858 92.11196 94.63581 91.80362 87.76968 97.61660
## 4859 91.29703 97.53524 95.33453 97.88507 94.08903
## 4860 101.86310 95.14710 102.44425 94.57160 101.96489
## 4861 102.77447 100.44107 98.37455 102.07249 112.35484
## 4862 101.35203 104.30985 102.62943 102.02099 106.56742
## 4863 114.50839 101.78633 103.08257 114.49640 110.32772
## 4864 103.89858 115.81716 105.58494 109.98478 110.08035
## 4865 107.88385 122.62304 108.02726 112.55576 114.70735
## 4866 118.92588 112.52256 112.72046 99.75145 132.29487
## 4867 114.64604 117.82492 135.10290 111.06092 112.50254
## 4868 108.32327 104.18835 172.70436 113.69280 105.50042
## 4869 111.97003 109.79184 115.95317 105.37942 131.13302
## 4870 116.73749 120.42606 113.56284 117.94491 482.34621
## 4871 188.77562 178.65124 123.61300 188.53177 113.59687
## 4872 120.40953 147.53804 117.03804 125.66086 100.34580
## 4873 147.99678 108.83501 182.98188 159.22729 152.26835
## 4874 136.37268 123.00804 120.99964 122.61141 155.56938
## 4875 138.72838 123.11356 135.08562 552.45355 123.65758
## 4876 87.17744 87.49543 68.86478 48.49429 80.21945
## 4877 77.26177 95.99851 65.10216 88.93484 54.27991
## 4878 74.82542 90.87033 80.42268 83.26203 85.58530
## 4901 88.74066 84.16547 87.11872 105.36680 93.96598
## 4902 54.23804 78.65690 45.30148 82.76681 90.23398
## 4903 66.58774 104.02357 80.21945 83.45336 83.11619
## 4926 77.81180 60.57741 80.88184 83.06365 87.19847
## 4927 79.47972 86.50740 84.35955 87.19847 41.91126
## 4928 55.46753 85.46386 85.73580 89.46176 85.03723
## 4929 75.69345 91.78296 86.45978 76.93336 63.88924
## 4930 70.85132 91.27376 92.92963 88.67350 92.92963
## 4931 75.36743 70.74005 95.08687 72.84820 82.74496
## 4932 100.69083 98.37955 91.10648 95.06454 82.13950
## 4933 76.71611 84.42922 93.25658 97.79130 74.58034
## 4934 90.73224 97.55989 97.88507 98.04965 91.37769
## 4935 104.82981 103.18209 99.56051 97.73434 100.11056
## 4936 96.83548 98.53717 99.09051 102.44425 98.86147
## 4937 104.10156 116.80319 103.72201 103.89341 102.12298
## 4938 111.40121 105.96919 104.26492 103.46585 105.45455
## 4939 107.37269 104.19667 116.83474 103.89858 116.12136
## 4940 122.20380 112.13793 106.69451 112.55576 110.56409
## 4941 133.41114 104.13499 105.33277 126.73526 122.20380
## 4942 107.03457 162.89176 111.20249 101.12053 111.88497
## 4943 151.51287 128.12313 110.21956 117.52451 171.79073
## 4944 105.84461 111.79653 109.88743 110.38324 131.42239
## 4945 138.90371 142.76316 131.72418 102.82229 138.61044
## 4946 118.26464 110.19345 136.66000 139.84188 129.23280
## 4947 99.75966 114.09905 127.98187 113.01538 160.95923
## 4948 233.93590 100.33670 116.51684 128.50156 118.14450
## 4949 125.83247 122.61141 122.18326 132.97959 125.86420
## 4950 125.68050 176.53174 146.68127 135.13134 104.60157
## 4951 89.29480 85.35007 90.82841 85.95653 85.57782
## 4952 69.07252 97.69924 60.67146 82.24845 86.31604
## 4953 77.81180 97.69924 97.69924 95.58136 87.02385
## 4954 86.77549 65.97222 79.00953 59.30769 70.47614
## 4955 97.01757 70.85132 61.05559 91.78669 86.46140
## 4976 59.26252 89.62937 94.96854 87.49543 87.33405
## 4977 55.46753 76.38382 104.44557 91.23674 83.11619
## 4978 89.86462 82.18981 69.00621 104.44557 77.81180
## 4979 92.69186 92.75428 90.90331 97.63935 85.01096
## 4980 71.43483 89.12743 94.78517 93.33426 70.83933
## 5001 82.38095 86.87865 79.47972 79.72385 89.29480
## 5002 56.17321 85.46386 86.31604 87.17744 85.65931
## 5003 83.06365 68.22153 86.87865 76.96728 82.37310
## 5004 73.45181 97.33569 91.70512 54.27027 91.65131
## 5005 97.33569 86.40904 64.83246 63.88924 68.72902
## 5006 93.49512 67.80351 88.21839 95.50927 95.86473
## 5007 65.86113 95.82647 92.87873 78.00297 97.40585
## 5008 92.38893 80.22518 73.79112 84.34966 95.01535
## 5009 91.23322 97.73416 102.69306 97.18961 99.37149
## 5010 100.04108 101.82777 97.49152 101.72575 99.42365
## 5011 98.53717 99.71523 100.05960 99.62979 101.86310
## 5012 102.40672 111.49880 102.00745 101.30346 110.47760
## 5013 103.88052 106.69773 107.63745 103.69813 104.99403
## 5014 106.15365 107.37269 103.29765 104.63845 105.58494
## 5015 105.35619 112.72938 155.66885 112.52256 114.60203
## 5016 108.14570 110.34835 119.60328 107.45388 128.58474
## 5017 114.64604 120.80224 108.45682 108.93434 115.60556
## 5018 143.61967 123.02067 104.05528 110.21956 107.68255
## 5019 111.98145 104.77194 117.88098 136.74067 116.73749
## 5020 137.21213 123.39230 116.03016 112.23080 120.42606
## 5021 178.65124 112.72984 114.46560 119.21853 125.15530
## 5022 169.39628 120.26897 118.92416 121.04988 112.95776
## 5023 125.59274 115.92970 131.22664 126.67551 322.99700
## 5024 215.03420 155.69308 254.51475 180.75454 138.41941
## 5025 145.45282 144.07929 121.48754 191.44475 146.90039
## 5076 60.67146 89.67846 89.92114 41.91126 88.97681
## 5077 60.03765 90.62159 89.20607 89.62937 87.19251
## 5078 97.73589 41.91126 95.99851 87.24826 85.35007
## 5079 79.07915 53.67943 95.73724 88.66636 86.88603
## 5080 78.03115 89.16226 79.07915 85.38321 80.66573
## 5081 57.28155 95.14822 87.88031 88.04751 97.99241
## 5082 93.81893 89.08083 96.92728 94.20244 92.75237
## 5083 93.54729 92.71862 86.59472 99.46346 80.22518
## 5084 89.63739 92.48857 97.91838 97.29414 94.88037
## 5085 99.93156 101.00116 100.06442 101.90641 99.41273
## 5086 100.82659 99.63279 99.73544 95.14710 100.34017
## 5087 112.73950 106.43074 143.70360 102.23617 124.71287
## 5088 110.07958 103.62028 109.78781 104.85359 102.92240
## 5089 116.83474 116.12136 141.25711 123.98612 140.53637
## 5090 112.47647 118.32347 103.79710 117.88453 114.52303
## 5091 120.56460 118.92588 103.79710 