Assignment
20251MBI024 Ineza Kevine
2026-06-04
#Find a data set of which you can fit multiple linear regression and interpret your results
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.2.1 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.3 ✔ tibble 3.3.1
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
library(dplyr)setwd("D:/AUCA/R for data science/exam practice")getwd()## [1] "D:/AUCA/R for data science/exam practice"df <- read.csv("CO2_emission.csv")
head(df)## Country.Name country_code Region
## 1 Aruba ABW Latin America & Caribbean
## 2 Afghanistan AFG South Asia
## 3 Angola AGO Sub-Saharan Africa
## 4 Albania ALB Europe & Central Asia
## 5 Andorra AND Europe & Central Asia
## 6 United Arab Emirates ARE Middle East & North Africa
## Indicator.Name X1990 X1991 X1992
## 1 CO2 emissions (metric tons per capita) NA NA NA
## 2 CO2 emissions (metric tons per capita) 0.1917451 0.1676816 0.09595774
## 3 CO2 emissions (metric tons per capita) 0.5536620 0.5445386 0.54355722
## 4 CO2 emissions (metric tons per capita) 1.8195416 1.2428102 0.68369983
## 5 CO2 emissions (metric tons per capita) 7.5218317 7.2353792 6.96307870
## 6 CO2 emissions (metric tons per capita) 30.1951886 31.7784962 29.08092584
## X1993 X1994 X1995 X1996 X1997 X1998
## 1 NA NA NA NA NA NA
## 2 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662 0.05269086
## 3 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697 1.09182531
## 4 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147 0.57215370
## 5 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115 7.63953851
## 6 29.27567777 30.84933296 31.12501806 30.92802588 30.48633262 29.66358052
## X1999 X2000 X2001 X2002 X2003 X2004
## 1 NA NA NA NA NA NA
## 2 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 3 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## 4 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 5 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 28.88710798 27.0351591 29.43026994 28.50146173 27.96926982 27.03893822
## X2005 X2006 X2007 X2008 X2009 X2010
## 1 NA NA NA NA NA NA
## 2 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140
## 3 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842
## 4 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978
## 6 25.38238104 22.93510429 21.37028576 22.0114692 19.8323489 19.0397698
## X2011 X2012 X2013 X2014 X2015 X2016 X2017
## 1 NA NA NA NA NA NA NA
## 2 0.2965062 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946
## 3 0.9855223 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007
## 4 1.6694232 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861
## 5 5.8508861 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339
## 6 18.5094574 19.2078011 20.0556476 20.0516980 21.0776420 21.4806686 20.7690223
## X2018 X2019 X2019.1
## 1 NA NA NA
## 2 0.1632953 0.1598244 0.1598244
## 3 0.7776749 0.7921371 0.7921371
## 4 1.7827389 1.6922483 1.6922483
## 5 6.3629754 6.4812174 6.4812174
## 6 18.3906781 19.3295633 19.3295633glimpse(df)## Rows: 215
## Columns: 35
## $ Country.Name <chr> "Aruba", "Afghanistan", "Angola", "Albania", "Andorra",…
## $ country_code <chr> "ABW", "AFG", "AGO", "ALB", "AND", "ARE", "ARG", "ARM",…
## $ Region <chr> "Latin America & Caribbean", "South Asia", "Sub-Saharan…
## $ Indicator.Name <chr> "CO2 emissions (metric tons per capita)", "CO2 emission…
## $ X1990 <dbl> NA, 0.19174511, 0.55366196, 1.81954163, 7.52183166, 30.…
## $ X1991 <dbl> NA, 0.16768158, 0.54453865, 1.24281022, 7.23537924, 31.…
## $ X1992 <dbl> NA, 0.09595774, 0.54355722, 0.68369983, 6.96307870, 29.…
## $ X1993 <dbl> NA, 0.08472111, 0.70898423, 0.63830704, 6.72417752, 29.…
## $ X1994 <dbl> NA, 0.07554583, 0.83680440, 0.64535519, 6.54157891, 30.…
## $ X1995 <dbl> NA, 0.06846796, 0.91214149, 0.60543625, 6.73347949, 31.…
## $ X1996 <dbl> NA, 0.06258803, 1.07216847, 0.61236736, 6.99159455, 30.…
## $ X1997 <dbl> NA, 0.05682662, 1.08663697, 0.46692147, 7.30744115, 30.…
## $ X1998 <dbl> NA, 0.05269086, 1.09182531, 0.57215370, 7.63953851, 29.…
## $ X1999 <dbl> NA, 0.04015697, 1.10985966, 0.95535931, 7.92319165, 28.…
## $ X2000 <dbl> NA, 0.03657370, 0.98807738, 1.02621311, 7.95228628, 27.…
## $ X2001 <dbl> NA, 0.03378536, 0.94182891, 1.05549588, 7.72154906, 29.…
## $ X2002 <dbl> NA, 0.04557366, 0.89557767, 1.23237878, 7.56623988, 28.…
## $ X2003 <dbl> NA, 0.05151838, 0.92486944, 1.33898498, 7.24241557, 27.…
## $ X2004 <dbl> NA, 0.04165539, 0.93026295, 1.40405869, 7.34426233, 27.…
## $ X2005 <dbl> NA, 0.06041878, 0.81353929, 1.33820940, 7.35378001, 25.…
## $ X2006 <dbl> NA, 0.06658329, 0.82184008, 1.33999574, 6.79054277, 22.…
## $ X2007 <dbl> NA, 0.06531235, 0.81175351, 1.39393137, 6.53104692, 21.…
## $ X2008 <dbl> NA, 0.12841656, 0.88865801, 1.38431125, 6.43930386, 22.…
## $ X2009 <dbl> NA, 0.17186242, 0.93940398, 1.44149356, 6.15668748, 19.…
## $ X2010 <dbl> NA, 0.24361404, 0.97618420, 1.52762366, 6.15719778, 19.…
## $ X2011 <dbl> NA, 0.29650624, 0.98552231, 1.66942319, 5.85088610, 18.…
## $ X2012 <dbl> NA, 0.25929533, 0.95069588, 1.50324046, 5.94465417, 19.…
## $ X2013 <dbl> NA, 0.18562366, 1.03629385, 1.53363004, 5.94280041, 20.…
## $ X2014 <dbl> NA, 0.14623562, 1.09977911, 1.66833737, 5.80712772, 20.…
## $ X2015 <dbl> NA, 0.17289674, 1.13504405, 1.60377515, 6.02618182, 21.…
## $ X2016 <dbl> NA, 0.1497893, 1.0318113, 1.5576644, 6.0806003, 21.4806…
## $ X2017 <dbl> NA, 0.13169456, 0.81330073, 1.78878607, 6.10413391, 20.…
## $ X2018 <dbl> NA, 0.16329530, 0.77767493, 1.78273895, 6.36297540, 18.…
## $ X2019 <dbl> NA, 0.15982437, 0.79213707, 1.69224832, 6.48121743, 19.…
## $ X2019.1 <dbl> NA, 0.15982437, 0.79213707, 1.69224832, 6.48121743, 19.…str(df)## 'data.frame': 215 obs. of 35 variables:
## $ Country.Name : chr "Aruba" "Afghanistan" "Angola" "Albania" ...
## $ country_code : chr "ABW" "AFG" "AGO" "ALB" ...
## $ Region : chr "Latin America & Caribbean" "South Asia" "Sub-Saharan Africa" "Europe & Central Asia" ...
## $ Indicator.Name: chr "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" "CO2 emissions (metric tons per capita)" ...
## $ X1990 : num NA 0.192 0.554 1.82 7.522 ...
## $ X1991 : num NA 0.168 0.545 1.243 7.235 ...
## $ X1992 : num NA 0.096 0.544 0.684 6.963 ...
## $ X1993 : num NA 0.0847 0.709 0.6383 6.7242 ...
## $ X1994 : num NA 0.0755 0.8368 0.6454 6.5416 ...
## $ X1995 : num NA 0.0685 0.9121 0.6054 6.7335 ...
## $ X1996 : num NA 0.0626 1.0722 0.6124 6.9916 ...
## $ X1997 : num NA 0.0568 1.0866 0.4669 7.3074 ...
## $ X1998 : num NA 0.0527 1.0918 0.5722 7.6395 ...
## $ X1999 : num NA 0.0402 1.1099 0.9554 7.9232 ...
## $ X2000 : num NA 0.0366 0.9881 1.0262 7.9523 ...
## $ X2001 : num NA 0.0338 0.9418 1.0555 7.7215 ...
## $ X2002 : num NA 0.0456 0.8956 1.2324 7.5662 ...
## $ X2003 : num NA 0.0515 0.9249 1.339 7.2424 ...
## $ X2004 : num NA 0.0417 0.9303 1.4041 7.3443 ...
## $ X2005 : num NA 0.0604 0.8135 1.3382 7.3538 ...
## $ X2006 : num NA 0.0666 0.8218 1.34 6.7905 ...
## $ X2007 : num NA 0.0653 0.8118 1.3939 6.531 ...
## $ X2008 : num NA 0.128 0.889 1.384 6.439 ...
## $ X2009 : num NA 0.172 0.939 1.441 6.157 ...
## $ X2010 : num NA 0.244 0.976 1.528 6.157 ...
## $ X2011 : num NA 0.297 0.986 1.669 5.851 ...
## $ X2012 : num NA 0.259 0.951 1.503 5.945 ...
## $ X2013 : num NA 0.186 1.036 1.534 5.943 ...
## $ X2014 : num NA 0.146 1.1 1.668 5.807 ...
## $ X2015 : num NA 0.173 1.135 1.604 6.026 ...
## $ X2016 : num NA 0.15 1.03 1.56 6.08 ...
## $ X2017 : num NA 0.132 0.813 1.789 6.104 ...
## $ X2018 : num NA 0.163 0.778 1.783 6.363 ...
## $ X2019 : num NA 0.16 0.792 1.692 6.481 ...
## $ X2019.1 : num NA 0.16 0.792 1.692 6.481 ...names(df)## [1] "Country.Name" "country_code" "Region" "Indicator.Name"
## [5] "X1990" "X1991" "X1992" "X1993"
## [9] "X1994" "X1995" "X1996" "X1997"
## [13] "X1998" "X1999" "X2000" "X2001"
## [17] "X2002" "X2003" "X2004" "X2005"
## [21] "X2006" "X2007" "X2008" "X2009"
## [25] "X2010" "X2011" "X2012" "X2013"
## [29] "X2014" "X2015" "X2016" "X2017"
## [33] "X2018" "X2019" "X2019.1"head(df)## Country.Name country_code Region
## 1 Aruba ABW Latin America & Caribbean
## 2 Afghanistan AFG South Asia
## 3 Angola AGO Sub-Saharan Africa
## 4 Albania ALB Europe & Central Asia
## 5 Andorra AND Europe & Central Asia
## 6 United Arab Emirates ARE Middle East & North Africa
## Indicator.Name X1990 X1991 X1992
## 1 CO2 emissions (metric tons per capita) NA NA NA
## 2 CO2 emissions (metric tons per capita) 0.1917451 0.1676816 0.09595774
## 3 CO2 emissions (metric tons per capita) 0.5536620 0.5445386 0.54355722
## 4 CO2 emissions (metric tons per capita) 1.8195416 1.2428102 0.68369983
## 5 CO2 emissions (metric tons per capita) 7.5218317 7.2353792 6.96307870
## 6 CO2 emissions (metric tons per capita) 30.1951886 31.7784962 29.08092584
## X1993 X1994 X1995 X1996 X1997 X1998
## 1 NA NA NA NA NA NA
## 2 0.08472111 0.07554583 0.06846796 0.06258803 0.05682662 0.05269086
## 3 0.70898423 0.83680440 0.91214149 1.07216847 1.08663697 1.09182531
## 4 0.63830704 0.64535519 0.60543625 0.61236736 0.46692147 0.57215370
## 5 6.72417752 6.54157891 6.73347949 6.99159455 7.30744115 7.63953851
## 6 29.27567777 30.84933296 31.12501806 30.92802588 30.48633262 29.66358052
## X1999 X2000 X2001 X2002 X2003 X2004
## 1 NA NA NA NA NA NA
## 2 0.04015697 0.0365737 0.03378536 0.04557366 0.05151838 0.04165539
## 3 1.10985966 0.9880774 0.94182891 0.89557767 0.92486944 0.93026295
## 4 0.95535931 1.0262131 1.05549588 1.23237878 1.33898498 1.40405869
## 5 7.92319165 7.9522863 7.72154906 7.56623988 7.24241557 7.34426233
## 6 28.88710798 27.0351591 29.43026994 28.50146173 27.96926982 27.03893822
## X2005 X2006 X2007 X2008 X2009 X2010
## 1 NA NA NA NA NA NA
## 2 0.06041878 0.06658329 0.06531235 0.1284166 0.1718624 0.2436140
## 3 0.81353929 0.82184008 0.81175351 0.8886580 0.9394040 0.9761842
## 4 1.33820940 1.33999574 1.39393137 1.3843112 1.4414936 1.5276237
## 5 7.35378001 6.79054277 6.53104692 6.4393039 6.1566875 6.1571978
## 6 25.38238104 22.93510429 21.37028576 22.0114692 19.8323489 19.0397698
## X2011 X2012 X2013 X2014 X2015 X2016 X2017
## 1 NA NA NA NA NA NA NA
## 2 0.2965062 0.2592953 0.1856237 0.1462356 0.1728967 0.1497893 0.1316946
## 3 0.9855223 0.9506959 1.0362939 1.0997791 1.1350441 1.0318113 0.8133007
## 4 1.6694232 1.5032405 1.5336300 1.6683374 1.6037751 1.5576644 1.7887861
## 5 5.8508861 5.9446542 5.9428004 5.8071277 6.0261818 6.0806003 6.1041339
## 6 18.5094574 19.2078011 20.0556476 20.0516980 21.0776420 21.4806686 20.7690223
## X2018 X2019 X2019.1
## 1 NA NA NA
## 2 0.1632953 0.1598244 0.1598244
## 3 0.7776749 0.7921371 0.7921371
## 4 1.7827389 1.6922483 1.6922483
## 5 6.3629754 6.4812174 6.4812174
## 6 18.3906781 19.3295633 19.3295633df <- df %>%
select(-matches("2019\\.1|2019\\.2"))names(df)## [1] "Country.Name" "country_code" "Region" "Indicator.Name"
## [5] "X1990" "X1991" "X1992" "X1993"
## [9] "X1994" "X1995" "X1996" "X1997"
## [13] "X1998" "X1999" "X2000" "X2001"
## [17] "X2002" "X2003" "X2004" "X2005"
## [21] "X2006" "X2007" "X2008" "X2009"
## [25] "X2010" "X2011" "X2012" "X2013"
## [29] "X2014" "X2015" "X2016" "X2017"
## [33] "X2018" "X2019"names(df) <- gsub("^X", "", names(df))names(df)## [1] "Country.Name" "country_code" "Region" "Indicator.Name"
## [5] "1990" "1991" "1992" "1993"
## [9] "1994" "1995" "1996" "1997"
## [13] "1998" "1999" "2000" "2001"
## [17] "2002" "2003" "2004" "2005"
## [21] "2006" "2007" "2008" "2009"
## [25] "2010" "2011" "2012" "2013"
## [29] "2014" "2015" "2016" "2017"
## [33] "2018" "2019"library(tidyverse)
df_long <- df %>%
pivot_longer(
cols = `1990`:`2019`,
names_to = "year",
values_to = "value"
) %>%
mutate(
year = as.numeric(year)
)df_long <- df_long %>%
filter(!is.na(value))df_long <- df_long %>%
mutate(
Region = as.factor(Region),
country_code = as.factor(country_code)
)model1 <- lm(value ~ year + Region, data = df_long)
summary(model1)##
## Call:
## lm(formula = value ~ year + Region, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.008 -2.027 -0.625 0.888 41.640
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.466189 13.940549 -0.033 0.973
## year 0.002332 0.006953 0.335 0.737
## RegionEurope & Central Asia 2.543608 0.192548 13.210 < 2e-16 ***
## RegionLatin America & Caribbean -1.345976 0.210110 -6.406 1.61e-10 ***
## RegionMiddle East & North Africa 5.108884 0.240492 21.243 < 2e-16 ***
## RegionNorth America 12.828038 0.605412 21.189 < 2e-16 ***
## RegionSouth Asia -3.481388 0.330366 -10.538 < 2e-16 ***
## RegionSub-Saharan Africa -3.374782 0.194154 -17.382 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.538 on 5696 degrees of freedom
## Multiple R-squared: 0.3237, Adjusted R-squared: 0.3229
## F-statistic: 389.5 on 7 and 5696 DF, p-value: < 2.2e-16model2 <- lm(value ~ year * Region + country_code, data = df_long)
summary(model2)##
## Call:
## lm(formula = value ~ year * Region + country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.8681 -0.2507 -0.0051 0.2358 12.6094
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -63.233898 9.959167 -6.349 2.34e-10
