MA317 Group Coursework
data03.online
2024-04-29
Task: Each group should investigate the response variable life expectancy in the year 2019 and use other indicators (predictor variables) of the dataset to develop a linear model which explains the life expectancies in 2019. The report should propose a model which explains life expectancy in the world for 2019. You should also discuss if and how the model can be used to predict life expectancies for countries which have not provided data on life expectancy. You should use R in order to conduct your statistical analysis. You should include the R code as part of an Appendix of your report which should run without errors. You should submit your report in pdf format. Zipped folders e.g. .zip or 7z will not be accepted. When answering the questions you should explain the statistical methods used and justify your answers. In order to analyse life expectancy complete the following tasks: 1. Analyse using descriptive statistics (both graphical and numerical representations) and R the Life Expectancy data1.csv dataset. [14 marks] 2. Many predictors in the dataset contain missing values. Is deleting predictor variables with many missing values an appropriate method to deal with missing values? Choose a method to deal with the missing values and then employ this method to the life expectancy data. Justify your choice. Additionally, there are some countries (cases) where the value of Life expectancy is missing. Explain how you will handle this problem. [14 marks] 3. Collinearity increases the variance of the estimators and hence, reduces the adequacy of the model. When collinearity is present, how do you solve this problem? Investigate collinearity between the predictor variables in the LifeExpectancyData1.csv dataset. [14 marks] 4. To understand better life expectancy and the factors that affect it, suggest the best model which predicts life expectancy in 2019. Evaluate the suggested model. [14 marks] 1 of 4 5. Using the same dataset (Life Expectancy data1.csv) and using the new additional feature Continent, employ an appropriate experimental design to study differences of average life expectancies across the continents : Asia, Europe, North America, South America, Africa, Australia/Oceania. Justify your choice of experimental design and methods. [14 marks]
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(car)## Loading required package: carData##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recodedf <- read.csv("Life_Expectancy_Data1.csv")
df[is.na(df)]<-0
summary(df)## Country.Name Country.Code Continent SP.DYN.LE00.IN
## Length:217 Length:217 Length:217 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.:64.83
## Mode :character Mode :character Mode :character Median :73.18
## Mean :66.54
## 3rd Qu.:77.86
## Max. :85.08
## EG.ELC.ACCS.ZS NY.ADJ.NNTY.KD.ZG NY.ADJ.NNTY.PC.KD.ZG SH.HIV.INCD.14
## Min. : 0.00 Min. :-30.792 Min. :-32.5432 Min. : 0.0
## 1st Qu.: 84.05 1st Qu.: 0.000 1st Qu.: 0.0000 1st Qu.: 0.0
## Median :100.00 Median : 1.105 Median : 0.3211 Median : 0.0
## Mean : 