105.35619 143.88818
## 5092 108.81487 108.26279 108.26279 125.96739 105.33872
## 5093 113.96239 104.83339 115.60556 103.62725 130.97942
## 5094 110.11771 117.88098 106.32642 119.33927 109.22285
## 5095 263.32021 252.84839 134.44921 111.97003 110.80547
## 5096 119.46401 116.11264 161.96111 109.61199 159.63346
## 5097 134.16978 193.74717 165.45022 147.53804 137.90285
## 5098 132.05508 109.17176 147.99678 126.98589 128.50156
## 5099 183.04556 122.61141 153.54877 110.00582 117.63546
## 5100 141.84511 124.18577 134.47319 117.74875 124.63820
## 5101 79.54693 87.90980 41.56536 86.93202 86.84609
## 5102 58.84880 104.44557 90.82841 85.57782 91.29758
## 5103 77.73452 84.05367 80.07062 84.43896 87.17744
## 5104 91.87575 91.62935 95.78239 81.19130 53.67943
## 5105 85.47067 91.87575 93.17554 51.42515 72.67259
## 5106 90.45155 95.06403 84.00430 75.51250 95.75119
## 5107 85.35279 89.32484 93.55041 96.35391 85.98439
## 5116 99.29293 154.11132 103.07126 108.04976 102.88066
## 5117 100.39817 105.36204 114.85910 111.63016 108.18351
## 5118 100.65657 108.01316 108.18351 134.81898 105.34240
## 5119 146.06715 132.04917 122.56407 108.26185 105.41838
## 5120 132.42378 155.31320 120.42606 105.65981 141.37796
## 5121 145.10697 106.35650 123.61300 113.31907 280.27231
## 5122 169.39628 112.23078 101.07600 117.18814 318.79224
## 5123 205.16297 124.58662 641.17255 152.11439 126.42586
## 5124 180.95638 121.95838 210.71568 144.24659 123.01758
## 5125 146.34694 159.70753 162.00465 136.81357 141.84511
## 5126 87.22372 57.46010 85.95653 58.63290 90.98065
## 5127 83.11619 76.54764 60.03765 86.30683 93.92426
## 5128 84.43896 60.65947 84.35955 82.38095 85.54361
## 5129 76.96681 62.53634 65.48751 90.14350 68.71703
## 5130 87.79523 79.87391 88.95131 86.88603 78.03115
## 5131 87.76968 68.83437 99.01804 73.98322 70.82698
## 5132 94.80933 93.20396 91.62297 95.52910 77.10697
## 5141 327.17640 112.13793 126.87452 107.31383 110.52339
## 5142 101.13796 113.17658 116.75936 106.04627 111.63016
## 5143 112.63187 126.70203 143.61967 184.33610 101.05646
## 5144 106.49688 116.03016 103.24642 105.65981 123.60592
## 5145 120.43349 117.22208 105.72636 110.11968 116.29395
## 5146 114.46560 119.46401 115.16107 141.39107 102.82315
## 5147 133.75443 118.87613 199.02722 118.87613 120.40953
## 5148 121.85682 642.56618 134.68528 123.20902 472.19814
## 5149 110.81273 133.97964 110.81273 126.91669 103.33840
## 5150 158.36933 149.47301 125.56559 123.52662 190.33094
## 5151 69.00621 85.57589 87.06867 39.70148 79.47932
## 5152 60.65947 74.82542 94.96854 87.17744 80.42268
## 5153 39.64751 37.81895 92.38744 91.29758 66.58774
## 5154 97.31578 90.14350 65.48751 63.37863 76.96681
## 5155 68.71703 92.14181 75.69345 51.50991 94.78517
## 5156 85.46920 85.25325 88.75876 84.34966 94.16257
## 5157 84.84667 94.80933 87.31443 91.72060 93.56191
## 5158 92.67817 99.88840 76.31949 92.07572 87.88031
## 5159 92.48857 100.79177 99.11068 88.01447 97.45069
## 5160 100.04108 97.27377 97.15385 102.66218 99.01178
## 5161 99.42365 101.52984 99.09979 100.35603 100.31855
## 5162 105.56488 143.70360 105.05959 106.56742 107.55417
## 5163 103.83656 110.10368 104.11632 114.49640 110.32772
## 5164 103.35298 115.52511 108.05007 109.15415 106.79215
## 5165 117.47672 107.46319 109.40839 155.66885 105.72252
## 5166 105.18351 106.12299 152.66391 119.50518 107.07546
## 5167 107.03457 114.09806 166.15643 171.60518 104.99610
## 5168 112.22418 163.02863 98.68517 100.39817 107.12818
## 5169 136.42447 106.32642 149.89209 110.82173 155.76773
## 5170 163.68244 149.15188 119.67466 108.04980 113.42653
## 5171 110.44112 106.07573 252.64430 117.71484 141.39107
## 5172 149.56982 298.75559 160.95923 117.87264 118.87613
## 5173 107.00071 131.25519 107.00071 641.17255 118.14756
## 5174 131.79459 110.00582 116.65668 107.63601 210.95186
## 5175 141.93627 159.64830 159.70753 144.07929 125.10588#summary of the value
summary(inflation_annual_data$value)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 36.79 95.91 105.52 116.95 120.43 1164.81 2182# Extracting the mean of the original data
original_mean <- 116.95
# Compute the mean of each imputed column
imputed_means <- colMeans(annual_imputation_cart$imp$value, na.rm = TRUE)
# Calculate absolute differences from the original mean
mean_differences <- abs(imputed_means - original_mean)
# Identify the column index with the smallest difference
closest_column <- which.min(mean_differences)
# Display the selected column
cat("The column closest to the original mean is column:", closest_column, "\n")## The column closest to the original mean is column: 1#impute the data into the original dataset
inflation_annual_data_imp <- complete(annual_imputation_cart, 1)# Summary of missing vs imputed data
summary(inflation_annual_data$value) # Original## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 36.79 95.91 105.52 116.95 120.43 1164.81 2182summary(inflation_annual_data_imp$value) # Imputed## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 36.79 92.40 103.99 115.02 118.64 1164.81The imputed data retains a similar range as the original dataset, suggesting the imputation did not introduce unrealistic values. Slight changes in the summary statistics indicate that imputed values reflect underlying patterns but might differ slightly from the original distribution
# Remove excess commas on series colomn
inflation_annual_data_imp <- inflation_annual_data_imp %>%
mutate(series = str_replace_all(series, ",,,", ""))Which continents exhibit the highest or lowest inflation rates for the different indicators?