## year 0.032039 0.004967 6.450 1.21e-10
## RegionEurope & Central Asia 182.062538 12.491963 14.574 < 2e-16
## RegionLatin America & Caribbean -8.006687 13.613063 -0.588 0.556448
## RegionMiddle East & North Africa 34.389482 15.578158 2.208 0.027317
## RegionNorth America 250.437018 38.980981 6.425 1.43e-10
## RegionSouth Asia -3.016360 21.314495 -0.142 0.887467
## RegionSub-Saharan Africa 36.553223 12.602042 2.901 0.003739
## country_codeAGO -0.149129 0.325462 -0.458 0.646821
## country_codeALB -3.193489 0.325462 -9.812 < 2e-16
## country_codeAND 2.336934 0.325462 7.180 7.87e-13
## country_codeARE 24.251033 0.325462 74.513 < 2e-16
## country_codeARG -1.601866 0.325462 -4.922 8.82e-07
## country_codeARM -2.625059 0.325462 -8.066 8.87e-16
## country_codeATG -0.096989 0.325462 -0.298 0.765711
## country_codeAUS 15.838322 0.325462 48.664 < 2e-16
## country_codeAUT 3.547344 0.325462 10.899 < 2e-16
## country_codeAZE -0.527146 0.325462 -1.620 0.105357
## country_codeBDI -1.014670 0.325462 -3.118 0.001833
## country_codeBEL 5.689041 0.325462 17.480 < 2e-16
## country_codeBEN -0.710719 0.325462 -2.184 0.029024
## country_codeBFA -0.934278 0.325462 -2.871 0.004112
## country_codeBGD 0.152622 0.325462 0.469 0.639132
## country_codeBGR 1.779374 0.325462 5.467 4.77e-08
## country_codeBHR 21.677024 0.325462 66.604 < 2e-16
## country_codeBHS 2.237421 0.325462 6.875 6.90e-12
## country_codeBIH 0.049109 0.325462 0.151 0.880069
## country_codeBLR 1.857654 0.325462 5.708 1.20e-08
## country_codeBLZ -3.378674 0.325462 -10.381 < 2e-16
## country_codeBOL -3.924199 0.325462 -12.057 < 2e-16
## country_codeBRA -3.493048 0.325462 -10.733 < 2e-16
## country_codeBRB -0.173706 0.325462 -0.534 0.593556
## country_codeBRN 15.073230 0.325462 46.313 < 2e-16
## country_codeBTN 0.478625 0.325462 1.471 0.141456
## country_codeBWA 1.359778 0.325462 4.178 2.99e-05
## country_codeCAF -0.998843 0.325462 -3.069 0.002158
## country_codeCAN -2.230251 0.325462 -6.853 8.05e-12
## country_codeCHE 1.304351 0.325462 4.008 6.21e-05
## country_codeCHL -1.725420 0.325462 -5.301 1.19e-07
## country_codeCHN 3.561359 0.325462 10.942 < 2e-16
## country_codeCIV -0.719756 0.325462 -2.211 0.027043
## country_codeCMR -0.697514 0.325462 -2.143 0.032145
## country_codeCOD -1.004572 0.325462 -3.087 0.002035
## country_codeCOG 0.108490 0.325462 0.333 0.738889
## country_codeCOL -3.823909 0.325462 -11.749 < 2e-16
## country_codeCOM -0.831300 0.325462 -2.554 0.010669
## country_codeCPV -0.311615 0.325462 -0.957 0.338380
## country_codeCRI -3.899059 0.325462 -11.980 < 2e-16
## country_codeCUB -2.937519 0.325462 -9.026 < 2e-16
## country_codeCYP 2.474145 0.325462 7.602 3.41e-14
## country_codeCZE 6.860147 0.325462 21.078 < 2e-16
## country_codeDEU 5.496587 0.325462 16.889 < 2e-16
## country_codeDJI -0.288340 0.325462 -0.886 0.375689
## country_codeDMA -3.179410 0.325462 -9.769 < 2e-16
## country_codeDNK 4.892993 0.325462 15.034 < 2e-16
## country_codeDOM -3.358059 0.325462 -10.318 < 2e-16
## country_codeDZA 2.216244 0.325462 6.810 1.08e-11
## country_codeECU -3.272187 0.325462 -10.054 < 2e-16
## country_codeEGY 1.226015 0.325462 3.767 0.000167
## country_codeERI -0.822484 0.331245 -2.483 0.013057
## country_codeESP 1.853119 0.325462 5.694 1.31e-08
## country_codeEST 8.481126 0.325462 26.059 < 2e-16
## country_codeETH -0.970977 0.325462 -2.983 0.002863
## country_codeFIN 6.139156 0.325462 18.863 < 2e-16
## country_codeFJI 0.289341 0.325462 0.889 0.374033
## country_codeFRA 1.172116 0.325462 3.601 0.000319
## country_codeFSM 0.270524 0.331260 0.817 0.414163
## country_codeGAB 2.959318 0.325462 9.093 < 2e-16
## country_codeGBR 3.717187 0.325462 11.421 < 2e-16
## country_codeGEO -2.382585 0.325462 -7.321 2.82e-13
## country_codeGHA -0.667153 0.325462 -2.050 0.040425
## country_codeGIN -0.846581 0.325462 -2.601 0.009316
## country_codeGMB -0.827194 0.325462 -2.542 0.011062
## country_codeGNB -0.891169 0.325462 -2.738 0.006198
## country_codeGNQ 4.988095 0.325462 15.326 < 2e-16
## country_codeGRC 3.335677 0.325462 10.249 < 2e-16
## country_codeGRD -2.856660 0.325462 -8.777 < 2e-16
## country_codeGTM -4.538142 0.325462 -13.944 < 2e-16
## country_codeGUY -3.039007 0.325462 -9.338 < 2e-16
## country_codeHND -4.464766 0.325462 -13.718 < 2e-16
## country_codeHRV -0.267895 0.325462 -0.823 0.410473
## country_codeHTI -5.135783 0.325462 -15.780 < 2e-16
## country_codeHUN 0.786905 0.325462 2.418 0.015646
## country_codeIDN 0.524591 0.325462 1.612 0.107055
## country_codeIND 1.009405 0.325462 3.101 0.001935
## country_codeIRL 4.929810 0.325462 15.147 < 2e-16
## country_codeIRN 5.253277 0.325462 16.141 < 2e-16
## country_codeIRQ 3.047444 0.325462 9.363 < 2e-16
## country_codeISL 2.502198 0.325462 7.688 1.76e-14
## country_codeISR 7.600754 0.325462 23.354 < 2e-16
## country_codeITA 2.491582 0.325462 7.656 2.26e-14
## country_codeJAM -2.058446 0.325462 -6.325 2.74e-10
## country_codeJOR 2.135598 0.325462 6.562 5.80e-11
## country_codeJPN 8.236514 0.325462 25.307 < 2e-16
## country_codeKAZ 7.406422 0.325462 22.757 < 2e-16
## country_codeKEN -0.779122 0.325462 -2.394 0.016704
## country_codeKGZ -2.812972 0.325462 -8.643 < 2e-16
## country_codeKHM -0.671830 0.325462 -2.064 0.039042
## country_codeKIR -0.485786 0.325462 -1.493 0.135598
## country_codeKNA -0.539886 0.325462 -1.659 0.097207
## country_codeKOR 8.935886 0.325462 27.456 < 2e-16
## country_codeKWT 23.112341 0.334467 69.102 < 2e-16
## country_codeLAO -0.375489 0.325462 -1.154 0.248668
## country_codeLBN 3.005570 0.325462 9.235 < 2e-16
## country_codeLBR -0.846016 0.325462 -2.599 0.009363
## country_codeLBY 7.409509 0.325462 22.766 < 2e-16
## country_codeLCA -2.516674 0.325462 -7.733 1.24e-14
## country_codeLIE 1.493511 0.325462 4.589 4.55e-06
## country_codeLKA 0.511548 0.325462 1.572 0.116064
## country_codeLSO -0.807198 0.325462 -2.480 0.013162
## country_codeLTU -0.227876 0.325462 -0.700 0.483856
## country_codeLUX 17.115382 0.325462 52.588 < 2e-16
## country_codeLVA -0.545676 0.325462 -1.677 0.093674
## country_codeMAR 0.574110 0.325462 1.764 0.077790
## country_codeMDA -0.849227 0.325462 -2.609 0.009097
## country_codeMDG -0.947529 0.325462 -2.911 0.003613
## country_codeMDV 2.045187 0.325462 6.284 3.55e-10
## country_codeMEX -1.554325 0.325462 -4.776 1.84e-06
## country_codeMHL 1.308436 0.331260 3.950 7.92e-05
## country_codeMKD -0.159153 0.325462 -0.489 0.624856
## country_codeMLI -0.923828 0.331227 -2.789 0.005303
## country_codeMLT 4.977278 0.325462 15.293 < 2e-16
## country_codeMMR -0.751099 0.325462 -2.308 0.021048
## country_codeMNE -1.288668 0.325462 -3.960 7.61e-05
## country_codeMNG 4.077466 0.325462 12.528 < 2e-16
## country_codeMOZ -0.928235 0.325462 -2.852 0.004360
## country_codeMRT -0.443590 0.325462 -1.363 0.172952
## country_codeMUS 1.257699 0.325462 3.864 0.000113
## country_codeMWI -0.983667 0.325462 -3.022 0.002520
## country_codeMYS 4.998246 0.325462 15.357 < 2e-16
## country_codeNAM 0.282860 0.328261 0.862 0.388894
## country_codeNER -0.977335 0.325462 -3.003 0.002686
## country_codeNGA -0.354572 0.325462 -1.089 0.276006
## country_codeNIC -4.611968 0.325462 -14.171 < 2e-16
## country_codeNLD 5.509005 0.325462 16.927 < 2e-16
## country_codeNOR 3.267648 0.325462 10.040 < 2e-16
## country_codeNPL 0.057174 0.325462 0.176 0.860559
## country_codeNRU 6.383634 0.325462 19.614 < 2e-16
## country_codeNZL 6.278328 0.325462 19.291 < 2e-16
## country_codeOMN 11.827954 0.325462 36.342 < 2e-16
## country_codePAK 0.625074 0.325462 1.921 0.054837
## country_codePAN -3.274066 0.325462 -10.060 < 2e-16
## country_codePER -4.050152 0.325462 -12.444 < 2e-16
## country_codePHL -0.067768 0.325462 -0.208 0.835064
## country_codePLW 11.077620 0.331260 33.441 < 2e-16
## country_codePNG -0.357869 0.325462 -1.100 0.271566
## country_codePOL 3.774521 0.325462 11.597 < 2e-16
## country_codePRK 1.923428 0.325462 5.910 3.63e-09
## country_codePRT 0.688551 0.325462 2.116 0.034423
## country_codePRY -4.558799 0.325462 -14.007 < 2e-16
## country_codeQAT 37.561595 0.325462 115.410 < 2e-16
## country_codeROU 0.072810 0.325462 0.224 0.822990
## country_codeRUS 7.024388 0.325462 21.583 < 2e-16
## country_codeRWA -0.974817 0.325462 -2.995 0.002755
## country_codeSAU 12.972570 0.325462 39.859 < 2e-16
## country_codeSDN -0.714536 0.325462 -2.195 0.028173
## country_codeSEN -0.573002 0.325462 -1.761 0.078365
## country_codeSGP 8.177079 0.325462 25.125 < 2e-16
## country_codeSLB -0.397229 0.325462 -1.221 0.222325
## country_codeSLE -0.955713 0.325462 -2.936 0.003333
## country_codeSLV -4.355922 0.325462 -13.384 < 2e-16
## country_codeSOM -0.988032 0.325462 -3.036 0.002410
## country_codeSRB 2.103883 0.325462 6.464 1.11e-10
## country_codeSSD -0.938310 0.325462 -2.883 0.003954
## country_codeSTP -0.566654 0.325462 -1.741 0.081726
## country_codeSUR -1.365974 0.325462 -4.197 2.75e-05
## country_codeSVK 2.646808 0.325462 8.132 5.15e-16
## country_codeSVN 2.873068 0.325462 8.828 < 2e-16
## country_codeSWE 0.973743 0.325462 2.992 0.002785
## country_codeSWZ -0.388129 0.325462 -1.193 0.233097
## country_codeSYC 3.216208 0.325462 9.882 < 2e-16
## country_codeSYR 1.679334 0.325462 5.160 2.56e-07
## country_codeTCD -0.965699 0.325462 -2.967 0.003019
## country_codeTGO -0.770586 0.325462 -2.368 0.017935
## country_codeTHA 2.062892 0.325462 6.338 2.51e-10
## country_codeTJK -3.820133 0.325462 -11.738 < 2e-16
## country_codeTKM 5.942881 0.325462 18.260 < 2e-16
## country_codeTLS -0.874255 0.376991 -2.319 0.020430
## country_codeTON 0.162107 0.325462 0.498 0.618445
## country_codeTTO 6.654215 0.325462 20.445 < 2e-16
## country_codeTUN 1.443936 0.325462 4.437 9.32e-06
## country_codeTUR -0.772883 0.325462 -2.375 0.017596
## country_codeTUV 0.011769 0.325462 0.036 0.971156
## country_codeTZA -0.912599 0.325462 -2.804 0.005065
## country_codeUGA -0.969482 0.325462 -2.979 0.002907
## country_codeUKR 2.260593 0.325462 6.946 4.20e-12
## country_codeURY -3.567963 0.325462 -10.963 < 2e-16
## country_codeUSA NA NA NA NA
## country_codeUZB NA NA NA NA
## country_codeVCT -3.183533 0.325462 -9.782 < 2e-16
## country_codeVEN NA NA NA NA
## country_codeVNM 0.251083 0.325462 0.771 0.440464
## country_codeVUT -0.502339 0.325462 -1.543 0.122775
## country_codeWSM NA NA NA NA
## country_codeYEM NA NA NA NA
## country_codeZAF 6.265177 0.325462 19.250 < 2e-16
## country_codeZMB -0.798310 0.325462 -2.453 0.014204
## country_codeZWE NA NA NA NA
## year:RegionEurope & Central Asia -0.089103 0.006230 -14.303 < 2e-16
## year:RegionLatin America & Caribbean 0.006163 0.006789 0.908 0.364027
## year:RegionMiddle East & North Africa -0.017248 0.007770 -2.220 0.026473
## year:RegionNorth America -0.116375 0.019446 -5.985 2.31e-09
## year:RegionSouth Asia 0.001070 0.010632 0.101 0.919851
## year:RegionSub-Saharan Africa -0.018205 0.006285 -2.897 0.003786
##
## (Intercept) ***
## year ***
## RegionEurope & Central Asia ***
## RegionLatin America & Caribbean
## RegionMiddle East & North Africa *
## RegionNorth America ***
## RegionSouth Asia
## RegionSub-Saharan Africa **
## country_codeAGO
## country_codeALB ***
## country_codeAND ***
## country_codeARE ***