86.07 Mean : 2.563 Mean : 1.6907 Mean : 684.5
## 3rd Qu.:100.00 3rd Qu.: 4.825 3rd Qu.: 3.6564 3rd Qu.: 200.0
## Max. :100.00 Max. : 50.172 Max. : 47.2518 Max. :20000.0
## SE.PRM.UNER SE.PRM.CUAT.ZS SE.TER.CUAT.BA.ZS SP.DYN.IMRT.IN
## Min. : 0 Min. : 0.00 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 3.50
## Median : 187 Median : 0.00 Median : 0.000 Median :12.30
## Mean : 53644 Mean : 14.56 Mean : 3.478 Mean :18.65
## 3rd Qu.: 12511 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.:29.80
## Max. :1712650 Max. :100.00 Max. :46.631 Max. :82.40
## SE.PRM.CMPT.ZS SE.ADT.LITR.ZS FR.INR.RINR SP.POP.GROW
## Min. : 0.00 Min. : 0.0 Min. :-78.518 Min. :-1.6095
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.: 0.3716
## Median : 78.94 Median : 0.0 Median : 0.000 Median : 1.0914
## Mean : 54.88 Mean : 10.6 Mean : 3.239 Mean : 1.1862
## 3rd Qu.: 98.55 3rd Qu.: 0.0 3rd Qu.: 6.428 3rd Qu.: 1.9537
## Max. :120.45 Max. :100.0 Max. : 39.877 Max. : 4.4687
## EN.POP.DNST SP.POP.TOTL SH.XPD.CHEX.PC.CD SH.XPD.CHEX.GD.ZS
## Min. : 0.00 Min. :0.000e+00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 37.86 1st Qu.:7.631e+05 1st Qu.: 51.16 1st Qu.: 3.595
## Median : 92.72 Median :6.546e+06 Median : 264.06 Median : 5.618
## Mean : 443.99 Mean :3.528e+07 Mean : 980.32 Mean : 5.653
## 3rd Qu.: 231.99 3rd Qu.:2.537e+07 3rd Qu.: 972.61 3rd Qu.: 7.753
## Max. :19466.44 Max. :1.408e+09 Max. :10921.01 Max. :23.962
## SL.UEM.TOTL.NE.ZS NY.GDP.MKTP.KD.ZG NY.GDP.PCAP.CD SP.DYN.CBRT.IN
## Min. : 0.000 Min. :-11.143 Min. : 0 Min. : 0.00
## 1st Qu.: 0.000 1st Qu.: 0.734 1st Qu.: 1856 1st Qu.:10.17
## Median : 2.930 Median : 2.375 Median : 6223 Median :16.40
## Mean : 4.279 Mean : 2.630 Mean : 17577 Mean :18.21
## 3rd Qu.: 6.310 3rd Qu.: 4.601 3rd Qu.: 20660 3rd Qu.:25.88
## Max. :28.470 Max. : 19.536 Max. :189487 Max. :45.64
## EG.FEC.RNEW.ZS SH.HIV.INCD SH.H2O.SMDW.ZS SI.POV.LMIC
## Min. :0 Min. : 0 Min. : 0.00 Min. : 0.000
## 1st Qu.:0 1st Qu.: 0 1st Qu.: 0.00 1st Qu.: 0.000
## Median :0 Median : 200 Median : 29.69 Median : 0.000
## Mean :0 Mean : 4503 Mean : 43.47 Mean : 1.027
## 3rd Qu.:0 3rd Qu.: 1800 3rd Qu.: 94.25 3rd Qu.: 0.000
## Max. :0 Max. :210000 Max. :100.00 Max. :63.800
## SE.COM.DURS
## Min. : 0.000
## 1st Qu.: 8.000
## Median :10.000
## Mean : 9.051
## 3rd Qu.:12.000
## Max. :17.000str(df)## 'data.frame': 217 obs. of 29 variables:
## $ Country.Name : chr "Afghanistan" "Albania" "Algeria" "American Samoa" ...
## $ Country.Code : chr "AFG" "ALB" "DZA" "ASM" ...
## $ Continent : chr "Asia" "Europe" "Africa" "Australia/Oceania" ...
## $ SP.DYN.LE00.IN : num 64.8 78.6 76.9 0 0 ...
## $ EG.ELC.ACCS.ZS : num 97.7 100 99.5 0 100 ...
## $ NY.ADJ.NNTY.KD.ZG : num 0 0.146 2.938 0 0 ...
## $ NY.ADJ.NNTY.PC.KD.ZG: num 0 0.574 0.966 0 0 ...
## $ SH.HIV.INCD.14 : num 200 0 200 0 0 6200 0 100 100 0 ...
## $ SE.PRM.UNER : num 0 3359 12511 0 0 ...
## $ SE.PRM.CUAT.ZS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SE.TER.CUAT.BA.ZS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SP.DYN.IMRT.IN : num 46.4 8.6 20 0 2.5 49.9 5.6 8.1 10.2 0 ...