# Compute summary statistics
annual_inflation_summary <- inflation_annual_data_imp %>%
group_by(continent, series) %>%
summarise(
mean_inflation = mean(value),
median_inflation = median(value),
min_inflation = min(value),
max_inflation = max(value)
) %>%
arrange(series, mean_inflation)
print(annual_inflation_summary)## # A tibble: 18 × 6
## # Groups: continent [6]
## continent series mean_inflation median_inflation min_inflation max_inflation
## <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Oceania CPI P… 108. 106. 76.5 183.
## 2 South Ame… CPI P… 110. 104. 39.6 254.
## 3 North Ame… CPI P… 110. 105. 50.0 643.
## 4 Africa CPI P… 116. 103. 41.6 694.
## 5 Europe CPI P… 118. 104. 36.8 1165.
## 6 Asia CPI P… 119. 104. 45.1 694.
## 7 Oceania Core … 106. 110. 70.4 129.
## 8 North Ame… Core … 107. 105. 36.8 204.
## 9 Europe Core … 111. 103. 37.6 694.
## 10 Asia Core … 117. 104. 58.6 1165.
## 11 South Ame… Core … 117. 108. 41.6 327.
## 12 Africa Core … 142. 116. 37.8 562.
## 13 North Ame… Core … 107. 105. 36.8 203.
## 14 Europe Core … 110. 103. 41.9 694.
## 15 Asia Core … 115. 103. 36.8 1159.
## 16 South Ame… Core … 117. 109. 52.6 327.
## 17 Africa Core … 141. 116. 37.6 559.
## 18 Oceania Core … 145. 102. 51.5 1165.#Distribution of Inflation Values
ggplot(inflation_annual_data_imp, aes(x = value)) +
geom_histogram(bins = 30, fill = "blue", color = "white") +
labs(title = "Distribution of Inflation Rates", x = "Inflation Value", y = "Frequency")
#the inflation over time by the continent
ggplot(inflation_annual_data_imp, aes(x = year, y = value, color = continent)) +
geom_line() +
labs(title = "Inflation Over Time by Continent", x = "Year", y = "Inflation Value") +
theme_minimal()
Inflation values for most continents were relatively stable and low from
2000 to around 2010. After 2010, there is a gradual rise in inflation
values for all continents, with significant spikes in the later years
(especially post-2020).
A substantial increase in inflation is observed across all continents, particularly from 2020 to 2024. This could be attributed to global events like the COVID-19 pandemic, supply chain disruptions, or economic recovery policies leading to inflationary pressures.
Africa and South America: Both regions exhibit higher variability in inflation values over time compared to other continents, South America in particular, shows significant spikes in inflation.
Asia and Europe: These regions display a more controlled inflation trend with lower spikes relative to others.
North America: Displays moderate inflation but also some increase in variability post-2020.
Oceania: Notable for having a sharp spike in the 2020–2024 period, likely contributing to the highest inflation value in the dataset (~1200).
Inflation variability increases substantially after 2020 for most continents, suggesting a period of economic uncertainty or heightened inflationary pressures. The spikes and spread indicate that some continents experienced extreme inflation events during this time.
#boxplot for inflation and continent
ggplot(inflation_annual_data_imp, aes(x = continent, y = value, fill = continent)) +
geom_boxplot() +
labs(title = "Inflation Distribution by Continent", x = "Continent", y = "Inflation Value") +
theme_minimal()
we can see that continets like, Asia, Europe and North America has outliers..(thats where the max of 1164 values may come from )
#inflation grouped by decade and continent
inflation_annual_data_imp %>%
group_by(decade, continent) %>%
summarise(mean_inflation = mean(value, na.rm = TRUE), .groups = "drop") %>%
ggplot(aes(x = decade, y = mean_inflation, fill = continent)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "Average Inflation by Decade and Continent", x = "Decade", y = "Average Inflation") +
theme_minimal()
How have inflation indicators evolved globally and within continents over time?
#aggregate data by year and continent
inflation_trends <- inflation_annual_data_imp %>%
group_by(year, continent, series) %>%
summarise(mean_inflation = mean(value, na.rm = TRUE), .groups = "drop") %>%
ungroup()#plot trends globally
ggplot(inflation_trends, aes(x = year, y = mean_inflation, colour = series)) +
geom_line() +
facet_wrap(~ continent, scales = "free_y") +
labs(title = "Inflation Trends By Continent",
x = "Year",
y = "Mean Inflation Rate",
color = "Inflation Indicator") +
theme_minimal()
Core CPI, Not Seasonally Adjusted (Red Line):
Measures inflation based on the core consumer price index without adjusting for seasonal variations
The red line generally tracks closely with the green line (seasonally adjusted CPI), indicating that seasonal effects might not be substantial across most regions.
Africa and South America: Show gradual increases until around 2020, followed by steep rises.Africa exceeds 300, and South America approaches 180 by 2024.
North America and Oceania: Sharp surges post-2020, with North America nearing 1000 and Oceania surpassing 1200. Asia and Europe: Display smoother trends with more gradual increases, staying below 250 and 200, respectively.
Core CPI, Seasonally Adjusted (Green Line):
Similar to the core CPI, but accounts for predictable seasonal patterns like holiday spending or harvest cycles
The green line mostly mirrors the red line in shape but is sometimes slightly higher or lower, reflecting adjustments for seasonal effects.
Africa, Asia, and Europe: The seasonal adjustments have a minimal impact on the trends, as the green and red lines are nearly identical. Seasonal effects do not seem to drive inflation significantly in these continents.
North America and Oceania: Differences between the green and red lines are more pronounced post-2020, indicating stronger seasonal effects in these regions during recent years.
South America: A higher green line suggests that seasonal adjustments slightly amplify inflation, possibly due to economic cycles tied to agriculture or holidays.
The Core CPI (not seasonally adjusted and seasonally adjusted)
shows similar trends across most continents, with minimal seasonal effects except for North America and Oceania post-2020.
The CPI Price (median-weighted, seasonally adjusted) diverges more, reflecting stronger volatility in year-over-year inflation changes, particularly in Oceania, North America, and South America.
Oceania and North America stand out for their extreme post-2020 inflation increases, while Asia and Europe show more stable and controlled trends.
Are there distinct patterns or differences in inflation trends between continents?
# Plot trends across continents
ggplot(inflation_trends, aes(x = year, y = mean_inflation, color = continent)) +
geom_line() +
facet_wrap(~ series, scales = "free_y") +
labs(
title = "Inflation Trends by Indicator and Continent",
x = "Year",
y = "Mean Inflation Rate",
color = "Continent"
) +
theme_minimal()
#barplot
ggplot(annual_inflation_summary, aes(x = reorder(continent, mean_inflation), y = mean_inflation, fill = series)) +
geom_bar(stat = "identity", position = "dodge") +
labs(
title = "Mean Inflation Rate by Continent and Indicator",
x = "Continent",
y = "Mean Inflation Rate",
fill = "Indicator"
) +
theme_minimal()
How do Core CPI (not seasonally adjusted) and Core CPI (seasonally adjusted) compare over time?
# Subset data for Core CPI comparisons
core_cpi_data <- inflation_annual_data_imp %>%
filter(series %in% c("Core CPI,not seas.adj", "Core CPI,seas.adj"))# Plot comparison globally
ggplot(core_cpi_data, aes(x = year, y = value, color = series)) +
geom_line() +
facet_wrap(~ continent, scales = "free_y") +
labs(
title = "Comparison of Core CPI (Seasonally Adjusted vs. Not Adjusted)",
x = "Year",
y = "Inflation Rate",
color = "Indicator"
) +
theme_minimal()
Global Correlation
# Calculate correlation globally
global_comparison <- core_cpi_data %>%
group_by(year, series) %>%
summarise(mean_value = mean(value, na.rm = TRUE)) %>%
spread(series, mean_value)
correlation <- cor(global_comparison$`Core CPI,not seas.adj`, global_comparison$`Core CPI,seas.adj`, use = "complete.obs")
print(paste("Correlation between seasonally adjusted and not adjusted Core CPI:", round(correlation, 2)))## [1] "Correlation between seasonally adjusted and not adjusted Core CPI: 0.99"A correlation of 0.99 suggests a very strong positive linear relationship between the two variables. When one variable increases, the other almost always increases by a proportional amount
Is the inflation rate becoming more or less volatile over time in specific continents?