## country_codeARG ***
## country_codeARM ***
## country_codeATG
## country_codeAUS ***
## country_codeAUT ***
## country_codeAZE
## country_codeBDI **
## country_codeBEL ***
## country_codeBEN *
## country_codeBFA **
## country_codeBGD
## country_codeBGR ***
## country_codeBHR ***
## country_codeBHS ***
## country_codeBIH
## country_codeBLR ***
## country_codeBLZ ***
## country_codeBOL ***
## country_codeBRA ***
## country_codeBRB
## country_codeBRN ***
## country_codeBTN
## country_codeBWA ***
## country_codeCAF **
## country_codeCAN ***
## country_codeCHE ***
## country_codeCHL ***
## country_codeCHN ***
## country_codeCIV *
## country_codeCMR *
## country_codeCOD **
## country_codeCOG
## country_codeCOL ***
## country_codeCOM *
## country_codeCPV
## country_codeCRI ***
## country_codeCUB ***
## country_codeCYP ***
## country_codeCZE ***
## country_codeDEU ***
## country_codeDJI
## country_codeDMA ***
## country_codeDNK ***
## country_codeDOM ***
## country_codeDZA ***
## country_codeECU ***
## country_codeEGY ***
## country_codeERI *
## country_codeESP ***
## country_codeEST ***
## country_codeETH **
## country_codeFIN ***
## country_codeFJI
## country_codeFRA ***
## country_codeFSM
## country_codeGAB ***
## country_codeGBR ***
## country_codeGEO ***
## country_codeGHA *
## country_codeGIN **
## country_codeGMB *
## country_codeGNB **
## country_codeGNQ ***
## country_codeGRC ***
## country_codeGRD ***
## country_codeGTM ***
## country_codeGUY ***
## country_codeHND ***
## country_codeHRV
## country_codeHTI ***
## country_codeHUN *
## country_codeIDN
## country_codeIND **
## country_codeIRL ***
## country_codeIRN ***
## country_codeIRQ ***
## country_codeISL ***
## country_codeISR ***
## country_codeITA ***
## country_codeJAM ***
## country_codeJOR ***
## country_codeJPN ***
## country_codeKAZ ***
## country_codeKEN *
## country_codeKGZ ***
## country_codeKHM *
## country_codeKIR
## country_codeKNA .
## country_codeKOR ***
## country_codeKWT ***
## country_codeLAO
## country_codeLBN ***
## country_codeLBR **
## country_codeLBY ***
## country_codeLCA ***
## country_codeLIE ***
## country_codeLKA
## country_codeLSO *
## country_codeLTU
## country_codeLUX ***
## country_codeLVA .
## country_codeMAR .
## country_codeMDA **
## country_codeMDG **
## country_codeMDV ***
## country_codeMEX ***
## country_codeMHL ***
## country_codeMKD
## country_codeMLI **
## country_codeMLT ***
## country_codeMMR *
## country_codeMNE ***
## country_codeMNG ***
## country_codeMOZ **
## country_codeMRT
## country_codeMUS ***
## country_codeMWI **
## country_codeMYS ***
## country_codeNAM
## country_codeNER **
## country_codeNGA
## country_codeNIC ***
## country_codeNLD ***
## country_codeNOR ***
## country_codeNPL
## country_codeNRU ***
## country_codeNZL ***
## country_codeOMN ***
## country_codePAK .
## country_codePAN ***
## country_codePER ***
## country_codePHL
## country_codePLW ***
## country_codePNG
## country_codePOL ***
## country_codePRK ***
## country_codePRT *
## country_codePRY ***
## country_codeQAT ***
## country_codeROU
## country_codeRUS ***
## country_codeRWA **
## country_codeSAU ***
## country_codeSDN *
## country_codeSEN .
## country_codeSGP ***
## country_codeSLB
## country_codeSLE **
## country_codeSLV ***
## country_codeSOM **
## country_codeSRB ***
## country_codeSSD **
## country_codeSTP .
## country_codeSUR ***
## country_codeSVK ***
## country_codeSVN ***
## country_codeSWE **
## country_codeSWZ
## country_codeSYC ***
## country_codeSYR ***
## country_codeTCD **
## country_codeTGO *
## country_codeTHA ***
## country_codeTJK ***
## country_codeTKM ***
## country_codeTLS *
## country_codeTON
## country_codeTTO ***
## country_codeTUN ***
## country_codeTUR *
## country_codeTUV
## country_codeTZA **
## country_codeUGA **
## country_codeUKR ***
## country_codeURY ***
## country_codeUSA
## country_codeUZB
## country_codeVCT ***
## country_codeVEN
## country_codeVNM
## country_codeVUT
## country_codeWSM
## country_codeYEM
## country_codeZAF ***
## country_codeZMB *
## country_codeZWE
## year:RegionEurope & Central Asia ***
## year:RegionLatin America & Caribbean
## year:RegionMiddle East & North Africa *
## year:RegionNorth America ***
## year:RegionSouth Asia
## year:RegionSub-Saharan Africa **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 5506 degrees of freedom
## Multiple R-squared: 0.9496, Adjusted R-squared: 0.9478
## F-statistic: 526.1 on 197 and 5506 DF, p-value: < 2.2e-16summary(model2)##
## Call:
## lm(formula = value ~ year * Region + country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -19.8681 -0.2507 -0.0051 0.2358 12.6094
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -63.233898 9.959167 -6.349 2.34e-10
## year 0.032039 0.004967 6.450 1.21e-10
## RegionEurope & Central Asia 182.062538 12.491963 14.574 < 2e-16
## RegionLatin America & Caribbean -8.006687 13.613063 -0.588 0.556448
## RegionMiddle East & North Africa 34.389482 15.578158 2.208 0.027317
## RegionNorth America 250.437018 38.980981 6.425 1.43e-10
## RegionSouth Asia -3.016360 21.314495 -0.142 0.887467
## RegionSub-Saharan Africa 36.553223 12.602042 2.901 0.003739
## country_codeAGO -0.149129 0.325462 -0.458 0.646821
## country_codeALB -3.193489 0.325462 -9.812 < 2e-16
## country_codeAND 2.336934 0.325462 7.180 7.87e-13
## country_codeARE 24.251033 0.325462 74.513 < 2e-16
## country_codeARG -1.601866 0.325462 -4.922 8.82e-07
## country_codeARM -2.625059 0.325462 -8.066 8.87e-16
## country_codeATG -0.096989 0.325462 -0.298 0.765711
## country_codeAUS 15.838322 0.325462 48.664 < 2e-16
## country_codeAUT 3.547344 0.325462 10.899 < 2e-16
## country_codeAZE -0.527146 0.325462 -1.620 0.105357
## country_codeBDI -1.014670 0.325462 -3.118 0.001833
## country_codeBEL 5.689041 0.325462 17.480 < 2e-16
## country_codeBEN -0.710719 0.325462 -2.184 0.029024
## country_codeBFA -0.934278 0.325462 -2.871 0.004112
## country_codeBGD 0.152622 0.325462 0.469 0.639132
## country_codeBGR 1.779374 0.325462 5.467 4.77e-08
## country_codeBHR 21.677024 0.325462 66.604 < 2e-16
## country_codeBHS 2.237421 0.325462 6.875 6.90e-12
## country_codeBIH 0.049109 0.325462 0.151 0.880069
## country_codeBLR 1.857654 0.325462 5.708 1.20e-08
## country_codeBLZ -3.378674 0.325462 -10.381 < 2e-16
## country_codeBOL -3.924199 0.325462 -12.057 < 2e-16
## country_codeBRA -3.493048 0.325462 -10.733 < 2e-16
## country_codeBRB -0.173706 0.325462 -0.534 0.593556
## country_codeBRN 15.073230 0.325462 46.313 < 2e-16
## country_codeBTN 0.478625 0.325462 1.471 0.141456
## country_codeBWA 1.359778 0.325462 4.178 2.99e-05
## country_codeCAF -0.998843 0.325462 -3.069 0.002158
## country_codeCAN -2.230251 0.325462 -6.853 8.05e-12
## country_codeCHE 1.304351 0.325462 4.008 6.21e-05
## country_codeCHL -1.725420 0.325462 -5.301 1.19e-07
## country_codeCHN 3.561359 0.325462 10.942 < 2e-16
## country_codeCIV -0.719756 0.325462 -2.211 0.027043
## country_codeCMR -0.697514 0.325462 -2.143 0.032145
## country_codeCOD -1.004572 0.325462 -3.087 0.002035
## country_codeCOG 0.108490 0.325462 0.333 0.738889
## country_codeCOL -3.823909 0.325462 -11.749 < 2e-16
## country_codeCOM -0.831300 0.325462 -2.554 0.010669
## country_codeCPV -0.311615 0.325462 -0.957 0.338380
## country_codeCRI -3.899059 0.325462 -11.980 < 2e-16
## country_codeCUB -2.937519 0.325462 -9.026 < 2e-16
## country_codeCYP 2.474145 0.325462 7.602 3.41e-14
## country_codeCZE 6.860147 0.325462 21.078 < 2e-16
## country_codeDEU 5.496587 0.325462 16.889 < 2e-16
## country_codeDJI -0.288340 0.325462 -0.886 0.375689
## country_codeDMA -3.179410 0.325462 -9.769 < 2e-16
## country_codeDNK 4.892993 0.325462 15.034 < 2e-16
## country_codeDOM -3.358059 0.325462 -10.318 < 2e-16
## country_codeDZA 2.216244 0.325462 6.810 1.08e-11
## country_codeECU -3.272187 0.325462 -10.054 < 2e-16
## country_codeEGY 1.226015 0.325462 3.767 0.000167
## country_codeERI -0.822484 0.331245 -2.483 0.013057
## country_codeESP 1.853119 0.325462 5.694 1.31e-08
## country_codeEST 8.481126 0.325462 26.059 < 2e-16
## country_codeETH -0.970977 0.325462 -2.983 0.002863
## country_codeFIN 6.139156 0.325462 18.863 < 2e-16
## country_codeFJI 0.289341 0.325462 0.889 0.374033
## country_codeFRA 1.172116 0.325462 3.601 0.000319
## country_codeFSM 0.270524 0.331260 0.817 0.414163
## country_codeGAB 2.959318 0.325462 9.093 < 2e-16
## country_codeGBR 3.717187 0.325462 11.421 < 2e-16
## country_codeGEO -2.382585 0.325462 -7.321 2.82e-13
## country_codeGHA -0.667153 0.325462 -2.050 0.040425
## country_codeGIN -0.846581 0.325462 -2.601 0.009316
## country_codeGMB -0.827194 0.325462 -2.542 0.011062
## country_codeGNB -0.891169 0.325462 -2.738 0.006198
## country_codeGNQ 4.988095 0.325462 15.326 < 2e-16
## country_codeGRC 3.335677 0.325462 10.249 < 2e-16
## country_codeGRD -2.856660 0.325462 -8.777 < 2e-16
## country_codeGTM -4.538142 0.325462 -13.944 < 2e-16
## country_codeGUY -3.039007 0.325462 -9.338 < 2e-16
## country_codeHND -4.464766 0.325462 -13.718 < 2e-16
## country_codeHRV -0.267895 0.325462 -0.823 0.410473
## country_codeHTI -5.135783 0.325462 -15.780 < 2e-16
## country_codeHUN 0.786905 0.325462 2.418 0.015646
## country_codeIDN 0.524591 0.325462 1.612 0.107055
## country_codeIND 1.009405 0.325462 3.101 0.001935
## country_codeIRL 4.929810 0.325462 15.147 < 2e-16
## country_codeIRN 5.253277 0.325462 16.141 < 2e-16
## country_codeIRQ 3.047444 0.325462 9.363 < 2e-16
## country_codeISL 2.502198 0.325462 7.688 1.76e-14
## country_codeISR 7.600754 0.325462 23.354 < 2e-16
## country_codeITA 2.491582 0.325462 7.656 2.26e-14
## country_codeJAM -2.058446 0.325462 -6.325 2.74e-10
## country_codeJOR 2.135598 0.325462 6.562 5.80e-11
## country_codeJPN 8.236514 0.325462 25.307 < 2e-16
## country_codeKAZ 7.406422 0.325462 22.757 < 2e-16
## country_codeKEN -0.779122 0.325462 -2.394 0.016704
## country_codeKGZ -2.812972 0.325462 -8.643 < 2e-16
## country_codeKHM -0.671830 0.325462 -2.064 0.039042
## country_codeKIR -0.485786 0.325462 -1.493 0.135598
## country_codeKNA -0.539886 0.325462 -1.659 0.097207
## country_codeKOR 8.935886 0.325462 27.456 < 2e-16
## country_codeKWT 23.112341 0.334467 69.102 < 2e-16
## country_codeLAO -0.375489 0.325462 -1.154 0.248668
## country_codeLBN 3.005570 0.325462 9.235 < 2e-16
## country_codeLBR -0.846016 0.325462 -2.599 0.009363
## country_codeLBY 7.409509 0.325462 22.766 < 2e-16
## country_codeLCA -2.516674 0.325462 -7.733 1.24e-14
## country_codeLIE 1.493511 0.325462 4.589 4.55e-06
## country_codeLKA 0.511548 0.325462 1.572 0.116064
## country_codeLSO -0.807198 0.325462 -2.480 0.013162
## country_codeLTU -0.227876 0.325462 -0.700 0.483856
## country_codeLUX 17.115382 0.325462 52.588 < 2e-16
## country_codeLVA -0.545676 0.325462 -1.677 0.093674
## country_codeMAR 0.574110 0.325462 1.764 0.077790
## country_codeMDA -0.849227 0.325462 -2.609 0.009097
## country_codeMDG -0.947529 0.325462 -2.911 0.003613
## country_codeMDV 2.045187 0.325462 6.284 3.55e-10
## country_codeMEX -1.554325 0.325462 -4.776 1.84e-06
## country_codeMHL 1.308436 0.331260 3.950 7.92e-05
## country_codeMKD -0.159153 0.325462 -0.489 0.624856
## country_codeMLI -0.923828 0.331227 -2.789 0.005303
## country_codeMLT 4.977278 0.325462 15.293 < 2e-16
## country_codeMMR -0.751099 0.325462 -2.308 0.021048
## country_codeMNE -1.288668 0.325462 -3.960 7.61e-05
## country_codeMNG 4.077466 0.325462 12.528 < 2e-16
## country_codeMOZ -0.928235 0.325462 -2.852 0.004360
## country_codeMRT -0.443590 0.325462 -1.363 0.172952
## country_codeMUS 1.257699 0.325462 3.864 0.000113
## country_codeMWI -0.983667 0.325462 -3.022 0.002520
## country_codeMYS 4.998246 0.325462 15.357 < 2e-16
## country_codeNAM 0.282860 0.328261 0.862 0.388894
## country_codeNER -0.977335 0.325462 -3.003 0.002686
## country_codeNGA -0.354572 0.325462 -1.089 0.276006
## country_codeNIC -4.611968 0.325462 -14.171 < 2e-16
## country_codeNLD 5.509005 0.325462 16.927 < 2e-16
## country_codeNOR 3.267648 0.325462 10.040 < 2e-16
## country_codeNPL 0.057174 0.325462 0.176 0.860559
## country_codeNRU 6.383634 0.325462 19.614 < 2e-16
## country_codeNZL 6.278328 0.325462 19.291 < 2e-16
## country_codeOMN 11.827954 0.325462 36.342 < 2e-16
## country_codePAK 0.625074 0.325462 1.921 0.054837
## country_codePAN -3.274066 0.325462 -10.060 < 2e-16
## country_codePER -4.050152 0.325462 -12.444 < 2e-16
## country_codePHL -0.067768 0.325462 -0.208 0.835064
## country_codePLW 11.077620 0.331260 33.441 < 2e-16
## country_codePNG -0.357869 0.325462 -1.100 0.271566
## country_codePOL 3.774521 0.325462 11.597 < 2e-16
## country_codePRK 1.923428 0.325462 5.910 3.63e-09
## country_codePRT 0.688551 0.325462 2.116 0.034423
## country_codePRY -4.558799 0.325462 -14.007 < 2e-16
## country_codeQAT 37.561595 0.325462 115.410 < 2e-16
## country_codeROU 0.072810 0.325462 0.224 0.822990
## country_codeRUS 7.024388 0.325462 21.583 < 2e-16
## country_codeRWA -0.974817 0.325462 -2.995 0.002755
## country_codeSAU 12.972570 0.325462 39.859 < 2e-16
## country_codeSDN -0.714536 0.325462 -2.195 0.028173
## country_codeSEN -0.573002 0.325462 -1.761 0.078365
## country_codeSGP 8.177079 0.325462 25.125 < 2e-16
## country_codeSLB -0.397229 0.325462 -1.221 0.222325
## country_codeSLE -0.955713 0.325462 -2.936 0.003333
## country_codeSLV -4.355922 0.325462 -13.384 < 2e-16
## country_codeSOM -0.988032 0.325462 -3.036 0.002410
## country_codeSRB 2.103883 0.325462 6.464 1.11e-10
## country_codeSSD -0.938310 0.325462 -2.883 0.003954
## country_codeSTP -0.566654 0.325462 -1.741 0.081726
## country_codeSUR -1.365974 0.325462 -4.197 2.75e-05
## country_codeSVK 2.646808 0.325462 8.132 5.15e-16
## country_codeSVN 2.873068 0.325462 8.828 < 2e-16
## country_codeSWE 0.973743 0.325462 2.992 0.002785
## country_codeSWZ -0.388129 0.325462 -1.193 0.233097
## country_codeSYC 3.216208 0.325462 9.882 < 2e-16
## country_codeSYR 1.679334 0.325462 5.160 2.56e-07
## country_codeTCD -0.965699 0.325462 -2.967 0.003019
## country_codeTGO -0.770586 0.325462 -2.368 0.017935
## country_codeTHA 2.062892 0.325462 6.338 2.51e-10
## country_codeTJK -3.820133 0.325462 -11.738 < 2e-16
## country_codeTKM 5.942881 0.325462 18.260 < 2e-16
## country_codeTLS -0.874255 0.376991 -2.319 0.020430
## country_codeTON 0.162107 0.325462 0.498 0.618445
## country_codeTTO 6.654215 0.325462 20.445 < 2e-16
## country_codeTUN 1.443936 0.325462 4.437 9.32e-06
## country_codeTUR -0.772883 0.325462 -2.375 0.017596
## country_codeTUV 0.011769 0.325462 0.036 0.971156
## country_codeTZA -0.912599 0.325462 -2.804 0.005065
## country_codeUGA -0.969482 0.325462 -2.979 0.002907
## country_codeUKR 2.260593 0.325462 6.946 4.20e-12
## country_codeURY -3.567963 0.325462 -10.963 < 2e-16
## country_codeUSA NA NA NA NA
## country_codeUZB NA NA NA NA
## country_codeVCT -3.183533 0.325462 -9.782 < 2e-16
## country_codeVEN NA NA NA NA
## country_codeVNM 0.251083 0.325462 0.771 0.440464
## country_codeVUT -0.502339 0.325462 -1.543 0.122775
## country_codeWSM NA NA NA NA
## country_codeYEM NA NA NA NA
## country_codeZAF 6.265177 0.325462 19.250 < 2e-16
## country_codeZMB -0.798310 0.325462 -2.453 0.014204
## country_codeZWE NA NA NA NA
## year:RegionEurope & Central Asia -0.089103 0.006230 -14.303 < 2e-16
## year:RegionLatin America & Caribbean 0.006163 0.006789 0.908 0.364027
## year:RegionMiddle East & North Africa -0.017248 0.007770 -2.220 0.026473
## year:RegionNorth America -0.116375 0.019446 -5.985 2.31e-09
## year:RegionSouth Asia 0.001070 0.010632 0.101 0.919851
## year:RegionSub-Saharan Africa -0.018205 0.006285 -2.897 0.003786
##
## (Intercept) ***
## year ***
## RegionEurope & Central Asia ***
## RegionLatin America & Caribbean
## RegionMiddle East & North Africa *
## RegionNorth America ***
## RegionSouth Asia
## RegionSub-Saharan Africa **
## country_codeAGO
## country_codeALB ***
## country_codeAND ***
## country_codeARE ***
## country_codeARG ***
## country_codeARM ***
## country_codeATG
## country_codeAUS ***
## country_codeAUT ***
## country_codeAZE
## country_codeBDI **
## country_codeBEL ***
## country_codeBEN *
## country_codeBFA **
## country_codeBGD
## country_codeBGR ***
## country_codeBHR ***
## country_codeBHS ***
## country_codeBIH
## country_codeBLR ***
## country_codeBLZ ***
## country_codeBOL ***
## country_codeBRA ***
## country_codeBRB
## country_codeBRN ***
## country_codeBTN
## country_codeBWA ***
## country_codeCAF **
## country_codeCAN ***
## country_codeCHE ***
## country_codeCHL ***
## country_codeCHN ***
## country_codeCIV *
## country_codeCMR *
## country_codeCOD **
## country_codeCOG
## country_codeCOL ***
## country_codeCOM *
## country_codeCPV
## country_codeCRI ***
## country_codeCUB ***
## country_codeCYP ***
## country_codeCZE ***
## country_codeDEU ***
## country_codeDJI
## country_codeDMA ***
## country_codeDNK ***
## country_codeDOM ***
## country_codeDZA ***
## country_codeECU ***
## country_codeEGY ***
## country_codeERI *
## country_codeESP ***
## country_codeEST ***
## country_codeETH **
## country_codeFIN ***
## country_codeFJI
## country_codeFRA ***
## country_codeFSM
## country_codeGAB ***
## country_codeGBR ***
## country_codeGEO ***
## country_codeGHA *
## country_codeGIN **
## country_codeGMB *
## country_codeGNB **
## country_codeGNQ ***
## country_codeGRC ***
## country_codeGRD ***
## country_codeGTM ***
## country_codeGUY ***
## country_codeHND ***
## country_codeHRV
## country_codeHTI ***
## country_codeHUN *
## country_codeIDN
## country_codeIND **
## country_codeIRL ***
## country_codeIRN ***
## country_codeIRQ ***
## country_codeISL ***
## country_codeISR ***
## country_codeITA ***
## country_codeJAM ***
## country_codeJOR ***
## country_codeJPN ***
## country_codeKAZ ***
## country_codeKEN *
## country_codeKGZ ***
## country_codeKHM *
## country_codeKIR
## country_codeKNA .
## country_codeKOR ***
## country_codeKWT ***
## country_codeLAO
## country_codeLBN ***
## country_codeLBR **
## country_codeLBY ***
## country_codeLCA ***
## country_codeLIE ***
## country_codeLKA
## country_codeLSO *
## country_codeLTU
## country_codeLUX ***
## country_codeLVA .
## country_codeMAR .
## country_codeMDA **
## country_codeMDG **
## country_codeMDV ***
## country_codeMEX ***
## country_codeMHL ***
## country_codeMKD
## country_codeMLI **
## country_codeMLT ***
## country_codeMMR *
## country_codeMNE ***
## country_codeMNG ***
## country_codeMOZ **
## country_codeMRT
## country_codeMUS ***
## country_codeMWI **
## country_codeMYS ***
## country_codeNAM
## country_codeNER **
## country_codeNGA
## country_codeNIC ***
## country_codeNLD ***
## country_codeNOR ***
## country_codeNPL
## country_codeNRU ***
## country_codeNZL ***
## country_codeOMN ***
## country_codePAK .
## country_codePAN ***
## country_codePER ***
## country_codePHL
## country_codePLW ***
## country_codePNG
## country_codePOL ***
## country_codePRK ***
## country_codePRT *
## country_codePRY ***
## country_codeQAT ***
## country_codeROU
## country_codeRUS ***
## country_codeRWA **
## country_codeSAU ***
## country_codeSDN *
## country_codeSEN .
## country_codeSGP ***
## country_codeSLB
## country_codeSLE **
## country_codeSLV ***
## country_codeSOM **
## country_codeSRB ***
## country_codeSSD **
## country_codeSTP .
## country_codeSUR ***
## country_codeSVK ***
## country_codeSVN ***
## country_codeSWE **
## country_codeSWZ
## country_codeSYC ***
## country_codeSYR ***
## country_codeTCD **
## country_codeTGO *
## country_codeTHA ***
## country_codeTJK ***
## country_codeTKM ***
## country_codeTLS *
## country_codeTON
## country_codeTTO ***
## country_codeTUN ***
## country_codeTUR *
## country_codeTUV
## country_codeTZA **
## country_codeUGA **
## country_codeUKR ***
## country_codeURY ***
## country_codeUSA
## country_codeUZB
## country_codeVCT ***
## country_codeVEN
## country_codeVNM
## country_codeVUT
## country_codeWSM
## country_codeYEM
## country_codeZAF ***
## country_codeZMB *
## country_codeZWE
## year:RegionEurope & Central Asia ***
## year:RegionLatin America & Caribbean
## year:RegionMiddle East & North Africa *
## year:RegionNorth America ***
## year:RegionSouth Asia
## year:RegionSub-Saharan Africa **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.261 on 5506 degrees of freedom
## Multiple R-squared: 0.9496, Adjusted R-squared: 0.9478
## F-statistic: 526.1 on 197 and 5506 DF, p-value: < 2.2e-16ggplot(df_long, aes(year, value, color = Region)) +
geom_line(stat = "summary", fun = mean)
model <- lm(value ~ year * Region, data = df_long)par(mfrow = c(2, 2))
plot(model)
ggplot(data.frame(residuals = resid(model)), aes(x = residuals)) +
geom_histogram(bins = 30) +
labs(title = "Residual Distribution")
ggplot(data.frame(
fitted = fitted(model),
resid = resid(model)
), aes(x = fitted, y = resid)) +
geom_point(alpha = 0.4) +
geom_hline(yintercept = 0, color = "red") +
labs(title = "Residuals vs Fitted Values")
plot(model, which = 5)
model <- lm(value ~ year * Region, data = df_long)library(lmtest)## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numericbptest(model)##
## studentized Breusch-Pagan test
##
## data: model
## BP = 776.75, df = 13, p-value < 2.2e-16library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:purrr':
##
## somevif(model)## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif## GVIF Df GVIF^(1/(2*Df))
## year 6.543057e+00 1 2.55794
## Region 2.425154e+28 6 231.95030
## year:Region 2.424993e+28 6 231.94902model1 <- lm(value ~ year + Region, data = df_long)
model2 <- lm(value ~ year * Region, data = df_long)
AIC(model1, model2)## df AIC
## model1 9 33450.89
## model2 15 33432.67summary(model1)$adj.r.squared## [1] 0.3228851summary(model2)$adj.r.squared## [1] 0.3257531##The regression analysis shows a strong relationship between the indicator value, time, and regional classification. The Breusch–Pagan test indicates heteroskedasticity, suggesting non-constant error variance. However, coefficient estimates remain valid. Multicollinearity is high for region and interaction terms due to model specification involving interaction effects. Despite this, the model remains useful for capturing heterogeneous regional time trends. Model comparison using AIC will determine whether the interaction specification provides a better fit than the simpler model.