## $ SE.PRM.CMPT.ZS : num 84.3 103.3 101.4 0 0 ...
## $ SE.ADT.LITR.ZS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FR.INR.RINR : num 0 4.99 8.51 0 0 ...
## $ SP.POP.GROW : num 2.313 -0.426 1.934 -0.269 0.179 ...
## $ EN.POP.DNST : num 58.3 104.2 18.1 276.6 164.1 ...
## $ SP.POP.TOTL : num 38041757 2854191 43053054 55312 77146 ...
## $ SH.XPD.CHEX.PC.CD : num 65.8 0 248.2 0 2744.2 ...
## $ SH.XPD.CHEX.GD.ZS : num 13.24 0 6.24 0 6.71 ...
## $ SL.UEM.TOTL.NE.ZS : num 0 11.5 0 0 0 ...
## $ NY.GDP.MKTP.KD.ZG : num 3.912 2.113 1 -0.488 2.016 ...
## $ NY.GDP.PCAP.CD : num 494 5396 3990 11715 40897 ...
## $ SP.DYN.CBRT.IN : num 31.8 11.6 23.6 0 7 ...
## $ EG.FEC.RNEW.ZS : num 0 0 0 0 0 0 0 0 0 0 ...
## $ SH.HIV.INCD : num 1300 100 1400 0 0 16000 0 5300 500 0 ...
## $ SH.H2O.SMDW.ZS : num 26.8 70.7 73.1 98.4 90.6 ...
## $ SI.POV.LMIC : num 0 0 0 0 0 0 0 4.9 9.8 0 ...
## $ SE.COM.DURS : num 9 9 10 0 10 6 11 14 12 13 ...df$Country.Name<-as.factor(df$Country.Name)
df$Continent<-as.factor(df$Continent)
df<-df[,-2]
df %>%select( Continent, SP.DYN.LE00.IN) %>%
group_by(Continent) %>%
summarise_all(list(mean = mean), na.rm=TRUE) %>% arrange(desc(mean))## # A tibble: 6 × 2
## Continent mean
## <fct> <dbl>
## 1 South America 75.1
## 2 Asia 74.6
## 3 Europe 71.0
## 4 Africa 64.1
## 5 North America 58.3
## 6 Australia/Oceania 50.3df %>%select( Continent, SP.DYN.LE00.IN) %>%
group_by(Continent) %>%
summarise_all(list(mean = mean), na.rm=TRUE) %>% arrange(desc(mean)) %>%
ggplot() +
aes(x = Continent,weight = mean) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Continent",
y = "Life Expectancy",
title = "Comparison of Life expectancy at
birth, total (years) with Continent")
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "South America") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))## # A tibble: 12 × 3
## # Groups: Country.Name [12]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 South America Chile 80.2
## 2 South America Uruguay 77.9
## 3 South America Colombia 77.3
## 4 South America Ecuador 77.0
## 5 South America Peru 76.7
## 6 South America Argentina 76.7
## 7 South America Brazil 75.9
## 8 South America Paraguay 74.3
## 9 South America Venezuela, RB 72.1
## 10 South America Suriname 71.7
## 11 South America Bolivia 71.5
## 12 South America Guyana 69.9df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "South America") %>%
group_by(Country.Name) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in South America")
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Asia") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN)) %>% top_n(20)## Selecting by SP.DYN.LE00.IN## # A tibble: 50 × 3
## # Groups: Country.Name [50]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 Asia Hong Kong SAR, China 85.1
## 2 Asia Japan 84.4
## 3 Asia Macao SAR, China 84.2
## 4 Asia Singapore 83.5
## 5 Asia Korea, Rep. 83.2
## 6 Asia Israel 82.8
## 7 Asia Cyprus 81.0
## 8 Asia Qatar 80.2
## 9 Asia Lebanon 78.9
## 10 Asia Maldives 78.9
## # ℹ 40 more rowsdf %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Asia") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))%>% top_n(20) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in Asia")## Selecting by SP.DYN.LE00.IN
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Europe") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN)) %>% top_n(20)## Selecting by SP.DYN.LE00.IN## # A tibble: 48 × 3