# Compute rolling standard deviation (volatility)
inflation_volatility <- inflation_annual_data_imp %>%
group_by(continent, series) %>%
arrange(year, .by_group = TRUE) %>%
mutate(
rolling_sd = rollapply(value, width = 5, FUN = sd, fill = NA, align = "right"),
rolling_sd = ifelse(is.na(rolling_sd), sd(value, na.rm = TRUE), rolling_sd) # Replace NA
)# Plot volatility trends
ggplot(inflation_volatility, aes(x = year, y = rolling_sd, color = continent)) +
geom_line() +
facet_wrap(~ series, scales = "free_y") +
labs(
title = "Rolling Volatility (5-year SD) of Inflation Indicators",
x = "Year",
y = "Volatility (SD)",
color = "Continent"
) +
theme_minimal()
What are the major volatility patterns in CPI Price % y-o-y across continents?
# Subset data for CPI Price % y-o-y
cpi_volatility <- inflation_volatility %>%
filter(series == "CPI Price, % y-o-y, median weighted, seas. adj.,")
# Plot CPI volatility trends
ggplot(cpi_volatility, aes(x = year, y = rolling_sd, color = continent)) +
geom_line() +
labs(
title = "Volatility Trends for CPI Price % y-o-y (5-year Rolling SD)",
x = "Year",
y = "Volatility (SD)",
color = "Continent"
) +
theme_minimal()
Africa (Red Line): Volatility is moderate but rises significantly after 2020, reaching levels above 100 by 2024. Suggests a reaction to external shocks (e.g., global economic crises, supply chain disruptions).
Asia (Yellow Line): Maintains relatively low volatility until 2020, after which there is a notable increase, peaking around 2024. This rise, while less extreme than in other continents, reflects the growing instability in global markets impacting the region.
Europe (Green Line): A sharp and dramatic increase in volatility starting around 2015, with fluctuations exceeding 300 SD post-2020. Reflects significant external shocks like the COVID-19 pandemic, geopolitical tensions, or energy crises disproportionately affecting Europe.
North America (Blue Line): Volatility remains low until 2020, followed by a steep increase. Peaks around 400 SD, the highest among all continents. Indicates major inflationary instability during recent years, potentially driven by supply chain issues, monetary policy adjustments, and global disruptions.
Oceania (Light Blue Line): Experiences the most extreme volatility, especially post-2020, with levels exceeding 400 SD by 2024. Reflects small regional economies’ sensitivity to global shocks and resource price fluctuations.
South America (Pink Line): Volatility trends upward over time but remains relatively moderate compared to other continents, staying below 100 SD even post-2020. Suggests that inflation variability is less volatile but still impacted by regional and global trends.
Post-2020 Spike: Across all continents, volatility increases sharply after 2020, driven by global disruptions like the COVID-19 pandemic, supply chain breakdowns, and geopolitical tensions.
High Volatility in Developed Regions: North America and Oceania experience the highest volatility post-2020, suggesting that developed economies are more sensitive to recent shocks.
Moderate Volatility in Developing Regions: Africa, Asia, and South America show more contained volatility increases, reflecting their slower integration into global economic cycles or more persistent inflationary trends.
Europe’s Unique Pattern: Europe exhibits early increases in volatility (starting ~2015) before a significant rise post-2020, potentially tied to unique factors like the Eurozone debt crisis or energy dependencies.
Rolling standard deviation highlights dramatic shifts in inflation volatility, especially in North America, Oceania, and Europe, post-2020
# Examine decade-level patterns
inflation_annual_data_imp <- inflation_annual_data_imp %>%
mutate(decade = floor(year / 10) * 10)
ggplot(inflation_annual_data_imp, aes(x = factor(decade), y = value, fill = continent)) +
geom_boxplot() +
facet_wrap(~ series, scales = "free_y") +
labs(
title = "Decade-level Inflation Patterns by Indicator and Continent",
x = "Decade",
y = "Inflation Rate",
fill = "Continent"
) +
theme_minimal()
Core CPI, Not Seasonally Adjusted
2000–2010:
Inflation rates are relatively stable across all continents, with few outliers. Rates are lower in Europe and North America compared to Africa and South America.
2010–2020:
A slight upward shift in inflation rates occurs across all continents, with South America showing higher medians and wider spreads. Africa and Asia begin to show greater variability compared to other continents.
2020–2025:
Significant increases in inflation rates and variability are observed across all continents. Oceania and North America display extreme outliers, with values exceeding 1000, indicating sporadic and highly volatile inflation events.
Core CPI, Seasonally Adjusted
2000–2010:
Similar patterns to the non-seasonally adjusted indicator, with low and stable inflation rates across continents. Europe and North America have the lowest rates and smallest spreads.
2010–2020:
A moderate increase in inflation rates across most continents. Africa and South America show slightly higher medians and variability compared to other continents.
2020–2025:
Sharp increases in inflation rates across all continents, with notable outliers in North America and Oceania. South America continues to show a wide spread, highlighting variability in inflation trends.
Year-over-Year CPI (Median Weighted)
2000–2010:
Inflation rates remain low and stable across all continents, with minimal spread and very few outliers.
2010–2020:
Gradual increases in median inflation rates, especially in Africa, Asia, and South America. Variability remains moderate compared to Core CPI indicators.
2020–2025:
A significant rise in inflation rates across all continents, with extreme outliers present in North America, Oceania, and Europe. Africa and Asia exhibit moderate spreads but show noticeable increases in inflation medians.
TIME SERIES ANALYSIS
create iflation time series for CPI Price, % y-o-y, median weighted
inflation_time_series <- inflation_annual_data_imp %>%
filter(series == "CPI Price, % y-o-y, median weighted, seas. adj.,") %>%
select(year,value, series,country,decade, continent) # Plot the time series
ggplot(inflation_time_series, aes(x = year, y = value)) +
geom_line(color = "blue", size = 0.5) +
geom_point(color = "red", size = 1) +
labs(
title = "Seasonally Adjusted Inflation Time Series ",
x = "Year",
y = "Inflation Rate (% y-o-y)"
) +
theme_minimal() 
ggplot(inflation_time_series, aes(x = year, y = value, color = continent)) +
geom_line(size = 0.2) +
labs(
title = "Seasonally Adjusted Inflation Time Series by continent",
x = "Year",
y = "Inflation Rate (% y-o-y)"
) +
theme_minimal()
check stationary of the series
adf_inflation <- inflation_time_series %>%
group_by(series, continent) %>%
summarise(
adf_p_value = tryCatch(
adf.test(na.omit(value))$p.value,
error = function(e) NA # Return NA if ADF test fails
),
.groups = "drop" # Drop grouping after summarise
)
print(adf_inflation)## # A tibble: 6 × 3
## series continent adf_p_value
## <chr> <chr> <dbl>
## 1 CPI Price, % y-o-y, median weighted, seas. adj., Africa 0.01
## 2 CPI Price, % y-o-y, median weighted, seas. adj., Asia 0.01
## 3 CPI Price, % y-o-y, median weighted, seas. adj., Europe 0.01
## 4 CPI Price, % y-o-y, median weighted, seas. adj., North America 0.01
## 5 CPI Price, % y-o-y, median weighted, seas. adj., Oceania 0.180
## 6 CPI Price, % y-o-y, median weighted, seas. adj., South America 0.01#visualize the stationary results
ggplot(adf_inflation, aes(x = continent, y = adf_p_value, fill = series)) +
geom_bar(stat = "identity", position = "dodge") +
labs(title = "ADF Test Results by Series and Continent",
x = "Continent", y = "P-value") +
theme_minimal()
# Interpret stationarity
adf_inflation<- adf_inflation%>%
mutate(
stationarity = ifelse(adf_p_value < 0.05, "Stationary", "Non-stationary")
)
print(adf_inflation)## # A tibble: 6 × 4
## series continent adf_p_value stationarity
## <chr> <chr> <dbl> <chr>
## 1 CPI Price, % y-o-y, median weighted, seas.… Africa 0.01 Stationary
## 2 CPI Price, % y-o-y, median weighted, seas.… Asia 0.01 Stationary
## 3 CPI Price, % y-o-y, median weighted, seas.… Europe 0.01 Stationary
## 4 CPI Price, % y-o-y, median weighted, seas.… North Am… 0.01 Stationary
## 5 CPI Price, % y-o-y, median weighted, seas.… Oceania 0.180 Non-station…
## 6 CPI Price, % y-o-y, median weighted, seas.… South Am… 0.01 StationaryAfrica, Asia, Europe, North America, and South America have p-values of 0.01, indicating stationarity.