#HW: Read about variable selection method
library(MASS)##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## selectdf_long <- df_long %>%
filter(!is.na(value)) %>%
mutate(
Region = as.factor(Region),
country_code = as.factor(country_code)
)full_model <- lm(value ~ year + Region + country_code, data = df_long)
summary(full_model)##
## Call:
## lm(formula = value ~ year + Region + country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0575 -0.2872 -0.0097 0.2343 12.5902
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.978134 4.015321 -0.742 0.458305
## year 0.001979 0.002000 0.990 0.322381
## RegionEurope & Central Asia 3.455601 0.336623 10.265 < 2e-16 ***
## RegionLatin America & Caribbean 4.347631 0.336623 12.915 < 2e-16 ***
## RegionMiddle East & North Africa -0.183460 0.336623 -0.545 0.585774
## RegionNorth America 17.162696 0.336623 50.985 < 2e-16 ***
## RegionSouth Asia -0.871810 0.336623 -2.590 0.009627 **
## RegionSub-Saharan Africa 0.060905 0.336623 0.181 0.856429
## country_codeAGO -0.149129 0.336623 -0.443 0.657773
## country_codeALB -3.193489 0.336623 -9.487 < 2e-16 ***
## country_codeAND 2.336934 0.336623 6.942 4.30e-12 ***
## country_codeARE 24.251033 0.336623 72.042 < 2e-16 ***
## country_codeARG -1.601866 0.336623 -4.759 2.00e-06 ***
## country_codeARM -2.625059 0.336623 -7.798 7.46e-15 ***
## country_codeATG -0.096989 0.336623 -0.288 0.773263
## country_codeAUS 15.838322 0.336623 47.051 < 2e-16 ***
## country_codeAUT 3.547344 0.336623 10.538 < 2e-16 ***
## country_codeAZE -0.527146 0.336623 -1.566 0.117410
## country_codeBDI -1.014670 0.336623 -3.014 0.002588 **
## country_codeBEL 5.689041 0.336623 16.900 < 2e-16 ***
## country_codeBEN -0.710719 0.336623 -2.111 0.034790 *
## country_codeBFA -0.934278 0.336623 -2.775 0.005531 **
## country_codeBGD 0.152622 0.336623 0.453 0.650286
## country_codeBGR 1.779374 0.336623 5.286 1.30e-07 ***
## country_codeBHR 21.677024 0.336623 64.395 < 2e-16 ***
## country_codeBHS 2.237421 0.336623 6.647 3.29e-11 ***
## country_codeBIH 0.049109 0.336623 0.146 0.884017
## country_codeBLR 1.857654 0.336623 5.518 3.58e-08 ***
## country_codeBLZ -3.378674 0.336623 -10.037 < 2e-16 ***
## country_codeBOL -3.924199 0.336623 -11.658 < 2e-16 ***
## country_codeBRA -3.493048 0.336623 -10.377 < 2e-16 ***
## country_codeBRB -0.173706 0.336623 -0.516 0.605859
## country_codeBRN 15.073230 0.336623 44.778 < 2e-16 ***
## country_codeBTN 0.478625 0.336623 1.422 0.155129
## country_codeBWA 1.359778 0.336623 4.039 5.43e-05 ***
## country_codeCAF -0.998843 0.336623 -2.967 0.003018 **
## country_codeCAN -2.230251 0.336623 -6.625 3.79e-11 ***
## country_codeCHE 1.304351 0.336623 3.875 0.000108 ***
## country_codeCHL -1.725420 0.336623 -5.126 3.07e-07 ***
## country_codeCHN 3.561359 0.336623 10.580 < 2e-16 ***
## country_codeCIV -0.719756 0.336623 -2.138 0.032547 *
## country_codeCMR -0.697514 0.336623 -2.072 0.038303 *
## country_codeCOD -1.004572 0.336623 -2.984 0.002855 **
## country_codeCOG 0.108490 0.336623 0.322 0.747246
## country_codeCOL -3.823909 0.336623 -11.360 < 2e-16 ***
## country_codeCOM -0.831300 0.336623 -2.470 0.013559 *
## country_codeCPV -0.311615 0.336623 -0.926 0.354638
## country_codeCRI -3.899059 0.336623 -11.583 < 2e-16 ***
## country_codeCUB -2.937519 0.336623 -8.726 < 2e-16 ***
## country_codeCYP 2.474145 0.336623 7.350 2.27e-13 ***
## country_codeCZE 6.860147 0.336623 20.379 < 2e-16 ***
## country_codeDEU 5.496587 0.336623 16.329 < 2e-16 ***
## country_codeDJI -0.288340 0.336623 -0.857 0.391723
## country_codeDMA -3.179410 0.336623 -9.445 < 2e-16 ***
## country_codeDNK 4.892993 0.336623 14.536 < 2e-16 ***
## country_codeDOM -3.358059 0.336623 -9.976 < 2e-16 ***
## country_codeDZA 2.216244 0.336623 6.584 5.01e-11 ***
## country_codeECU -3.272187 0.336623 -9.721 < 2e-16 ***
## country_codeEGY 1.226015 0.336623 3.642 0.000273 ***
## country_codeERI -0.810629 0.342588 -2.366 0.018006 *
## country_codeESP 1.853119 0.336623 5.505 3.86e-08 ***
## country_codeEST 8.481126 0.336623 25.195 < 2e-16 ***
## country_codeETH -0.970977 0.336623 -2.884 0.003936 **
## country_codeFIN 6.139156 0.336623 18.237 < 2e-16 ***
## country_codeFJI 0.289341 0.336623 0.860 0.390081
## country_codeFRA 1.172116 0.336623 3.482 0.000502 ***
## country_codeFSM 0.300584 0.342588 0.877 0.380312
## country_codeGAB 2.959318 0.336623 8.791 < 2e-16 ***
## country_codeGBR 3.717187 0.336623 11.043 < 2e-16 ***
## country_codeGEO -2.382585 0.336623 -7.078 1.65e-12 ***
## country_codeGHA -0.667153 0.336623 -1.982 0.047540 *
## country_codeGIN -0.846581 0.336623 -2.515 0.011934 *
## country_codeGMB -0.827194 0.336623 -2.457 0.014028 *
## country_codeGNB -0.891169 0.336623 -2.647 0.008135 **
## country_codeGNQ 4.988095 0.336623 14.818 < 2e-16 ***
## country_codeGRC 3.335677 0.336623 9.909 < 2e-16 ***
## country_codeGRD -2.856660 0.336623 -8.486 < 2e-16 ***
## country_codeGTM -4.538142 0.336623 -13.481 < 2e-16 ***
## country_codeGUY -3.039007 0.336623 -9.028 < 2e-16 ***
## country_codeHND -4.464766 0.336623 -13.263 < 2e-16 ***
## country_codeHRV -0.267895 0.336623 -0.796 0.426165
## country_codeHTI -5.135783 0.336623 -15.257 < 2e-16 ***
## country_codeHUN 0.786905 0.336623 2.338 0.019441 *
## country_codeIDN 0.524591 0.336623 1.558 0.119198
## country_codeIND 1.009405 0.336623 2.999 0.002724 **
## country_codeIRL 4.929810 0.336623 14.645 < 2e-16 ***
## country_codeIRN 5.253277 0.336623 15.606 < 2e-16 ***
## country_codeIRQ 3.047444 0.336623 9.053 < 2e-16 ***
## country_codeISL 2.502198 0.336623 7.433 1.22e-13 ***
## country_codeISR 7.600754 0.336623 22.579 < 2e-16 ***
## country_codeITA 2.491582 0.336623 7.402 1.55e-13 ***
## country_codeJAM -2.058446 0.336623 -6.115 1.03e-09 ***
## country_codeJOR 2.135598 0.336623 6.344 2.41e-10 ***
## country_codeJPN 8.236514 0.336623 24.468 < 2e-16 ***
## country_codeKAZ 7.406422 0.336623 22.002 < 2e-16 ***
## country_codeKEN -0.779122 0.336623 -2.315 0.020676 *
## country_codeKGZ -2.812972 0.336623 -8.356 < 2e-16 ***
## country_codeKHM -0.671830 0.336623 -1.996 0.046006 *
## country_codeKIR -0.485786 0.336623 -1.443 0.149045
## country_codeKNA -0.539886 0.336623 -1.604 0.108809
## country_codeKOR 8.935886 0.336623 26.546 < 2e-16 ***
## country_codeKWT 23.128712 0.345857 66.874 < 2e-16 ***
## country_codeLAO -0.375489 0.336623 -1.115 0.264703
## country_codeLBN 3.005570 0.336623 8.929 < 2e-16 ***
## country_codeLBR -0.846016 0.336623 -2.513 0.011991 *
## country_codeLBY 7.409509 0.336623 22.011 < 2e-16 ***
## country_codeLCA -2.516674 0.336623 -7.476 8.85e-14 ***
## country_codeLIE 1.493511 0.336623 4.437 9.31e-06 ***
## country_codeLKA 0.511548 0.336623 1.520 0.128658
## country_codeLSO -0.807198 0.336623 -2.398 0.016521 *
## country_codeLTU -0.227876 0.336623 -0.677 0.498469
## country_codeLUX 17.115382 0.336623 50.844 < 2e-16 ***
## country_codeLVA -0.545676 0.336623 -1.621 0.105069
## country_codeMAR 0.574110 0.336623 1.705 0.088158 .
## country_codeMDA -0.849227 0.336623 -2.523 0.011671 *
## country_codeMDG -0.947529 0.336623 -2.815 0.004898 **
## country_codeMDV 2.045187 0.336623 6.076 1.32e-09 ***
## country_codeMEX -1.554325 0.336623 -4.617 3.97e-06 ***
## country_codeMHL 1.338496 0.342588 3.907 9.46e-05 ***
## country_codeMKD -0.159153 0.336623 -0.473 0.636380
## country_codeMLI -0.918747 0.342583 -2.682 0.007344 **
## country_codeMLT 4.977278 0.336623 14.786 < 2e-16 ***
## country_codeMMR -0.751099 0.336623 -2.231 0.025703 *
## country_codeMNE -1.288668 0.336623 -3.828 0.000131 ***
## country_codeMNG 4.077466 0.336623 12.113 < 2e-16 ***
## country_codeMOZ -0.928235 0.336623 -2.757 0.005844 **
## country_codeMRT -0.443590 0.336623 -1.318 0.187638
## country_codeMUS 1.257699 0.336623 3.736 0.000189 ***
## country_codeMWI -0.983667 0.336623 -2.922 0.003490 **
## country_codeMYS 4.998246 0.336623 14.848 < 2e-16 ***
## country_codeNAM 0.288788 0.339514 0.851 0.395034
## country_codeNER -0.977335 0.336623 -2.903 0.003707 **
## country_codeNGA -0.354572 0.336623 -1.053 0.292241
## country_codeNIC -4.611968 0.336623 -13.701 < 2e-16 ***
## country_codeNLD 5.509005 0.336623 16.365 < 2e-16 ***
## country_codeNOR 3.267648 0.336623 9.707 < 2e-16 ***
## country_codeNPL 0.057174 0.336623 0.170 0.865138
## country_codeNRU 6.383634 0.336623 18.964 < 2e-16 ***
## country_codeNZL 6.278328 0.336623 18.651 < 2e-16 ***
## country_codeOMN 11.827954 0.336623 35.137 < 2e-16 ***
## country_codePAK 0.625074 0.336623 1.857 0.063380 .
## country_codePAN -3.274066 0.336623 -9.726 < 2e-16 ***
## country_codePER -4.050152 0.336623 -12.032 < 2e-16 ***
## country_codePHL -0.067768 0.336623 -0.201 0.840458
## country_codePLW 11.107680 0.342588 32.423 < 2e-16 ***
## country_codePNG -0.357869 0.336623 -1.063 0.287777
## country_codePOL 3.774521 0.336623 11.213 < 2e-16 ***
## country_codePRK 1.923428 0.336623 5.714 1.16e-08 ***
## country_codePRT 0.688551 0.336623 2.045 0.040857 *
## country_codePRY -4.558799 0.336623 -13.543 < 2e-16 ***
## country_codeQAT 37.561595 0.336623 111.583 < 2e-16 ***
## country_codeROU 0.072810 0.336623 0.216 0.828766
## country_codeRUS 7.024388 0.336623 20.867 < 2e-16 ***
## country_codeRWA -0.974817 0.336623 -2.896 0.003796 **
## country_codeSAU 12.972570 0.336623 38.537 < 2e-16 ***
## country_codeSDN -0.714536 0.336623 -2.123 0.033827 *
## country_codeSEN -0.573002 0.336623 -1.702 0.088774 .
## country_codeSGP 8.177079 0.336623 24.291 < 2e-16 ***
## country_codeSLB -0.397229 0.336623 -1.180 0.238036
## country_codeSLE -0.955713 0.336623 -2.839 0.004540 **
## country_codeSLV -4.355922 0.336623 -12.940 < 2e-16 ***
## country_codeSOM -0.988032 0.336623 -2.935 0.003348 **
## country_codeSRB 2.103883 0.336623 6.250 4.41e-10 ***
## country_codeSSD -0.938310 0.336623 -2.787 0.005331 **
## country_codeSTP -0.566654 0.336623 -1.683 0.092365 .
## country_codeSUR -1.365974 0.336623 -4.058 5.02e-05 ***
## country_codeSVK 2.646808 0.336623 7.863 4.49e-15 ***
## country_codeSVN 2.873068 0.336623 8.535 < 2e-16 ***
## country_codeSWE 0.973743 0.336623 2.893 0.003835 **
## country_codeSWZ -0.388129 0.336623 -1.153 0.248958
## country_codeSYC 3.216208 0.336623 9.554 < 2e-16 ***
## country_codeSYR 1.679334 0.336623 4.989 6.26e-07 ***
## country_codeTCD -0.965699 0.336623 -2.869 0.004136 **
## country_codeTGO -0.770586 0.336623 -2.289 0.022108 *
## country_codeTHA 2.062892 0.336623 6.128 9.50e-10 ***
## country_codeTJK -3.820133 0.336623 -11.348 < 2e-16 ***
## country_codeTKM 5.942881 0.336623 17.654 < 2e-16 ***
## country_codeTLS -0.693893 0.388884 -1.784 0.074427 .
## country_codeTON 0.162107 0.336623 0.482 0.630132
## country_codeTTO 6.654215 0.336623 19.768 < 2e-16 ***
## country_codeTUN 1.443936 0.336623 4.289 1.82e-05 ***
## country_codeTUR -0.772883 0.336623 -2.296 0.021714 *
## country_codeTUV 0.011769 0.336623 0.035 0.972112
## country_codeTZA -0.912599 0.336623 -2.711 0.006728 **
## country_codeUGA -0.969482 0.336623 -2.880 0.003992 **
## country_codeUKR 2.260593 0.336623 6.715 2.06e-11 ***
## country_codeURY -3.567963 0.336623 -10.599 < 2e-16 ***
## country_codeUSA NA NA NA NA
## country_codeUZB NA NA NA NA
## country_codeVCT -3.183533 0.336623 -9.457 < 2e-16 ***
## country_codeVEN NA NA NA NA
## country_codeVNM 0.251083 0.336623 0.746 0.455768
## country_codeVUT -0.502339 0.336623 -1.492 0.135681
## country_codeWSM NA NA NA NA
## country_codeYEM NA NA NA NA
## country_codeZAF 6.265177 0.336623 18.612 < 2e-16 ***
## country_codeZMB -0.798310 0.336623 -2.372 0.017749 *
## country_codeZWE NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.304 on 5512 degrees of freedom
## Multiple R-squared: 0.946, Adjusted R-squared: 0.9441
## F-statistic: 505.3 on 191 and 5512 DF, p-value: < 2.2e-16null_model <- lm(value ~ 1, data = df_long)
forward_model <- step(null_model,
scope = list(lower = null_model,
upper = full_model),
direction = "forward")## Start: AIC=19478.72