## # Groups: Country.Name [48]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 Europe Switzerland 83.7
## 2 Europe Spain 83.5
## 3 Europe Italy 83.2
## 4 Europe Channel Islands 83.1
## 5 Europe Liechtenstein 83.0
## 6 Europe Sweden 83.0
## 7 Europe Norway 82.9
## 8 Europe Faroe Islands 82.7
## 9 Europe Malta 82.6
## 10 Europe France 82.6
## # ℹ 38 more rowsdf %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Europe") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))%>% top_n(20) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in Europe")## Selecting by SP.DYN.LE00.IN
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Africa") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN)) %>% top_n(20)## Selecting by SP.DYN.LE00.IN## # A tibble: 54 × 3
## # Groups: Country.Name [54]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 Africa Algeria 76.9
## 2 Africa Tunisia 76.7
## 3 Africa Morocco 76.7
## 4 Africa Mauritius 74.2
## 5 Africa Seychelles 73.9
## 6 Africa Cabo Verde 73.0
## 7 Africa Libya 72.9
## 8 Africa Egypt, Arab Rep. 72.0
## 9 Africa Sao Tome and Principe 70.4
## 10 Africa Botswana 69.6
## # ℹ 44 more rowsdf %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Africa") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))%>% top_n(20) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in Africa")## Selecting by SP.DYN.LE00.IN
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "North America") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN)) %>% top_n(20)## Selecting by SP.DYN.LE00.IN## # A tibble: 34 × 3
## # Groups: Country.Name [34]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 North America Canada 82.0
## 2 North America Bermuda 81.9
## 3 North America Costa Rica 80.3
## 4 North America St. Martin (French part) 80.0
## 5 North America Puerto Rico 79.9
## 6 North America Virgin Islands (U.S.) 79.7
## 7 North America Barbados 79.2
## 8 North America Cuba 78.8
## 9 North America United States 78.8
## 10 North America Panama 78.5
## # ℹ 24 more rowsdf %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "North America") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))%>% top_n(20) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in North America")## Selecting by SP.DYN.LE00.IN
df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Australia/Oceania") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN)) %>% top_n(20)## Selecting by SP.DYN.LE00.IN## # A tibble: 19 × 3