Oceania has a p-value of 0.18, suggesting the series is non-stationary and requires transformation (e.g., differencing) to achieve stationarity.
difference Oceania with ove 0.05 p value
oceania_data <- inflation_time_series %>%
filter(continent == "Oceania")
#Extract the 'value' column and apply differencing
oceania_values <- oceania_data$value
# First-order differencing
diff_oceania_values <- diff(oceania_values)
#the ADF test to the differenced series
oceania_adf_diff <- adf.test(diff_oceania_values)
# Print the results
print(oceania_adf_diff)##
## Augmented Dickey-Fuller Test
##
## data: diff_oceania_values
## Dickey-Fuller = -4.2319, Lag order = 2, p-value = 0.01517
## alternative hypothesis: stationarythe differencing worked now Oceana has a p-value of less than 0.05
# Update the Oceania row with the differenced series results
adf_inflation <- adf_inflation %>%
mutate(
adf_p_value = ifelse(continent == "Oceania", 0.02, adf_p_value),
stationarity = ifelse(adf_p_value < 0.05, "Stationary", "Non-stationary")
)
print(adf_inflation)## # A tibble: 6 × 4
## series continent adf_p_value stationarity
## <chr> <chr> <dbl> <chr>
## 1 CPI Price, % y-o-y, median weighted, seas.… Africa 0.01 Stationary
## 2 CPI Price, % y-o-y, median weighted, seas.… Asia 0.01 Stationary
## 3 CPI Price, % y-o-y, median weighted, seas.… Europe 0.01 Stationary
## 4 CPI Price, % y-o-y, median weighted, seas.… North Am… 0.01 Stationary
## 5 CPI Price, % y-o-y, median weighted, seas.… Oceania 0.02 Stationary
## 6 CPI Price, % y-o-y, median weighted, seas.… South Am… 0.01 StationaryTrend Analysis for Global CPI Price % y-o-y
global_cpi <- inflation_time_series %>%
group_by(year) %>%
summarise(global_value = mean(value, na.rm = TRUE))
# Fit linear and quadratic models
linear_model <- lm(global_value ~ year, data = global_cpi)
quadratic_model <- lm(global_value ~ poly(year, 2), data = global_cpi)
# Visualize trends with updated `linewidth` aesthetic
ggplot(global_cpi, aes(x = year, y = global_value)) +
geom_line(color = "blue", linewidth = 1) + # Replace `size` with `linewidth`
geom_smooth(method = "lm", formula = y ~ x, se = FALSE, color = "red", linetype = "dashed") +
geom_smooth(method = "lm", formula = y ~ poly(x, 2), se = FALSE, color = "green", linetype = "dotted") +
labs(
title = "Trend Analysis of Global CPI Price % y-o-y",
x = "Year",
y = "CPI Inflation Rate"
) +
theme_minimal()
Observed Global CPI Inflation Trend (Blue Line)
The global CPI inflation rate shows an overall upward trend from 2000 to 2025, with notable acceleration in recent years (2020 onward). Key inflection points occur around 2008 (global financial crisis), 2015 (oil price shocks), and post-2020 (COVID-19 pandemic, supply chain issues, and geopolitical tensions).
Volatility and Nonlinearity: The blue line demonstrates clear nonlinear behavior, with periods of relatively stable growth (2000–2008) followed by sharp increases and fluctuations. Significant inflation surges are visible after 2020, likely due to pandemic-related disruptions and geopolitical shocks like the Russia-Ukraine war.
Linear Model (Red Dashed Line)
The linear trend captures the general upward movement in inflation over time but fails to account for nonlinear fluctuations (e.g., sharp increases from 2019). It underestimates inflation during the rapid surges post-2020, as seen by the divergence from the blue line.
While the linear model provides a simple approximation, it is insufficient for capturing the complexities of global inflation dynamics, especially during periods of volatility.
Quadratic Model (Green Dotted Line)
The quadratic model fits the observed data more closely than the linear model, especially in capturing the nonlinear upward acceleration after 2020. It predicts a steeper inflation trajectory in recent years, aligning better with the observed trend.
The quadratic model reflects an accelerating inflation trend, indicating that global inflation is not just rising but doing so at an increasing rate. This suggests that external shocks or structural changes in the global economy are driving more pronounced inflationary pressures over time.
Prepare time series for CPI Price % y-o-y (global)
# Aggregate data to compute global CPI by year
global_cpi_agg <- global_cpi %>%
group_by(year) %>%
summarise(global_value = mean(global_value, na.rm = TRUE))
cpi_ts <- ts(global_cpi_agg$global_value, start = min(global_cpi_agg$year), frequency = 1)
print(cpi_ts)## Time Series:
## Start = 2000
## End = 2024
## Frequency = 1
## [1] 79.20431 77.05048 78.13900 78.92403 83.16288 88.65038 89.60080
## [8] 90.82867 95.49767 100.18082 100.00293 104.67266 107.06267 111.83918
## [15] 111.75392 122.36993 142.18807 120.44484 130.72968 134.39828 141.32840
## [22] 144.26777 156.37021 191.71717 217.96528# Calculate a 3-year rolling average
global_cpi_agg$rolling_avg <- rollmean(global_cpi_agg$global_value, k = 3, fill = NA)
# Plot original data and rolling average
ggplot(global_cpi_agg, aes(x = year)) +
geom_line(aes(y = global_value, color = "Original Data")) +
geom_line(aes(y = rolling_avg, color = "3-Year Rolling Average"), size = 1) +
labs(
title = "Global CPI Inflation with Rolling Average",
x = "Year",
y = "CPI Inflation Rate",
color = "Legend"
) +
theme_minimal()
Trend Stabilization: The rolling average reveals a long-term accelerating trend in inflation, particularly post-2020, without being overly influenced by individual spikes or dips in the data.
Volatility Mitigation: By smoothing out short-term volatility, the rolling average highlights the overall trajectory of inflation growth, reducing noise from one-off events.
Lag Effect: The rolling average slightly lags behind the original data due to its calculation method (average over a 3-year window), which can delay the reflection of sudden changes in inflation.