## value ~ 1
##
## Df Sum of Sq RSS AIC
## + country_code 190 164053 9371 3213.5
## + Region 6 56138 117286 17259.8
## <none> 173424 19478.7
## + year 1 2 173422 19480.6
##
## Step: AIC=3213.51
## value ~ country_code
##
## Df Sum of Sq RSS AIC
## <none> 9370.6 3213.5
## + year 1 1.6648 9368.9 3214.5summary(forward_model)##
## Call:
## lm(formula = value ~ country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0867 -0.2911 -0.0096 0.2372 12.5872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.116895 0.238028 0.491 0.623377
## country_codeAGO 0.783586 0.336623 2.328 0.019959 *
## country_codeALB 1.133921 0.336623 3.369 0.000761 ***
## country_codeAND 6.664344 0.336623 19.798 < 2e-16 ***
## country_codeARE 24.939382 0.336623 74.087 < 2e-16 ***
## country_codeARG 3.617575 0.336623 10.747 < 2e-16 ***
## country_codeARM 1.702351 0.336623 5.057 4.39e-07 ***
## country_codeATG 5.122452 0.336623 15.217 < 2e-16 ***
## country_codeAUS 16.710132 0.336623 49.641 < 2e-16 ***
## country_codeAUT 7.874754 0.336623 23.393 < 2e-16 ***
## country_codeAZE 3.800264 0.336623 11.289 < 2e-16 ***
## country_codeBDI -0.081955 0.336623 -0.243 0.807656
## country_codeBEL 10.016451 0.336623 29.756 < 2e-16 ***
## country_codeBEN 0.221996 0.336623 0.659 0.509616
## country_codeBFA -0.001563 0.336623 -0.005 0.996295
## country_codeBGD 0.152622 0.336623 0.453 0.650285
## country_codeBGR 6.106784 0.336623 18.141 < 2e-16 ***
## country_codeBHR 22.365373 0.336623 66.440 < 2e-16 ***
## country_codeBHS 7.456861 0.336623 22.152 < 2e-16 ***
## country_codeBIH 4.376519 0.336623 13.001 < 2e-16 ***
## country_codeBLR 6.185064 0.336623 18.374 < 2e-16 ***
## country_codeBLZ 1.840766 0.336623 5.468 4.74e-08 ***
## country_codeBOL 1.295242 0.336623 3.848 0.000121 ***
## country_codeBRA 1.726393 0.336623 5.129 3.02e-07 ***
## country_codeBRB 5.045735 0.336623 14.989 < 2e-16 ***
## country_codeBRN 15.945040 0.336623 47.368 < 2e-16 ***
## country_codeBTN 0.478625 0.336623 1.422 0.155128
## country_codeBWA 2.292493 0.336623 6.810 1.08e-11 ***
## country_codeCAF -0.066128 0.336623 -0.196 0.844268
## country_codeCAN 15.804255 0.336623 46.949 < 2e-16 ***
## country_codeCHE 5.631761 0.336623 16.730 < 2e-16 ***
## country_codeCHL 3.494021 0.336623 10.380 < 2e-16 ***
## country_codeCHN 4.433169 0.336623 13.170 < 2e-16 ***
## country_codeCIV 0.212959 0.336623 0.633 0.526999
## country_codeCMR 0.235201 0.336623 0.699 0.484764
## country_codeCOD -0.071857 0.336623 -0.213 0.830973
## country_codeCOG 1.041205 0.336623 3.093 0.001991 **
## country_codeCOL 1.395531 0.336623 4.146 3.44e-05 ***
## country_codeCOM 0.101415 0.336623 0.301 0.763219
## country_codeCPV 0.621100 0.336623 1.845 0.065078 .
## country_codeCRI 1.320382 0.336623 3.922 8.87e-05 ***
## country_codeCUB 2.281922 0.336623 6.779 1.34e-11 ***
## country_codeCYP 6.801556 0.336623 20.205 < 2e-16 ***
## country_codeCZE 11.187557 0.336623 33.235 < 2e-16 ***
## country_codeDEU 9.823997 0.336623 29.184 < 2e-16 ***
## country_codeDJI 0.400010 0.336623 1.188 0.234766
## country_codeDMA 2.040031 0.336623 6.060 1.45e-09 ***
## country_codeDNK 9.220403 0.336623 27.391 < 2e-16 ***
## country_codeDOM 1.861382 0.336623 5.530 3.36e-08 ***
## country_codeDZA 2.904593 0.336623 8.629 < 2e-16 ***
## country_codeECU 1.947254 0.336623 5.785 7.67e-09 ***
## country_codeEGY 1.914364 0.336623 5.687 1.36e-08 ***
## country_codeERI 0.124065 0.342581 0.362 0.717256
## country_codeESP 6.180530 0.336623 18.360 < 2e-16 ***
## country_codeEST 12.808536 0.336623 38.050 < 2e-16 ***
## country_codeETH -0.038262 0.336623 -0.114 0.909509
## country_codeFIN 10.466566 0.336623 31.093 < 2e-16 ***
## country_codeFJI 1.161150 0.336623 3.449 0.000566 ***
## country_codeFRA 5.499527 0.336623 16.337 < 2e-16 ***
## country_codeFSM 1.174372 0.342581 3.428 0.000612 ***
## country_codeGAB 3.892033 0.336623 11.562 < 2e-16 ***
## country_codeGBR 8.044597 0.336623 23.898 < 2e-16 ***
## country_codeGEO 1.944825 0.336623 5.777 8.00e-09 ***
## country_codeGHA 0.265562 0.336623 0.789 0.430205
## country_codeGIN 0.086134 0.336623 0.256 0.798056
## country_codeGMB 0.105521 0.336623 0.313 0.753935
## country_codeGNB 0.041546 0.336623 0.123 0.901778
## country_codeGNQ 5.920810 0.336623 17.589 < 2e-16 ***
## country_codeGRC 7.663088 0.336623 22.765 < 2e-16 ***
## country_codeGRD 2.362781 0.336623 7.019 2.50e-12 ***
## country_codeGTM 0.681299 0.336623 2.024 0.043026 *
## country_codeGUY 2.180433 0.336623 6.477 1.01e-10 ***
## country_codeHND 0.754675 0.336623 2.242 0.025007 *
## country_codeHRV 4.059515 0.336623 12.060 < 2e-16 ***
## country_codeHTI 0.083657 0.336623 0.249 0.803742
## country_codeHUN 5.114315 0.336623 15.193 < 2e-16 ***
## country_codeIDN 1.396401 0.336623 4.148 3.40e-05 ***
## country_codeIND 1.009405 0.336623 2.999 0.002724 **
## country_codeIRL 9.257220 0.336623 27.500 < 2e-16 ***
## country_codeIRN 5.941626 0.336623 17.651 < 2e-16 ***
## country_codeIRQ 3.735793 0.336623 11.098 < 2e-16 ***
## country_codeISL 6.829608 0.336623 20.289 < 2e-16 ***
## country_codeISR 8.289103 0.336623 24.624 < 2e-16 ***
## country_codeITA 6.818992 0.336623 20.257 < 2e-16 ***
## country_codeJAM 3.160994 0.336623 9.390 < 2e-16 ***
## country_codeJOR 2.823947 0.336623 8.389 < 2e-16 ***
## country_codeJPN 9.108324 0.336623 27.058 < 2e-16 ***
## country_codeKAZ 11.733833 0.336623 34.858 < 2e-16 ***
## country_codeKEN 0.153593 0.336623 0.456 0.648210
## country_codeKGZ 1.514438 0.336623 4.499 6.97e-06 ***
## country_codeKHM 0.199980 0.336623 0.594 0.552486
## country_codeKIR 0.386023 0.336623 1.147 0.251533
## country_codeKNA 4.679554 0.336623 13.901 < 2e-16 ***
## country_codeKOR 9.807696 0.336623 29.136 < 2e-16 ***
## country_codeKWT 23.819590 0.345847 68.873 < 2e-16 ***
## country_codeLAO 0.496320 0.336623 1.474 0.140428
## country_codeLBN 3.693919 0.336623 10.973 < 2e-16 ***
## country_codeLBR 0.086699 0.336623 0.258 0.796761
## country_codeLBY 8.097859 0.336623 24.056 < 2e-16 ***
## country_codeLCA 2.702766 0.336623 8.029 1.19e-15 ***
## country_codeLIE 5.820922 0.336623 17.292 < 2e-16 ***
## country_codeLKA 0.511548 0.336623 1.520 0.128657
## country_codeLSO 0.125517 0.336623 0.373 0.709258
## country_codeLTU 4.099534 0.336623 12.178 < 2e-16 ***
## country_codeLUX 21.442792 0.336623 63.700 < 2e-16 ***
## country_codeLVA 3.781735 0.336623 11.234 < 2e-16 ***
## country_codeMAR 1.262459 0.336623 3.750 0.000178 ***
## country_codeMDA 3.478183 0.336623 10.333 < 2e-16 ***
## country_codeMDG -0.014814 0.336623 -0.044 0.964899
## country_codeMDV 2.045187 0.336623 6.076 1.32e-09 ***
## country_codeMEX 3.665115 0.336623 10.888 < 2e-16 ***
## country_codeMHL 2.212284 0.342581 6.458 1.15e-10 ***
## country_codeMKD 4.168257 0.336623 12.383 < 2e-16 ***
## country_codeMLI 0.014816 0.342581 0.043 0.965505
## country_codeMLT 5.665627 0.336623 16.831 < 2e-16 ***
## country_codeMMR 0.120711 0.336623 0.359 0.719912
## country_codeMNE 3.038743 0.336623 9.027 < 2e-16 ***
## country_codeMNG 4.949275 0.336623 14.703 < 2e-16 ***
## country_codeMOZ 0.004480 0.336623 0.013 0.989381
## country_codeMRT 0.489125 0.336623 1.453 0.146271
## country_codeMUS 2.190414 0.336623 6.507 8.34e-11 ***
## country_codeMWI -0.050952 0.336623 -0.151 0.879695
## country_codeMYS 5.870056 0.336623 17.438 < 2e-16 ***
## country_codeNAM 1.222492 0.339512 3.601 0.000320 ***
## country_codeNER -0.044620 0.336623 -0.133 0.894554
## country_codeNGA 0.578143 0.336623 1.717 0.085948 .
## country_codeNIC 0.607472 0.336623 1.805 0.071191 .
## country_codeNLD 9.836416 0.336623 29.221 < 2e-16 ***
## country_codeNOR 7.595058 0.336623 22.563 < 2e-16 ***
## country_codeNPL 0.057174 0.336623 0.170 0.865137
## country_codeNRU 7.255444 0.336623 21.554 < 2e-16 ***
## country_codeNZL 7.150138 0.336623 21.241 < 2e-16 ***
## country_codeOMN 12.516303 0.336623 37.182 < 2e-16 ***
## country_codePAK 0.625074 0.336623 1.857 0.063379 .
## country_codePAN 1.945375 0.336623 5.779 7.92e-09 ***
## country_codePER 1.169289 0.336623 3.474 0.000517 ***
## country_codePHL 0.804042 0.336623 2.389 0.016948 *
## country_codePLW 11.981469 0.342581 34.974 < 2e-16 ***
## country_codePNG 0.513941 0.336623 1.527 0.126879
## country_codePOL 8.101931 0.336623 24.068 < 2e-16 ***
## country_codePRK 2.795238 0.336623 8.304 < 2e-16 ***
## country_codePRT 5.015961 0.336623 14.901 < 2e-16 ***
## country_codePRY 0.660641 0.336623 1.963 0.049748 *
## country_codeQAT 38.249945 0.336623 113.628 < 2e-16 ***
## country_codeROU 4.400220 0.336623 13.072 < 2e-16 ***
## country_codeRUS 11.351798 0.336623 33.723 < 2e-16 ***
## country_codeRWA -0.042102 0.336623 -0.125 0.900471
## country_codeSAU 13.660919 0.336623 40.582 < 2e-16 ***
## country_codeSDN 0.218179 0.336623 0.648 0.516921
## country_codeSEN 0.359713 0.336623 1.069 0.285299
## country_codeSGP 9.048889 0.336623 26.881 < 2e-16 ***
## country_codeSLB 0.474581 0.336623 1.410 0.158646
## country_codeSLE -0.022998 0.336623 -0.068 0.945534
## country_codeSLV 0.863518 0.336623 2.565 0.010337 *
## country_codeSOM -0.055317 0.336623 -0.164 0.869477
## country_codeSRB 6.431293 0.336623 19.105 < 2e-16 ***
## country_codeSSD -0.005595 0.336623 -0.017 0.986740
## country_codeSTP 0.366061 0.336623 1.087 0.276884
## country_codeSUR 3.853467 0.336623 11.447 < 2e-16 ***
## country_codeSVK 6.974218 0.336623 20.718 < 2e-16 ***
## country_codeSVN 7.200479 0.336623 21.390 < 2e-16 ***
## country_codeSWE 5.301153 0.336623 15.748 < 2e-16 ***
## country_codeSWZ 0.544586 0.336623 1.618 0.105765
## country_codeSYC 4.148923 0.336623 12.325 < 2e-16 ***
## country_codeSYR 2.367683 0.336623 7.034 2.26e-12 ***
## country_codeTCD -0.032984 0.336623 -0.098 0.921948
## country_codeTGO 0.162129 0.336623 0.482 0.630085
## country_codeTHA 2.934701 0.336623 8.718 < 2e-16 ***
## country_codeTJK 0.507277 0.336623 1.507 0.131878
## country_codeTKM 10.270291 0.336623 30.510 < 2e-16 ***
## country_codeTLS 0.189790 0.388699 0.488 0.625377
## country_codeTON 1.033917 0.336623 3.071 0.002141 **
## country_codeTTO 11.873655 0.336623 35.273 < 2e-16 ***
## country_codeTUN 2.132285 0.336623 6.334 2.57e-10 ***
## country_codeTUR 3.554528 0.336623 10.559 < 2e-16 ***
## country_codeTUV 0.883579 0.336623 2.625 0.008693 **
## country_codeTZA 0.020116 0.336623 0.060 0.952351
## country_codeUGA -0.036767 0.336623 -0.109 0.913030
## country_codeUKR 6.588003 0.336623 19.571 < 2e-16 ***
## country_codeURY 1.651478 0.336623 4.906 9.56e-07 ***
## country_codeUSA 18.034505 0.336623 53.575 < 2e-16 ***
## country_codeUZB 4.327410 0.336623 12.855 < 2e-16 ***
## country_codeVCT 2.035908 0.336623 6.048 1.56e-09 ***
## country_codeVEN 5.219441 0.336623 15.505 < 2e-16 ***
## country_codeVNM 1.122892 0.336623 3.336 0.000856 ***
## country_codeVUT 0.369471 0.336623 1.098 0.272436
## country_codeWSM 0.871810 0.336623 2.590 0.009626 **
## country_codeYEM 0.688349 0.336623 2.045 0.040915 *
## country_codeZAF 7.197892 0.336623 21.383 < 2e-16 ***
## country_codeZMB 0.134405 0.336623 0.399 0.689705
## country_codeZWE 0.932715 0.336623 2.771 0.005611 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.304 on 5513 degrees of freedom
## Multiple R-squared: 0.946, Adjusted R-squared: 0.9441
## F-statistic: 508 on 190 and 5513 DF, p-value: < 2.2e-16backward_model <- step(full_model,
direction = "backward")## Start: AIC=3214.49
## value ~ year + Region + country_code
##
##
## Step: AIC=3214.49
## value ~ year + country_code
##
## Df Sum of Sq RSS AIC
## - year 1 2 9371 3213.5
## <none> 9369 3214.5
## - country_code 190 164053 173422 19480.6
##
## Step: AIC=3213.51
## value ~ country_code
##
## Df Sum of Sq RSS AIC
## <none> 9371 3213.5
## - country_code 190 164053 173424 19478.7summary(backward_model)##
## Call:
## lm(formula = value ~ country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0867 -0.2911 -0.0096 0.2372 12.5872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.116895 0.238028 0.491 0.623377