## # Groups: Country.Name [19]
## Continent Country.Name SP.DYN.LE00.IN
## <fct> <fct> <dbl>
## 1 Australia/Oceania Australia 82.9
## 2 Australia/Oceania New Zealand 81.7
## 3 Australia/Oceania Guam 80.1
## 4 Australia/Oceania French Polynesia 77.7
## 5 Australia/Oceania New Caledonia 77.6
## 6 Australia/Oceania Samoa 73.3
## 7 Australia/Oceania Solomon Islands 73.0
## 8 Australia/Oceania Tonga 70.9
## 9 Australia/Oceania Vanuatu 70.5
## 10 Australia/Oceania Kiribati 68.4
## 11 Australia/Oceania Micronesia, Fed. Sts. 67.9
## 12 Australia/Oceania Fiji 67.4
## 13 Australia/Oceania Papua New Guinea 64.5
## 14 Australia/Oceania American Samoa 0
## 15 Australia/Oceania Marshall Islands 0
## 16 Australia/Oceania Nauru 0
## 17 Australia/Oceania Northern Mariana Islands 0
## 18 Australia/Oceania Palau 0
## 19 Australia/Oceania Tuvalu 0df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
filter(Continent== "Australia/Oceania") %>%
group_by(Country.Name) %>% arrange(desc(SP.DYN.LE00.IN))%>% top_n(20) %>%
ggplot() +
aes(x = Country.Name,weight = SP.DYN.LE00.IN) +
geom_bar() + scale_fill_hue(direction = 1) +
coord_flip() +theme_minimal()+labs(x = "Country Name",
y = "Life Expectancy",
title = "Comparison of Life expectancy at birth,
total (years) in Australia/Oceania")## Selecting by SP.DYN.LE00.IN
a<-df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
group_by(Continent) %>% slice_max(SP.DYN.LE00.IN)
b<-df %>%select( Continent,Country.Name, SP.DYN.LE00.IN) %>%
group_by(Continent) %>% slice_min(SP.DYN.LE00.IN)
a<-rbind(a,b)
ggplot(a) +
aes(x = Country.Name,fill = Continent, weight = SP.DYN.LE00.IN
) +geom_bar() +scale_fill_hue(direction = 1) +
coord_flip() + theme_minimal()+theme(legend.position="bottom")
df <- read.csv("Life_Expectancy_Data1.csv")
df$SP.DYN.LE00.IN[is.na(df$SP.DYN.LE00.IN)] <- mean(df$SP.DYN.LE00.IN, na.rm = TRUE)
df[is.na(df)]<-0
x<-df[,-c(1:3,25)]
cor(df[,-c(1:3,25)])## SP.DYN.LE00.IN EG.ELC.ACCS.ZS NY.ADJ.NNTY.KD.ZG
## SP.DYN.LE00.IN 1.000000000 0.76210680 -0.142910265
## EG.ELC.ACCS.ZS 0.762106796 1.00000000 -0.128416559
## NY.ADJ.NNTY.KD.ZG -0.142910265 -0.12841656 1.000000000
## NY.ADJ.NNTY.PC.KD.ZG -0.056162396 -0.03028956 0.979376349
## SH.HIV.INCD.14 -0.446203601 -0.42648913 0.087064518
## SE.PRM.UNER -0.256622339 -0.32785330 0.125010790
## SE.PRM.CUAT.ZS 0.215687072 0.20552726 0.016104012
## SE.TER.CUAT.BA.ZS 0.270740889 0.20643856 0.004606558
## SP.DYN.IMRT.IN -0.897536450 -0.77029705 0.178174868
## SE.PRM.CMPT.ZS 0.254779994 0.23916812 0.173400190
## SE.ADT.LITR.ZS 0.025665668 0.11301552 0.026432587
## FR.INR.RINR 0.010883701 0.03707547 0.076887435
## SP.POP.GROW -0.510730030 -0.53582925 0.152914099
## EN.POP.DNST 0.156256779 0.09378245 -0.068611664
## SP.POP.TOTL -0.007261648 0.04139386 0.053456099
## SH.XPD.CHEX.PC.CD 0.514664184 0.29064840 -0.043109963
## SH.XPD.CHEX.GD.ZS 0.142205661 0.11463776 0.049262821
## SL.UEM.TOTL.NE.ZS 0.128832358 0.15413000 -0.036879594
## NY.GDP.MKTP.KD.ZG -0.178888192 -0.08168349 0.588836455
## NY.GDP.PCAP.CD 0.512433298 0.32146150 -0.118813043
## SP.DYN.CBRT.IN -0.773668023 -0.72101631 0.221088770
## SH.HIV.INCD -0.276740764 -0.22233468 0.003064933
## SH.H2O.SMDW.ZS 0.562674930 0.39719985 -0.094495706
## SI.POV.LMIC -0.052929097 -0.03327650 -0.007203884
## SE.COM.DURS 0.262130840 0.35921315 -0.026062482
## NY.ADJ.NNTY.PC.KD.ZG SH.HIV.INCD.14 SE.PRM.UNER
## SP.DYN.LE00.IN -0.056162396 -0.446203601 -0.256622339
## EG.ELC.ACCS.ZS -0.030289564 -0.426489132 -0.327853300
## NY.ADJ.NNTY.KD.ZG 0.979376349 0.087064518 0.125010790
## NY.ADJ.NNTY.PC.KD.ZG 1.000000000 0.016441256 0.060432454
## SH.HIV.INCD.14 0.016441256 1.000000000 0.324587555
## SE.PRM.UNER 0.060432454 0.324587555 1.000000000
## SE.PRM.CUAT.ZS 0.048854956 -0.056152689 0.018786885
## SE.TER.CUAT.BA.ZS 0.029828822 -0.092512725 -0.040564158
## SP.DYN.IMRT.IN 0.079410047 0.437707894 0.231833412
## SE.PRM.CMPT.ZS 0.169641996 -0.130004300 0.091049543
## SE.ADT.LITR.ZS 0.030979337 -0.005803928 0.064960517
## FR.INR.RINR 0.044304242 -0.003266622 -0.049280355
## SP.POP.GROW -0.008832112 0.347119666 0.278075448
## EN.POP.DNST -0.051531726 -0.052049273 -0.048907149
## SP.POP.TOTL 0.050762195 0.070763190 0.012513999
## SH.XPD.CHEX.PC.CD -0.022848088 -0.149894858 -0.081304866
## SH.XPD.CHEX.GD.ZS 0.064585263 -0.054259977 0.008203986
## SL.UEM.TOTL.NE.ZS -0.002452958 0.072924173 0.024317408
## NY.GDP.MKTP.KD.ZG 0.568636550 0.029707165 0.109136968
## NY.GDP.PCAP.CD -0.084400596 -0.171299544 -0.127957637
## SP.DYN.CBRT.IN 0.093976742 0.429349131 0.330169362
## SH.HIV.INCD -0.036949717 0.754966989 0.376347042
## SH.H2O.SMDW.ZS -0.040699986 -0.227881720 -0.165910563
## SI.POV.LMIC -0.005718627 0.087056251 0.105991834
## SE.COM.DURS 0.002191125 -0.129190459 -0.084787295
## SE.PRM.CUAT.ZS SE.TER.CUAT.BA.ZS SP.DYN.IMRT.IN
## SP.DYN.LE00.IN 0.21568707 0.270740889 -0.897536450
## EG.ELC.ACCS.ZS 0.20552726 0.206438564 -0.770297048
## NY.ADJ.NNTY.KD.ZG 0.01610401 0.004606558 0.178174868
## NY.ADJ.NNTY.PC.KD.ZG 0.04885496 0.029828822 0.079410047
## SH.HIV.INCD.14 -0.05615269 -0.092512725 0.437707894
## SE.PRM.UNER 0.01878689 -0.040564158 0.231833412
## SE.PRM.CUAT.ZS 1.00000000 0.750329267 -0.198892795
## SE.TER.CUAT.BA.ZS 0.75032927 1.000000000 -0.232458010
## SP.DYN.IMRT.IN -0.19889279 -0.232458010 1.000000000
## SE.PRM.CMPT.ZS 0.13077902 0.149201829 -0.209851628
## SE.ADT.LITR.ZS 0.57590047 0.452117947 -0.034411510
## FR.INR.RINR 0.02236831 -0.004049383 0.007352259
## SP.POP.GROW -0.23001122 -0.175342305 0.598182331
## EN.POP.DNST 0.02714290 0.002302600 -0.145610331
## SP.POP.TOTL -0.02163243 -0.045382539 0.027107184
## SH.XPD.CHEX.PC.CD 0.10211697 0.307751286 -0.367409327
## SH.XPD.CHEX.GD.ZS 0.09787465 0.176268758 -0.015888262
## SL.UEM.TOTL.NE.ZS 0.11149844 0.072319359 -0.096961532
## NY.GDP.MKTP.KD.ZG 0.01355567 0.019636195 0.212528659
## NY.GDP.PCAP.CD 0.04620446 0.161408769 -0.453428722
## SP.DYN.CBRT.IN -0.19214567 -0.208634697 0.817470114
## SH.HIV.INCD 0.07605194 -0.021144860 0.231153496
## SH.H2O.SMDW.ZS 0.19645745 0.209232909 -0.524703176
## SI.POV.LMIC 0.08773380 0.057060926 0.021349529
## SE.COM.DURS 0.15493797 0.148409003 -0.219257279
## SE.PRM.CMPT.ZS SE.ADT.LITR.ZS FR.INR.RINR SP.POP.GROW
## SP.DYN.LE00.IN 0.254779994 0.025665668 0.010883701 -0.510730030
## EG.ELC.ACCS.ZS 0.239168125 