#use the left join to join the other variable to the global cpi data
global_cpi_agg <- global_cpi_agg %>%
left_join(inflation_time_series %>% select(year, value, decade, continent, series), by = "year")use the ARIMA MODEL to analyse the Global CPI Price % Y-O-Y
#which model will be the best fit
inflation_arima <- auto.arima(global_cpi_agg$global_value, ic = "aic", trace = TRUE)##
## Fitting models using approximations to speed things up...
##
## ARIMA(2,1,2) with drift : 6065.833
## ARIMA(0,1,0) with drift : 6055.876
## ARIMA(1,1,0) with drift : 6058.854
## ARIMA(0,1,1) with drift : 6057.857
## ARIMA(0,1,0) : 6059.565
## ARIMA(1,1,1) with drift : Inf
##
## Now re-fitting the best model(s) without approximations...
##
## ARIMA(0,1,0) with drift : 6056.552
##
## Best model: ARIMA(0,1,0) with drift# Fitthe ARIMA model (best model 0,1,0)
arima_model <- Arima(global_cpi_agg$global_value, order = c(0,1,0))
# Summary of ARIMA model
summary(arima_model)## Series: global_cpi_agg$global_value
## ARIMA(0,1,0)
##
## sigma^2 = 1.966: log likelihood = -3029.12
## AIC=6060.24 AICc=6060.24 BIC=6065.69
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.08048706 1.401864 0.1084989 0.05448345 0.07894949 0.9998434
## ACF1
## Training set -0.003307334Model Fit:
The low ME, RMSE, and MAE values indicate that the model fits the training data reasonably well, with minimal errors.
Bias and Accuracy:
The near-zero ME and MAPE suggest the model has no significant bias with no harsh errors, meaning the forecast can be accurate
Residual Independence:
The low ACF1 indicates that residuals are effectively white noise, which confirms that the model captures all patterns in the data.
#Check Residual Diagnostics
checkresiduals(arima_model)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(0,1,0)
## Q* = 0.1905, df = 10, p-value = 1
##
## Model df: 0. Total lags used: 10Residual Plot (Top Panel)
Description: The residual plot displays the residuals over time.
Analysis:
The residuals appear to fluctuate randomly around zero, suggesting that the ARIMA model has captured most of the systematic patterns in the data. However, there are some spikes at specific points, which could indicate periods of unusual activity or model underfitting for specific observations.
Autocorrelation Function (ACF) Plot (Bottom-Left Panel)
Description: The ACF plot shows the autocorrelation of residuals at different lags.
Analysis:
All autocorrelations are within the blue confidence intervals, indicating that there is no significant autocorrelation in the residuals. This suggests that the ARIMA model adequately removed autocorrelation from the time series, making the residuals appear like white noise.
Histogram/Density Plot of Residuals (Bottom-Right Panel)
Description: This plot shows the distribution of residuals compared to a normal distribution.
Analysis:
The residuals appear centered around zero, with a sharp peak, but there may be some deviation from normality due to the presence of outliers or skewness.
Ljung-Box Test
Null Hypothesis: The residuals are independent (i.e., there is no autocorrelation in the residuals at any lag).
Alternative Hypothesis: The residuals exhibit autocorrelation at one or more lags
Since the p-value is significantly greater than 0.05, we fail to reject the null hypothesis. This indicates that the residuals are independent and exhibit no significant autocorrelation.
Good Fit:
The ARIMA(0,1,0) model effectively captures all patterns in the data. Residuals behaving like white noise confirm that no additional information remains unexplained.
#Forecast Future Values
inflation_forecast <- forecast(arima_model, h = 10) # Forecast for the next 10 years
#Summarize Forecast
print(inflation_forecast, n = Inf)## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## 1726 217.9653 216.1682 219.7624 215.2169 220.7137
## 1727 217.9653 215.4238 220.5067 214.0785 221.8521
## 1728 217.9653 214.8526 221.0779 213.2049 222.7256
## 1729 217.9653 214.3711 221.5594 212.4685 223.4621
## 1730 217.9653 213.9469 221.9837 211.8197 224.1109
## 1731 217.9653 213.5633 222.3672 211.2331 224.6975
## 1732 217.9653 213.2106 222.7199 210.6937 225.2369
## 1733 217.9653 212.8824 223.0482 210.1916 225.7389
## 1734 217.9653 212.5740 223.3565 209.7201 226.2105
## 1735 217.9653 212.2824 223.6482 209.2741 226.6565# Plot the forecast
autoplot(inflation_forecast) +
labs(
title = "Forecast of Global CPI Inflation",
x = "Year",
y = "CPI Inflation Rate"
) +
theme_minimal()
The ARIMA(0,1,0) model specifies a random walk with drift, meaning the value at each time step is influenced by the value of the previous time step plus some noise. It does not include autoregressive (AR) or moving average (MA) terms that might capture more complex temporal patterns. As a result, the model assumes that the global CPI inflation rate will increase at a relatively constant rate (drift) and predicts a single point forecast for each year.
The model doesn’t account for seasonal variations (like ARIMA with seasonal components). Hence, it forecasts one general value per time point (year).
Since the drift is minimal, the forecast remains almost constant across the years.
Point Forecasts:
The predicted global CPI inflation rate for the next 10 years is consistently 217.9653 across all years. This reflects the model’s assumption of no additional variability or trends beyond the first difference.
Uncertainty:
While the point forecasts remain the same, the prediction intervals (e.g., Lo 80/Hi 80, Lo 95/Hi 95) grow wider as we move further into the future. This happens because forecasting uncertainty compounds over time, especially in a random walk model.
Stable Trends:
The model suggests a stabilized trend in inflation without drastic changes, which aligns with the structure of the ARIMA(0,1,0) model.
analyze and compare inflation trends across continents
#Aggregate Data by Continent and Year
continent_cpi <- inflation_time_series %>%
group_by(continent, year) %>%
summarise(continent_value = mean(value, na.rm = TRUE), .groups = "drop")#Create Time Series for Each Continent
continent_ts <- continent_cpi %>%
group_by(continent) %>%
summarise(ts_data = list(ts(continent_value, start = min(year), frequency = 1)))#Fit ARIMA Models for Each Continent
arima_continent <- continent_ts %>%
mutate(
arima_model = map(ts_data, ~ auto.arima(.x)),
arima_forecast = map(arima_model, ~ forecast(.x, h = 10))
)
print(arima_continent)## # A tibble: 6 × 4
## continent ts_data arima_model arima_forecast
## <chr> <list> <list> <list>
## 1 Africa <ts [25]> <fr_ARIMA> <forecast>
## 2 Asia <ts [25]> <fr_ARIMA> <forecast>
## 3 Europe <ts [25]> <fr_ARIMA> <forecast>
## 4 North America <ts [25]> <fr_ARIMA> <forecast>
## 5 Oceania <ts [25]> <fr_ARIMA> <forecast>
## 6 South America <ts [25]> <fr_ARIMA> <forecast>#Extract Forecasts and Compare
forecast_comparison <- arima_continent %>%
select(continent, arima_forecast) %>%
mutate(
forecast_data = map(arima_forecast, ~ {
data.frame(
year = seq(max(continent_cpi$year) + 1, by = 1, length.out = 10),
Point_Forecast = .x$mean,
lower_80 = .x$lower[, 1],
upper_80 = .x$upper[, 1],
lower_95 = .x$lower[, 2],
upper_95 = .x$upper[, 2]
)
})
) %>%
unnest(cols = c(forecast_data))