## country_codeAGO 0.783586 0.336623 2.328 0.019959 *
## country_codeALB 1.133921 0.336623 3.369 0.000761 ***
## country_codeAND 6.664344 0.336623 19.798 < 2e-16 ***
## country_codeARE 24.939382 0.336623 74.087 < 2e-16 ***
## country_codeARG 3.617575 0.336623 10.747 < 2e-16 ***
## country_codeARM 1.702351 0.336623 5.057 4.39e-07 ***
## country_codeATG 5.122452 0.336623 15.217 < 2e-16 ***
## country_codeAUS 16.710132 0.336623 49.641 < 2e-16 ***
## country_codeAUT 7.874754 0.336623 23.393 < 2e-16 ***
## country_codeAZE 3.800264 0.336623 11.289 < 2e-16 ***
## country_codeBDI -0.081955 0.336623 -0.243 0.807656
## country_codeBEL 10.016451 0.336623 29.756 < 2e-16 ***
## country_codeBEN 0.221996 0.336623 0.659 0.509616
## country_codeBFA -0.001563 0.336623 -0.005 0.996295
## country_codeBGD 0.152622 0.336623 0.453 0.650285
## country_codeBGR 6.106784 0.336623 18.141 < 2e-16 ***
## country_codeBHR 22.365373 0.336623 66.440 < 2e-16 ***
## country_codeBHS 7.456861 0.336623 22.152 < 2e-16 ***
## country_codeBIH 4.376519 0.336623 13.001 < 2e-16 ***
## country_codeBLR 6.185064 0.336623 18.374 < 2e-16 ***
## country_codeBLZ 1.840766 0.336623 5.468 4.74e-08 ***
## country_codeBOL 1.295242 0.336623 3.848 0.000121 ***
## country_codeBRA 1.726393 0.336623 5.129 3.02e-07 ***
## country_codeBRB 5.045735 0.336623 14.989 < 2e-16 ***
## country_codeBRN 15.945040 0.336623 47.368 < 2e-16 ***
## country_codeBTN 0.478625 0.336623 1.422 0.155128
## country_codeBWA 2.292493 0.336623 6.810 1.08e-11 ***
## country_codeCAF -0.066128 0.336623 -0.196 0.844268
## country_codeCAN 15.804255 0.336623 46.949 < 2e-16 ***
## country_codeCHE 5.631761 0.336623 16.730 < 2e-16 ***
## country_codeCHL 3.494021 0.336623 10.380 < 2e-16 ***
## country_codeCHN 4.433169 0.336623 13.170 < 2e-16 ***
## country_codeCIV 0.212959 0.336623 0.633 0.526999
## country_codeCMR 0.235201 0.336623 0.699 0.484764
## country_codeCOD -0.071857 0.336623 -0.213 0.830973
## country_codeCOG 1.041205 0.336623 3.093 0.001991 **
## country_codeCOL 1.395531 0.336623 4.146 3.44e-05 ***
## country_codeCOM 0.101415 0.336623 0.301 0.763219
## country_codeCPV 0.621100 0.336623 1.845 0.065078 .
## country_codeCRI 1.320382 0.336623 3.922 8.87e-05 ***
## country_codeCUB 2.281922 0.336623 6.779 1.34e-11 ***
## country_codeCYP 6.801556 0.336623 20.205 < 2e-16 ***
## country_codeCZE 11.187557 0.336623 33.235 < 2e-16 ***
## country_codeDEU 9.823997 0.336623 29.184 < 2e-16 ***
## country_codeDJI 0.400010 0.336623 1.188 0.234766
## country_codeDMA 2.040031 0.336623 6.060 1.45e-09 ***
## country_codeDNK 9.220403 0.336623 27.391 < 2e-16 ***
## country_codeDOM 1.861382 0.336623 5.530 3.36e-08 ***
## country_codeDZA 2.904593 0.336623 8.629 < 2e-16 ***
## country_codeECU 1.947254 0.336623 5.785 7.67e-09 ***
## country_codeEGY 1.914364 0.336623 5.687 1.36e-08 ***
## country_codeERI 0.124065 0.342581 0.362 0.717256
## country_codeESP 6.180530 0.336623 18.360 < 2e-16 ***
## country_codeEST 12.808536 0.336623 38.050 < 2e-16 ***
## country_codeETH -0.038262 0.336623 -0.114 0.909509
## country_codeFIN 10.466566 0.336623 31.093 < 2e-16 ***
## country_codeFJI 1.161150 0.336623 3.449 0.000566 ***
## country_codeFRA 5.499527 0.336623 16.337 < 2e-16 ***
## country_codeFSM 1.174372 0.342581 3.428 0.000612 ***
## country_codeGAB 3.892033 0.336623 11.562 < 2e-16 ***
## country_codeGBR 8.044597 0.336623 23.898 < 2e-16 ***
## country_codeGEO 1.944825 0.336623 5.777 8.00e-09 ***
## country_codeGHA 0.265562 0.336623 0.789 0.430205
## country_codeGIN 0.086134 0.336623 0.256 0.798056
## country_codeGMB 0.105521 0.336623 0.313 0.753935
## country_codeGNB 0.041546 0.336623 0.123 0.901778
## country_codeGNQ 5.920810 0.336623 17.589 < 2e-16 ***
## country_codeGRC 7.663088 0.336623 22.765 < 2e-16 ***
## country_codeGRD 2.362781 0.336623 7.019 2.50e-12 ***
## country_codeGTM 0.681299 0.336623 2.024 0.043026 *
## country_codeGUY 2.180433 0.336623 6.477 1.01e-10 ***
## country_codeHND 0.754675 0.336623 2.242 0.025007 *
## country_codeHRV 4.059515 0.336623 12.060 < 2e-16 ***
## country_codeHTI 0.083657 0.336623 0.249 0.803742
## country_codeHUN 5.114315 0.336623 15.193 < 2e-16 ***
## country_codeIDN 1.396401 0.336623 4.148 3.40e-05 ***
## country_codeIND 1.009405 0.336623 2.999 0.002724 **
## country_codeIRL 9.257220 0.336623 27.500 < 2e-16 ***
## country_codeIRN 5.941626 0.336623 17.651 < 2e-16 ***
## country_codeIRQ 3.735793 0.336623 11.098 < 2e-16 ***
## country_codeISL 6.829608 0.336623 20.289 < 2e-16 ***
## country_codeISR 8.289103 0.336623 24.624 < 2e-16 ***
## country_codeITA 6.818992 0.336623 20.257 < 2e-16 ***
## country_codeJAM 3.160994 0.336623 9.390 < 2e-16 ***
## country_codeJOR 2.823947 0.336623 8.389 < 2e-16 ***
## country_codeJPN 9.108324 0.336623 27.058 < 2e-16 ***
## country_codeKAZ 11.733833 0.336623 34.858 < 2e-16 ***
## country_codeKEN 0.153593 0.336623 0.456 0.648210
## country_codeKGZ 1.514438 0.336623 4.499 6.97e-06 ***
## country_codeKHM 0.199980 0.336623 0.594 0.552486
## country_codeKIR 0.386023 0.336623 1.147 0.251533
## country_codeKNA 4.679554 0.336623 13.901 < 2e-16 ***
## country_codeKOR 9.807696 0.336623 29.136 < 2e-16 ***
## country_codeKWT 23.819590 0.345847 68.873 < 2e-16 ***
## country_codeLAO 0.496320 0.336623 1.474 0.140428
## country_codeLBN 3.693919 0.336623 10.973 < 2e-16 ***
## country_codeLBR 0.086699 0.336623 0.258 0.796761
## country_codeLBY 8.097859 0.336623 24.056 < 2e-16 ***
## country_codeLCA 2.702766 0.336623 8.029 1.19e-15 ***
## country_codeLIE 5.820922 0.336623 17.292 < 2e-16 ***
## country_codeLKA 0.511548 0.336623 1.520 0.128657
## country_codeLSO 0.125517 0.336623 0.373 0.709258
## country_codeLTU 4.099534 0.336623 12.178 < 2e-16 ***
## country_codeLUX 21.442792 0.336623 63.700 < 2e-16 ***
## country_codeLVA 3.781735 0.336623 11.234 < 2e-16 ***
## country_codeMAR 1.262459 0.336623 3.750 0.000178 ***
## country_codeMDA 3.478183 0.336623 10.333 < 2e-16 ***
## country_codeMDG -0.014814 0.336623 -0.044 0.964899
## country_codeMDV 2.045187 0.336623 6.076 1.32e-09 ***
## country_codeMEX 3.665115 0.336623 10.888 < 2e-16 ***
## country_codeMHL 2.212284 0.342581 6.458 1.15e-10 ***
## country_codeMKD 4.168257 0.336623 12.383 < 2e-16 ***
## country_codeMLI 0.014816 0.342581 0.043 0.965505
## country_codeMLT 5.665627 0.336623 16.831 < 2e-16 ***
## country_codeMMR 0.120711 0.336623 0.359 0.719912
## country_codeMNE 3.038743 0.336623 9.027 < 2e-16 ***
## country_codeMNG 4.949275 0.336623 14.703 < 2e-16 ***
## country_codeMOZ 0.004480 0.336623 0.013 0.989381
## country_codeMRT 0.489125 0.336623 1.453 0.146271
## country_codeMUS 2.190414 0.336623 6.507 8.34e-11 ***
## country_codeMWI -0.050952 0.336623 -0.151 0.879695
## country_codeMYS 5.870056 0.336623 17.438 < 2e-16 ***
## country_codeNAM 1.222492 0.339512 3.601 0.000320 ***
## country_codeNER -0.044620 0.336623 -0.133 0.894554
## country_codeNGA 0.578143 0.336623 1.717 0.085948 .
## country_codeNIC 0.607472 0.336623 1.805 0.071191 .
## country_codeNLD 9.836416 0.336623 29.221 < 2e-16 ***
## country_codeNOR 7.595058 0.336623 22.563 < 2e-16 ***
## country_codeNPL 0.057174 0.336623 0.170 0.865137
## country_codeNRU 7.255444 0.336623 21.554 < 2e-16 ***
## country_codeNZL 7.150138 0.336623 21.241 < 2e-16 ***
## country_codeOMN 12.516303 0.336623 37.182 < 2e-16 ***
## country_codePAK 0.625074 0.336623 1.857 0.063379 .
## country_codePAN 1.945375 0.336623 5.779 7.92e-09 ***
## country_codePER 1.169289 0.336623 3.474 0.000517 ***
## country_codePHL 0.804042 0.336623 2.389 0.016948 *
## country_codePLW 11.981469 0.342581 34.974 < 2e-16 ***
## country_codePNG 0.513941 0.336623 1.527 0.126879
## country_codePOL 8.101931 0.336623 24.068 < 2e-16 ***
## country_codePRK 2.795238 0.336623 8.304 < 2e-16 ***
## country_codePRT 5.015961 0.336623 14.901 < 2e-16 ***
## country_codePRY 0.660641 0.336623 1.963 0.049748 *
## country_codeQAT 38.249945 0.336623 113.628 < 2e-16 ***
## country_codeROU 4.400220 0.336623 13.072 < 2e-16 ***
## country_codeRUS 11.351798 0.336623 33.723 < 2e-16 ***
## country_codeRWA -0.042102 0.336623 -0.125 0.900471
## country_codeSAU 13.660919 0.336623 40.582 < 2e-16 ***
## country_codeSDN 0.218179 0.336623 0.648 0.516921
## country_codeSEN 0.359713 0.336623 1.069 0.285299
## country_codeSGP 9.048889 0.336623 26.881 < 2e-16 ***
## country_codeSLB 0.474581 0.336623 1.410 0.158646
## country_codeSLE -0.022998 0.336623 -0.068 0.945534
## country_codeSLV 0.863518 0.336623 2.565 0.010337 *
## country_codeSOM -0.055317 0.336623 -0.164 0.869477
## country_codeSRB 6.431293 0.336623 19.105 < 2e-16 ***
## country_codeSSD -0.005595 0.336623 -0.017 0.986740
## country_codeSTP 0.366061 0.336623 1.087 0.276884
## country_codeSUR 3.853467 0.336623 11.447 < 2e-16 ***
## country_codeSVK 6.974218 0.336623 20.718 < 2e-16 ***
## country_codeSVN 7.200479 0.336623 21.390 < 2e-16 ***
## country_codeSWE 5.301153 0.336623 15.748 < 2e-16 ***
## country_codeSWZ 0.544586 0.336623 1.618 0.105765
## country_codeSYC 4.148923 0.336623 12.325 < 2e-16 ***
## country_codeSYR 2.367683 0.336623 7.034 2.26e-12 ***
## country_codeTCD -0.032984 0.336623 -0.098 0.921948
## country_codeTGO 0.162129 0.336623 0.482 0.630085
## country_codeTHA 2.934701 0.336623 8.718 < 2e-16 ***
## country_codeTJK 0.507277 0.336623 1.507 0.131878
## country_codeTKM 10.270291 0.336623 30.510 < 2e-16 ***
## country_codeTLS 0.189790 0.388699 0.488 0.625377
## country_codeTON 1.033917 0.336623 3.071 0.002141 **
## country_codeTTO 11.873655 0.336623 35.273 < 2e-16 ***
## country_codeTUN 2.132285 0.336623 6.334 2.57e-10 ***
## country_codeTUR 3.554528 0.336623 10.559 < 2e-16 ***
## country_codeTUV 0.883579 0.336623 2.625 0.008693 **
## country_codeTZA 0.020116 0.336623 0.060 0.952351
## country_codeUGA -0.036767 0.336623 -0.109 0.913030
## country_codeUKR 6.588003 0.336623 19.571 < 2e-16 ***
## country_codeURY 1.651478 0.336623 4.906 9.56e-07 ***
## country_codeUSA 18.034505 0.336623 53.575 < 2e-16 ***
## country_codeUZB 4.327410 0.336623 12.855 < 2e-16 ***
## country_codeVCT 2.035908 0.336623 6.048 1.56e-09 ***
## country_codeVEN 5.219441 0.336623 15.505 < 2e-16 ***
## country_codeVNM 1.122892 0.336623 3.336 0.000856 ***
## country_codeVUT 0.369471 0.336623 1.098 0.272436
## country_codeWSM 0.871810 0.336623 2.590 0.009626 **
## country_codeYEM 0.688349 0.336623 2.045 0.040915 *
## country_codeZAF 7.197892 0.336623 21.383 < 2e-16 ***
## country_codeZMB 0.134405 0.336623 0.399 0.689705
## country_codeZWE 0.932715 0.336623 2.771 0.005611 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.304 on 5513 degrees of freedom
## Multiple R-squared: 0.946, Adjusted R-squared: 0.9441
## F-statistic: 508 on 190 and 5513 DF, p-value: < 2.2e-16step_model <- stepAIC(full_model, direction = "both")## Start: AIC=3214.49
## value ~ year + Region + country_code
##
##
## Step: AIC=3214.49
## value ~ year + country_code
##
## Df Sum of Sq RSS AIC
## - year 1 2 9371 3213.5
## <none> 9369 3214.5
## - country_code 190 164053 173422 19480.6
##
## Step: AIC=3213.51
## value ~ country_code
##
## Df Sum of Sq RSS AIC
## <none> 9371 3213.5
## + year 1 2 9369 3214.5
## - country_code 190 164053 173424 19478.7summary(step_model)##
## Call:
## lm(formula = value ~ country_code, data = df_long)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.0867 -0.2911 -0.0096 0.2372 12.5872
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.116895 0.238028 0.491 0.623377