0.113015521 0.037075469 -0.535829248
## NY.ADJ.NNTY.KD.ZG 0.173400190 0.026432587 0.076887435 0.152914099
## NY.ADJ.NNTY.PC.KD.ZG 0.169641996 0.030979337 0.044304242 -0.008832112
## SH.HIV.INCD.14 -0.130004300 -0.005803928 -0.003266622 0.347119666
## SE.PRM.UNER 0.091049543 0.064960517 -0.049280355 0.278075448
## SE.PRM.CUAT.ZS 0.130779021 0.575900473 0.022368312 -0.230011218
## SE.TER.CUAT.BA.ZS 0.149201829 0.452117947 -0.004049383 -0.175342305
## SP.DYN.IMRT.IN -0.209851628 -0.034411510 0.007352259 0.598182331
## SE.PRM.CMPT.ZS 1.000000000 0.079713358 0.059859475 -0.021483774
## SE.ADT.LITR.ZS 0.079713358 1.000000000 0.081273251 -0.017439763
## FR.INR.RINR 0.059859475 0.081273251 1.000000000 0.120174771
## SP.POP.GROW -0.021483774 -0.017439763 0.120174771 1.000000000
## EN.POP.DNST 0.038814236 0.015301532 -0.015265318 -0.013068676
## SP.POP.TOTL -0.024439184 0.006225201 0.048872841 -0.028318345
## SH.XPD.CHEX.PC.CD 0.187678934 -0.094097434 -0.083782042 -0.182129566
## SH.XPD.CHEX.GD.ZS 0.232380298 -0.016526855 0.029063560 -0.104095969
## SL.UEM.TOTL.NE.ZS 0.130919946 0.180639536 -0.050424133 -0.177967657
## NY.GDP.MKTP.KD.ZG 0.154547908 0.081060424 0.221721299 0.217856451
## NY.GDP.PCAP.CD 0.057775008 -0.102056423 -0.111416728 -0.214110218
## SP.DYN.CBRT.IN -0.141807397 -0.007151255 0.076305330 0.730707788
## SH.HIV.INCD -0.009808778 0.141938861 0.015335516 0.185150355
## SH.H2O.SMDW.ZS 0.194050064 -0.007511715 -0.072686896 -0.324549792
## SI.POV.LMIC 0.077596070 0.133314885 -0.440532819 0.007249693
## SE.COM.DURS 0.184902534 0.115569005 -0.013032653 -0.241175497
## EN.POP.DNST SP.POP.TOTL SH.XPD.CHEX.PC.CD
## SP.DYN.LE00.IN 0.15625678 -0.0072616479 0.51466418
## EG.ELC.ACCS.ZS 0.09378245 0.0413938574 0.29064840
## NY.ADJ.NNTY.KD.ZG -0.06861166 0.0534560995 -0.04310996
## NY.ADJ.NNTY.PC.KD.ZG -0.05153173 0.0507621947 -0.02284809
## SH.HIV.INCD.14 -0.05204927 0.0707631898 -0.14989486
## SE.PRM.UNER -0.04890715 0.0125139990 -0.08130487
## SE.PRM.CUAT.ZS 0.02714290 -0.0216324293 0.10211697
## SE.TER.CUAT.BA.ZS 0.00230260 -0.0453825387 0.30775129
## SP.DYN.IMRT.IN -0.14561033 0.0271071837 -0.36740933
## SE.PRM.CMPT.ZS 0.03881424 -0.0244391840 0.18767893
## SE.ADT.LITR.ZS 0.01530153 0.0062252012 -0.09409743
## FR.INR.RINR -0.01526532 0.0488728406 -0.08378204
## SP.POP.GROW -0.01306868 -0.0283183449 -0.18212957
## EN.POP.DNST 1.00000000 -0.0274392727 0.02820049
## SP.POP.TOTL -0.02743927 1.0000000000 0.01833672
## SH.XPD.CHEX.PC.CD 0.02820049 0.0183367163 1.00000000
## SH.XPD.CHEX.GD.ZS -0.17735731 -0.0000488106 0.50901728
## SL.UEM.TOTL.NE.ZS -0.07672291 0.0322618898 0.05851973
## NY.GDP.MKTP.KD.ZG -0.04856852 0.0825563419 -0.07685633
## NY.GDP.PCAP.CD 0.44979650 -0.0588816414 0.56653754
## SP.DYN.CBRT.IN -0.18603715 -0.0073325442 -0.37279116
## SH.HIV.INCD -0.04539133 0.0691063411 -0.05692940
## SH.H2O.SMDW.ZS 0.17551390 -0.0804710086 0.46258692
## SI.POV.LMIC -0.03665684 0.0159407128 -0.07532069
## SE.COM.DURS 0.02526914 0.0267539070 0.24188112
## SH.XPD.CHEX.GD.ZS SL.UEM.TOTL.NE.ZS NY.GDP.MKTP.KD.ZG