print(forecast_comparison, n = Inf)## # A tibble: 60 × 8
## continent arima_forecast year Point_Forecast lower_80 upper_80 lower_95
## <chr> <list> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Africa <forecast> 2025 315. 268. 362. 243.
## 2 Africa <forecast> 2026 315. 248. 381. 213.
## 3 Africa <forecast> 2027 315. 233. 396. 190.
## 4 Africa <forecast> 2028 315. 221. 409. 171.
## 5 Africa <forecast> 2029 315. 210. 420. 154.
## 6 Africa <forecast> 2030 315. 200. 430. 139.
## 7 Africa <forecast> 2031 315. 190. 439. 125.
## 8 Africa <forecast> 2032 315. 182. 448. 111.
## 9 Africa <forecast> 2033 315. 174. 456. 99.1
## 10 Africa <forecast> 2034 315. 166. 464. 87.5
## 11 Asia <forecast> 2025 286. 254. 317. 238.
## 12 Asia <forecast> 2026 294. 250. 338. 226.
## 13 Asia <forecast> 2027 302. 248. 356. 219.
## 14 Asia <forecast> 2028 311. 248. 373. 215.
## 15 Asia <forecast> 2029 319. 249. 389. 212.
## 16 Asia <forecast> 2030 327. 250. 404. 210.
## 17 Asia <forecast> 2031 335. 253. 418. 209.
## 18 Asia <forecast> 2032 344. 255. 432. 208.
## 19 Asia <forecast> 2033 352. 258. 446. 208.
## 20 Asia <forecast> 2034 360. 261. 459. 209.
## 21 Europe <forecast> 2025 208. 181. 234. 167.
## 22 Europe <forecast> 2026 213. 184. 242. 168.
## 23 Europe <forecast> 2027 219. 187. 251. 170.
## 24 Europe <forecast> 2028 224. 190. 258. 171.
## 25 Europe <forecast> 2029 229. 193. 266. 173.
## 26 Europe <forecast> 2030 235. 196. 274. 175.
## 27 Europe <forecast> 2031 240. 199. 281. 178.
## 28 Europe <forecast> 2032 245. 203. 288. 180.
## 29 Europe <forecast> 2033 251. 206. 296. 183.
## 30 Europe <forecast> 2034 256. 210. 303. 185.
## 31 North America <forecast> 2025 196. 178. 213. 169.
## 32 North America <forecast> 2026 153. 136. 170. 127.
## 33 North America <forecast> 2027 164. 147. 181. 138.
## 34 North America <forecast> 2028 198. 175. 220. 164.
## 35 North America <forecast> 2029 167. 145. 189. 133.
## 36 North America <forecast> 2030 178. 156. 201. 144.
## 37 North America <forecast> 2031 202. 176. 228. 163.
## 38 North America <forecast> 2032 180. 155. 206. 141.
## 39 North America <forecast> 2033 191. 165. 217. 151.
## 40 North America <forecast> 2034 208. 180. 236. 165.
## 41 Oceania <forecast> 2025 134. 113. 155. 102.
## 42 Oceania <forecast> 2026 135. 114. 157. 102.
## 43 Oceania <forecast> 2027 134. 107. 162. 92.2
## 44 Oceania <forecast> 2028 135. 106. 164. 91.0
## 45 Oceania <forecast> 2029 134. 102. 167. 85.0
## 46 Oceania <forecast> 2030 135. 101. 169. 83.0
## 47 Oceania <forecast> 2031 135. 98.1 171. 78.8
## 48 Oceania <forecast> 2032 135. 96.6 173. 76.4
## 49 Oceania <forecast> 2033 135. 94.4 175. 73.0
## 50 Oceania <forecast> 2034 135. 92.8 177. 70.7
## 51 South America <forecast> 2025 167. 149. 185. 139.
## 52 South America <forecast> 2026 149. 130. 168. 120.
## 53 South America <forecast> 2027 166. 143. 190. 130.
## 54 South America <forecast> 2028 160. 135. 185. 121.
## 55 South America <forecast> 2029 170. 141. 198. 127.
## 56 South America <forecast> 2030 168. 139. 198. 123.
## 57 South America <forecast> 2031 174. 143. 206. 126.
## 58 South America <forecast> 2032 176. 142. 209. 124.
## 59 South America <forecast> 2033 180. 145. 215. 126.
## 60 South America <forecast> 2034 182. 145. 219. 126.
## # ℹ 1 more variable: upper_95 <dbl>Africa:
Point Forecasts: Inflation values are relatively high, stabilizing around 315 CPI throughout the forecast period.
Uncertainty: The prediction intervals (e.g., lower 95% and upper 95%) show the broadest range, indicating the greatest uncertainty in Africa’s future inflation trends. This could reflect volatile historical patterns or economic instability.
Asia:
Point Forecasts: Asia shows a steady increase in inflation from 286 CPI in 2025 to 360 CPI in 2034.
Uncertainty: Moderate confidence intervals, suggesting a more predictable inflation trend compared to Africa.
Europe:
Point Forecasts: Inflation is lower in Europe compared to Africa and Asia, rising gradually from 208 CPI in 2025 to 256 CPI in 2034.
Uncertainty: The confidence intervals remain relatively narrow, reflecting stable inflation trends in Europe
North America:
Point Forecasts: North America’s inflation trends are more fluctuating, with values ranging from 196 CPI in 2025 to 208 CPI in 2034. Some declines are also observed in intermediate years.
Uncertainty: Moderate intervals, indicating some variability but less volatility compared to Africa.
Oceania:
Point Forecasts: Oceania has the lowest inflation levels, remaining nearly constant at 134-135 CPI throughout the forecast period.
Uncertainty: The narrowest prediction intervals, suggesting high confidence in the forecast.
South America:
Point Forecasts: Inflation in South America fluctuates moderately, with values ranging from 167 CPI in 2025 to 182 CPI in 2034.
Uncertainty: Intervals show moderate width, suggesting some variability in the forecasts.
# Plot forecasts for each continent
ggplot(forecast_comparison, aes(x = year, y = Point_Forecast, color = continent)) +
geom_line(size = 1) +
geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = continent), alpha = 0.2, color = NA) +
labs(
title = "Forecasted Inflation Trends by Continent",
x = "Year",
y = "Inflation CPI Value",
color = "Continent",
fill = "Continent"
) +
theme_minimal()
The shaded regions represent prediction intervals, where darker shading (central bands) corresponds to the 80% prediction interval, and lighter shading indicates the 95% prediction interval.
Africa and Asia: Have the highest inflation levels and wider prediction intervals, reflecting uncertainty and volatility in these regions.
Oceania: Has the most stable and lowest inflation trends, with little variability or growth over the forecast period.
Europe and North America: Show moderate inflation trends, with gradual increases and relatively narrow prediction intervals.
South America: Falls in between, with moderate inflation and variability.
Uncertainty (Widening Bands):
The prediction intervals widen as we move further into the future, which is typical of time series forecasts. This reflects increasing uncertainty about future trends.
Relative Comparisons:
Africa stands out as the most volatile and unpredictable region, likely driven by economic variability or external shocks.
Oceania is the most stable, with consistently low inflation levels.
Asia shows growth, suggesting higher inflationary pressures over time compared to other regions.
Check how well the forecasts align with recent historical trends for each continent.
# Merge historical and forecast data for comparison
# Aggregate historical data to yearly values
historical_data <- global_cpi_agg %>%
group_by(continent, year) %>%
summarise(value = mean(global_value, na.rm = TRUE))
# Align forecast columns
forecast_comparison_clean <- forecast_comparison %>%
select(continent, year, value = Point_Forecast)# Combine historical and forecast data
forecast_validation <- bind_rows(historical_data, forecast_comparison_clean)
# Check the combined data
sample_n(forecast_validation,10)## # A tibble: 60 × 3
## # Groups: continent [6]
## continent year value
## <chr> <dbl> <dbl>
## 1 Africa 2002 78.1
## 2 Africa 2021 144.
## 3 Africa 2034 315.
## 4 Africa 2003 78.9
## 5 Africa 2016 142.
## 6 Africa 2005 88.7
## 7 Africa 2024 218.
## 8 Africa 2007 90.8
## 9 Africa 2013 112.
## 10 Africa 2006 89.6
## # ℹ 50 more rows #Plot historical vs. forecast data
ggplot(forecast_validation, aes(x = year, y = value, color = continent)) +
geom_line(size = 1) +
labs(
title = "Historical and Forecasted CPI by Continent",
x = "Year",
y = "CPI Value",
color = "Continent"
) +
theme_minimal()
Africa
Historical Trend: Africa shows a steep increase in CPI leading up to the forecast period.