## country_codeAGO 0.783586 0.336623 2.328 0.019959 *
## country_codeALB 1.133921 0.336623 3.369 0.000761 ***
## country_codeAND 6.664344 0.336623 19.798 < 2e-16 ***
## country_codeARE 24.939382 0.336623 74.087 < 2e-16 ***
## country_codeARG 3.617575 0.336623 10.747 < 2e-16 ***
## country_codeARM 1.702351 0.336623 5.057 4.39e-07 ***
## country_codeATG 5.122452 0.336623 15.217 < 2e-16 ***
## country_codeAUS 16.710132 0.336623 49.641 < 2e-16 ***
## country_codeAUT 7.874754 0.336623 23.393 < 2e-16 ***
## country_codeAZE 3.800264 0.336623 11.289 < 2e-16 ***
## country_codeBDI -0.081955 0.336623 -0.243 0.807656
## country_codeBEL 10.016451 0.336623 29.756 < 2e-16 ***
## country_codeBEN 0.221996 0.336623 0.659 0.509616
## country_codeBFA -0.001563 0.336623 -0.005 0.996295
## country_codeBGD 0.152622 0.336623 0.453 0.650285
## country_codeBGR 6.106784 0.336623 18.141 < 2e-16 ***
## country_codeBHR 22.365373 0.336623 66.440 < 2e-16 ***
## country_codeBHS 7.456861 0.336623 22.152 < 2e-16 ***
## country_codeBIH 4.376519 0.336623 13.001 < 2e-16 ***
## country_codeBLR 6.185064 0.336623 18.374 < 2e-16 ***
## country_codeBLZ 1.840766 0.336623 5.468 4.74e-08 ***
## country_codeBOL 1.295242 0.336623 3.848 0.000121 ***
## country_codeBRA 1.726393 0.336623 5.129 3.02e-07 ***
## country_codeBRB 5.045735 0.336623 14.989 < 2e-16 ***
## country_codeBRN 15.945040 0.336623 47.368 < 2e-16 ***
## country_codeBTN 0.478625 0.336623 1.422 0.155128
## country_codeBWA 2.292493 0.336623 6.810 1.08e-11 ***
## country_codeCAF -0.066128 0.336623 -0.196 0.844268
## country_codeCAN 15.804255 0.336623 46.949 < 2e-16 ***
## country_codeCHE 5.631761 0.336623 16.730 < 2e-16 ***
## country_codeCHL 3.494021 0.336623 10.380 < 2e-16 ***
## country_codeCHN 4.433169 0.336623 13.170 < 2e-16 ***
## country_codeCIV 0.212959 0.336623 0.633 0.526999
## country_codeCMR 0.235201 0.336623 0.699 0.484764
## country_codeCOD -0.071857 0.336623 -0.213 0.830973
## country_codeCOG 1.041205 0.336623 3.093 0.001991 **
## country_codeCOL 1.395531 0.336623 4.146 3.44e-05 ***
## country_codeCOM 0.101415 0.336623 0.301 0.763219
## country_codeCPV 0.621100 0.336623 1.845 0.065078 .
## country_codeCRI 1.320382 0.336623 3.922 8.87e-05 ***
## country_codeCUB 2.281922 0.336623 6.779 1.34e-11 ***
## country_codeCYP 6.801556 0.336623 20.205 < 2e-16 ***
## country_codeCZE 11.187557 0.336623 33.235 < 2e-16 ***
## country_codeDEU 9.823997 0.336623 29.184 < 2e-16 ***
## country_codeDJI 0.400010 0.336623 1.188 0.234766
## country_codeDMA 2.040031 0.336623 6.060 1.45e-09 ***
## country_codeDNK 9.220403 0.336623 27.391 < 2e-16 ***
## country_codeDOM 1.861382 0.336623 5.530 3.36e-08 ***
## country_codeDZA 2.904593 0.336623 8.629 < 2e-16 ***
## country_codeECU 1.947254 0.336623 5.785 7.67e-09 ***
## country_codeEGY 1.914364 0.336623 5.687 1.36e-08 ***
## country_codeERI 0.124065 0.342581 0.362 0.717256
## country_codeESP 6.180530 0.336623 18.360 < 2e-16 ***
## country_codeEST 12.808536 0.336623 38.050 < 2e-16 ***
## country_codeETH -0.038262 0.336623 -0.114 0.909509
## country_codeFIN 10.466566 0.336623 31.093 < 2e-16 ***
## country_codeFJI 1.161150 0.336623 3.449 0.000566 ***
## country_codeFRA 5.499527 0.336623 16.337 < 2e-16 ***
## country_codeFSM 1.174372 0.342581 3.428 0.000612 ***
## country_codeGAB 3.892033 0.336623 11.562 < 2e-16 ***
## country_codeGBR 8.044597 0.336623 23.898 < 2e-16 ***
## country_codeGEO 1.944825 0.336623 5.777 8.00e-09 ***
## country_codeGHA 0.265562 0.336623 0.789 0.430205
## country_codeGIN 0.086134 0.336623 0.256 0.798056
## country_codeGMB 0.105521 0.336623 0.313 0.753935
## country_codeGNB 0.041546 0.336623 0.123 0.901778
## country_codeGNQ 5.920810 0.336623 17.589 < 2e-16 ***
## country_codeGRC 7.663088 0.336623 22.765 < 2e-16 ***
## country_codeGRD 2.362781 0.336623 7.019 2.50e-12 ***
## country_codeGTM 0.681299 0.336623 2.024 0.043026 *
## country_codeGUY 2.180433 0.336623 6.477 1.01e-10 ***
## country_codeHND 0.754675 0.336623 2.242 0.025007 *
## country_codeHRV 4.059515 0.336623 12.060 < 2e-16 ***
## country_codeHTI 0.083657 0.336623 0.249 0.803742
## country_codeHUN 5.114315 0.336623 15.193 < 2e-16 ***
## country_codeIDN 1.396401 0.336623 4.148 3.40e-05 ***
## country_codeIND 1.009405 0.336623 2.999 0.002724 **
## country_codeIRL 9.257220 0.336623 27.500 < 2e-16 ***
## country_codeIRN 5.941626 0.336623 17.651 < 2e-16 ***
## country_codeIRQ 3.735793 0.336623 11.098 < 2e-16 ***
## country_codeISL 6.829608 0.336623 20.289 < 2e-16 ***
## country_codeISR 8.289103 0.336623 24.624 < 2e-16 ***
## country_codeITA 6.818992 0.336623 20.257 < 2e-16 ***
## country_codeJAM 3.160994 0.336623 9.390 < 2e-16 ***
## country_codeJOR 2.823947 0.336623 8.389 < 2e-16 ***
## country_codeJPN 9.108324 0.336623 27.058 < 2e-16 ***
## country_codeKAZ 11.733833 0.336623 34.858 < 2e-16 ***
## country_codeKEN 0.153593 0.336623 0.456 0.648210
## country_codeKGZ 1.514438 0.336623 4.499 6.97e-06 ***
## country_codeKHM 0.199980 0.336623 0.594 0.552486
## country_codeKIR 0.386023 0.336623 1.147 0.251533
## country_codeKNA 4.679554 0.336623 13.901 < 2e-16 ***
## country_codeKOR 9.807696 0.336623 29.136 < 2e-16 ***
## country_codeKWT 23.819590 0.345847 68.873 < 2e-16 ***
## country_codeLAO 0.496320 0.336623 1.474 0.140428
## country_codeLBN 3.693919 0.336623 10.973 < 2e-16 ***
## country_codeLBR 0.086699 0.336623 0.258 0.796761
## country_codeLBY 8.097859 0.336623 24.056 < 2e-16 ***
## country_codeLCA 2.702766 0.336623 8.029 1.19e-15 ***
## country_codeLIE 5.820922 0.336623 17.292 < 2e-16 ***
## country_codeLKA 0.511548 0.336623 1.520 0.128657
## country_codeLSO 0.125517 0.336623 0.373 0.709258
## country_codeLTU 4.099534 0.336623 12.178 < 2e-16 ***
## country_codeLUX 21.442792 0.336623 63.700 < 2e-16 ***
## country_codeLVA 3.781735 0.336623 11.234 < 2e-16 ***
## country_codeMAR 1.262459 0.336623 3.750 0.000178 ***
## country_codeMDA 3.478183 0.336623 10.333 < 2e-16 ***
## country_codeMDG -0.014814 0.336623 -0.044 0.964899
## country_codeMDV 2.045187 0.336623 6.076 1.32e-09 ***
## country_codeMEX 3.665115 0.336623 10.888 < 2e-16 ***
## country_codeMHL 2.212284 0.342581 6.458 1.15e-10 ***
## country_codeMKD 4.168257 0.336623 12.383 < 2e-16 ***
## country_codeMLI 0.014816 0.342581 0.043 0.965505
## country_codeMLT 5.665627 0.336623 16.831 < 2e-16 ***
## country_codeMMR 0.120711 0.336623 0.359 0.719912
## country_codeMNE 3.038743 0.336623 9.027 < 2e-16 ***
## country_codeMNG 4.949275 0.336623 14.703 < 2e-16 ***
## country_codeMOZ 0.004480 0.336623 0.013 0.989381
## country_codeMRT 0.489125 0.336623 1.453 0.146271
## country_codeMUS 2.190414 0.336623 6.507 8.34e-11 ***
## country_codeMWI -0.050952 0.336623 -0.151 0.879695
## country_codeMYS 5.870056 0.336623 17.438 < 2e-16 ***
## country_codeNAM 1.222492 0.339512 3.601 0.000320 ***
## country_codeNER -0.044620 0.336623 -0.133 0.894554
## country_codeNGA 0.578143 0.336623 1.717 0.085948 .
## country_codeNIC 0.607472 0.336623 1.805 0.071191 .
## country_codeNLD 9.836416 0.336623 29.221 < 2e-16 ***
## country_codeNOR 7.595058 0.336623 22.563 < 2e-16 ***
## country_codeNPL 0.057174 0.336623 0.170 0.865137
## country_codeNRU 7.255444 0.336623 21.554 < 2e-16 ***
## country_codeNZL 7.150138 0.336623 21.241 < 2e-16 ***
## country_codeOMN 12.516303 0.336623 37.182 < 2e-16 ***
## country_codePAK 0.625074 0.336623 1.857 0.063379 .
## country_codePAN 1.945375 0.336623 5.779 7.92e-09 ***
## country_codePER 1.169289 0.336623 3.474 0.000517 ***
## country_codePHL 0.804042 0.336623 2.389 0.016948 *
## country_codePLW 11.981469 0.342581 34.974 < 2e-16 ***
## country_codePNG 0.513941 0.336623 1.527 0.126879
## country_codePOL 8.101931 0.336623 24.068 < 2e-16 ***
## country_codePRK 2.795238 0.336623 8.304 < 2e-16 ***
## country_codePRT 5.015961 0.336623 14.901 < 2e-16 ***
## country_codePRY 0.660641 0.336623 1.963 0.049748 *
## country_codeQAT 38.249945 0.336623 113.628 < 2e-16 ***
## country_codeROU 4.400220 0.336623 13.072 < 2e-16 ***
## country_codeRUS 11.351798 0.336623 33.723 < 2e-16 ***
## country_codeRWA -0.042102 0.336623 -0.125 0.900471
## country_codeSAU 13.660919 0.336623 40.582 < 2e-16 ***
## country_codeSDN 0.218179 0.336623 0.648 0.516921
## country_codeSEN 0.359713 0.336623 1.069 0.285299
## country_codeSGP 9.048889 0.336623 26.881 < 2e-16 ***
## country_codeSLB 0.474581 0.336623 1.410 0.158646
## country_codeSLE -0.022998 0.336623 -0.068 0.945534
## country_codeSLV 0.863518 0.336623 2.565 0.010337 *
## country_codeSOM -0.055317 0.336623 -0.164 0.869477
## country_codeSRB 6.431293 0.336623 19.105 < 2e-16 ***
## country_codeSSD -0.005595 0.336623 -0.017 0.986740
## country_codeSTP 0.366061 0.336623 1.087 0.276884
## country_codeSUR 3.853467 0.336623 11.447 < 2e-16 ***
## country_codeSVK 6.974218 0.336623 20.718 < 2e-16 ***
## country_codeSVN 7.200479 0.336623 21.390 < 2e-16 ***
## country_codeSWE 5.301153 0.336623 15.748 < 2e-16 ***
## country_codeSWZ 0.544586 0.336623 1.618 0.105765
## country_codeSYC 4.148923 0.336623 12.325 < 2e-16 ***
## country_codeSYR 2.367683 0.336623 7.034 2.26e-12 ***
## country_codeTCD -0.032984 0.336623 -0.098 0.921948
## country_codeTGO 0.162129 0.336623 0.482 0.630085
## country_codeTHA 2.934701 0.336623 8.718 < 2e-16 ***
## country_codeTJK 0.507277 0.336623 1.507 0.131878
## country_codeTKM 10.270291 0.336623 30.510 < 2e-16 ***
## country_codeTLS 0.189790 0.388699 0.488 0.625377
## country_codeTON 1.033917 0.336623 3.071 0.002141 **
## country_codeTTO 11.873655 0.336623 35.273 < 2e-16 ***
## country_codeTUN 2.132285 0.336623 6.334 2.57e-10 ***
## country_codeTUR 3.554528 0.336623 10.559 < 2e-16 ***
## country_codeTUV 0.883579 0.336623 2.625 0.008693 **
## country_codeTZA 0.020116 0.336623 0.060 0.952351
## country_codeUGA -0.036767 0.336623 -0.109 0.913030
## country_codeUKR 6.588003 0.336623 19.571 < 2e-16 ***
## country_codeURY 1.651478 0.336623 4.906 9.56e-07 ***
## country_codeUSA 18.034505 0.336623 53.575 < 2e-16 ***
## country_codeUZB 4.327410 0.336623 12.855 < 2e-16 ***
## country_codeVCT 2.035908 0.336623 6.048 1.56e-09 ***
## country_codeVEN 5.219441 0.336623 15.505 < 2e-16 ***
## country_codeVNM 1.122892 0.336623 3.336 0.000856 ***
## country_codeVUT 0.369471 0.336623 1.098 0.272436
## country_codeWSM 0.871810 0.336623 2.590 0.009626 **
## country_codeYEM 0.688349 0.336623 2.045 0.040915 *
## country_codeZAF 7.197892 0.336623 21.383 < 2e-16 ***
## country_codeZMB 0.134405 0.336623 0.399 0.689705
## country_codeZWE 0.932715 0.336623 2.771 0.005611 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.304 on 5513 degrees of freedom
## Multiple R-squared: 0.946, Adjusted R-squared: 0.9441
## F-statistic: 508 on 190 and 5513 DF, p-value: < 2.2e-16AIC(full_model, forward_model, backward_model, step_model)## df AIC
## full_model 193 19403.74
## forward_model 192 19402.76
## backward_model 192 19402.76
## step_model 192 19402.76best_model <- step_model
par(mfrow = c(2,2))
plot(best_model)