Forecast Alignment: The forecast assumes a stabilization in CPI at around 315. While this represents a leveling-off from the rapid historical growth, it may not fully account for the variability and upward trajectory evident in recent historical data.
Key Insight: The forecast might underestimate potential future increases if current economic factors driving inflation persist.
Asia
Historical Trend: Asia has experienced consistent growth in CPI over time, with a noticeable upward trend before the forecast period.
Forecast Alignment: The forecast continues this upward trajectory, projecting further increases. This aligns well with recent trends, suggesting the model effectively captures the region’s economic momentum.
Key Insight: The smooth growth in the forecast appears realistic and consistent with historical patterns.
Europe
Historical Trend: Europe shows relatively stable CPI growth with a slower, steadier increase compared to other continents.
Forecast Alignment: The forecast continues this gradual increase, maintaining the trend from historical data. This suggests a strong alignment with historical patterns.
Key Insight: The forecast reflects Europe’s stable inflationary environment and suggests continuity of current conditions.
North America
Historical Trend: North America shows moderate fluctuations in CPI, with no dramatic upward or downward trends.
Forecast Alignment: The forecast suggests a continuation of these moderate fluctuations, aligning well with historical behavior.
Key Insight: The forecast appears to account for the region’s historical stability and moderate variability.
Oceania
Historical Trend: Oceania shows consistently low and stable CPI values historically.
Forecast Alignment: The forecast maintains this stability, projecting nearly constant CPI values over time. This is well-aligned with the historical trend.
Key Insight: The region’s inflationary stability is accurately reflected in the forecast.
South America
Historical Trend: South America shows noticeable growth in CPI over the years, but with some variability.
Forecast Alignment: The forecast predicts a continuation of moderate growth with fluctuations. This aligns reasonably well with historical trends but may slightly underestimate recent volatility.
Key Insight: While the general trend is consistent, the forecast might not fully capture South America’s recent variability in inflation.
Trend Continuity: For most continents, the forecasts align with the direction and stability of recent historical trends, suggesting the models effectively capture the overall dynamics.
Potential Misalignments: Africa and South America show possible underestimations of recent variability, which could lead to less accurate projections if the underlying drivers of volatility persist.
Examine residuals (differences between actual and fitted values during model training) to check for randomness and unbiased errors.
# Extract residuals for each continent
residuals_check <- arima_continent %>%
mutate(residuals = map(arima_model, residuals)) %>%
unnest(cols = c(residuals)) %>%
rename(residual = residuals)
print(residuals_check)## # A tibble: 150 × 5
## continent ts_data arima_model arima_forecast residual
## <chr> <list> <list> <list> <dbl>
## 1 Africa <ts [25]> <fr_ARIMA> <forecast> 0.0851
## 2 Africa <ts [25]> <fr_ARIMA> <forecast> -19.8
## 3 Africa <ts [25]> <fr_ARIMA> <forecast> 16.1
## 4 Africa <ts [25]> <fr_ARIMA> <forecast> -9.27
## 5 Africa <ts [25]> <fr_ARIMA> <forecast> 8.37
## 6 Africa <ts [25]> <fr_ARIMA> <forecast> 7.91
## 7 Africa <ts [25]> <fr_ARIMA> <forecast> -7.85
## 8 Africa <ts [25]> <fr_ARIMA> <forecast> 11.9
## 9 Africa <ts [25]> <fr_ARIMA> <forecast> 2.84
## 10 Africa <ts [25]> <fr_ARIMA> <forecast> 4.13
## # ℹ 140 more rows# Plot residuals
ggplot(residuals_check, aes(x = residual)) +
geom_histogram(binwidth = 5, fill = "steelblue", color = "black") +
facet_wrap(~continent, scales = "free") +
labs(
title = "Residual Distribution by Continent",
x = "Residual",
y = "Frequency"
) +
theme_minimal()
Africa
Observation: The residuals have a wider spread, ranging from around -50 to 150, with some extreme positive outliers.
Interpretation: The model might struggle to fit African data accurately, potentially due to higher variability or extreme values in the dataset.
Asia
Observation: The residuals are mostly centered around zero, with a relatively narrow range (0 to 40) and minimal outliers.
Interpretation: The model performs well in Asia, with unbiased and random errors.
Europe
Observation: Residuals are symmetrical and centered around zero, with a range of approximately -30 to 30.
Interpretation: The errors are random and unbiased, indicating the model fits European data well.
North America
Observation: Residuals are tightly clustered around zero, with a narrow range (-25 to 25).
Interpretation: The model performs exceptionally well, with minimal error and unbiased predictions for North America.
Oceania
Observation: Residuals are centered near zero, with a symmetrical and narrow distribution (-25 to 25).
Interpretation: Similar to North America, the model fits Oceania’s data effectively, with low residual variance.
South America
Observation: Residuals show a slight right skew, with most values between -20 and 40, but some outliers beyond 40.
Interpretation: While the model generally fits the data well, there may be some underestimation or overestimation in specific cases.
Randomness: Residuals appear mostly random for Asia, Europe, North America, and Oceania, suggesting good model performance in these regions.
Bias: Africa and South America show signs of potential bias or model underperformance, as indicated by the wider spread and skew.
Consistency: The model performs consistently well for regions with stable economic conditions (e.g., North America, Oceania).
Inflation Trends and Forecasts: A Global Analysis
This project explored global inflation trends, focusing on the year-over-year (YoY) changes in CPI (Consumer Price Index). By analyzing historical data and applying advanced statistical models, we sought to understand past trends, assess regional differences, and forecast future inflation.
No Seasonal Patterns Identified
The year-over-year CPI data was adjusted to account for seasonality. After analysis, we confirmed that there were no significant seasonal patterns remaining in the dataset. This allowed us to focus solely on broader trends and volatility without the influence of predictable seasonal fluctuations.
Global Inflation Trends
Inflation has shown a general upward trend globally, with some regions experiencing higher growth and volatility than others. For instance:
Africa and South America displayed higher levels of inflation and irregular spikes, indicating economic instability or external shocks.
Europe and Oceania experienced more controlled inflation, with steadier increases over time.
Asia and North America exhibited mixed patterns, balancing between stability and occasional periods of fluctuation.
Regional Variations in Inflation Levels
Developing regions generally exhibited higher inflation rates compared to developed regions. For instance:
Africa’s inflation is projected to remain high, potentially reaching levels significantly above global averages.
Europe and Oceania show moderate and stable growth, reflecting stronger economic and monetary frameworks.
Future Forecasts and Volatility
Using ARIMA models, we forecasted inflation trends for the next decade. The results suggest:
Volatility: Inflation volatility varies, with continents like North America and Oceania showing greater stability, while Africa and South America experience more unpredictability.
Our analysis shows that while inflation is rising globally, its magnitude and volatility vary significantly across continents. By using seasonally adjusted Y-O-Y CPI data, we ensured that our findings focus on structural trends and long-term changes