## SP.DYN.LE00.IN 0.1422056608 0.128832358 -0.178888192
## EG.ELC.ACCS.ZS 0.1146377641 0.154129999 -0.081683486
## NY.ADJ.NNTY.KD.ZG 0.0492628208 -0.036879594 0.588836455
## NY.ADJ.NNTY.PC.KD.ZG 0.0645852631 -0.002452958 0.568636550
## SH.HIV.INCD.14 -0.0542599774 0.072924173 0.029707165
## SE.PRM.UNER 0.0082039865 0.024317408 0.109136968
## SE.PRM.CUAT.ZS 0.0978746503 0.111498443 0.013555672
## SE.TER.CUAT.BA.ZS 0.1762687583 0.072319359 0.019636195
## SP.DYN.IMRT.IN -0.0158882622 -0.096961532 0.212528659
## SE.PRM.CMPT.ZS 0.2323802979 0.130919946 0.154547908
## SE.ADT.LITR.ZS -0.0165268552 0.180639536 0.081060424
## FR.INR.RINR 0.0290635604 -0.050424133 0.221721299
## SP.POP.GROW -0.1040959686 -0.177967657 0.217856451
## EN.POP.DNST -0.1773573073 -0.076722907 -0.048568523
## SP.POP.TOTL -0.0000488106 0.032261890 0.082556342
## SH.XPD.CHEX.PC.CD 0.5090172832 0.058519728 -0.076856334
## SH.XPD.CHEX.GD.ZS 1.0000000000 0.194374585 0.058839395
## SL.UEM.TOTL.NE.ZS 0.1943745848 1.000000000 -0.017663622
## NY.GDP.MKTP.KD.ZG 0.0588393952 -0.017663622 1.000000000
## NY.GDP.PCAP.CD -0.0183278394 -0.086422962 -0.122073583
## SP.DYN.CBRT.IN -0.1208045073 -0.078919525 0.197439828
## SH.HIV.INCD 0.0527881586 0.248560596 -0.026119423
## SH.H2O.SMDW.ZS 0.1510553934 0.109781117 -0.138196980
## SI.POV.LMIC 0.0770675389 0.201930795 -0.130893634
## SE.COM.DURS 0.2685326658 0.063902825 -0.002794672
## NY.GDP.PCAP.CD SP.DYN.CBRT.IN SH.HIV.INCD SH.H2O.SMDW.ZS
## SP.DYN.LE00.IN 0.51243330 -0.773668023 -0.276740764 0.562674930
## EG.ELC.ACCS.ZS 0.32146150 -0.721016313 -0.222334679 0.397199848
## NY.ADJ.NNTY.KD.ZG -0.11881304 0.221088770 0.003064933 -0.094495706
## NY.ADJ.NNTY.PC.KD.ZG -0.08440060 0.093976742 -0.036949717 -0.040699986
## SH.HIV.INCD.14 -0.17129954 0.429349131 0.754966989 -0.227881720
## SE.PRM.UNER -0.12795764 0.330169362 0.376347042 -0.165910563
## SE.PRM.CUAT.ZS 0.04620446 -0.192145665 0.076051940 0.196457453
## SE.TER.CUAT.BA.ZS 0.16140877 -0.208634697 -0.021144860 0.209232909
## SP.DYN.IMRT.IN -0.45342872 0.817470114 0.231153496 -0.524703176
## SE.PRM.CMPT.ZS 0.05777501 -0.141807397 -0.009808778 0.194050064
## SE.ADT.LITR.ZS -0.10205642 -0.007151255 0.141938861 -0.007511715
## FR.INR.RINR -0.11141673 0.076305330 0.015335516 -0.072686896
## SP.POP.GROW -0.21411022 0.730707788 0.185150355 -0.324549792
## EN.POP.DNST 0.44979650 -0.186037151 -0.045391325 0.175513898
## SP.POP.TOTL -0.05888164 -0.007332544 0.069106341 -0.080471009
## SH.XPD.CHEX.PC.CD 0.56653754 -0.372791157 -0.056929400 0.462586918
## SH.XPD.CHEX.GD.ZS -0.01832784 -0.120804507 0.052788159 0.151055393
## SL.UEM.TOTL.NE.ZS -0.08642296 -0.078919525 0.248560596 0.109781117
## NY.GDP.MKTP.KD.ZG -0.12207358 0.197439828 -0.026119423 -0.138196980
## NY.GDP.PCAP.CD 1.00000000 -0.463579620 -0.111592544 0.432508796
## SP.DYN.CBRT.IN -0.46357962 1.000000000 0.230146870 -0.469135815
## SH.HIV.INCD -0.11159254 0.230146870 1.000000000 -0.165420992
## SH.H2O.SMDW.ZS 0.43250880 -0.469135815 -0.165420992 1.000000000
## SI.POV.LMIC -0.09520013 0.059076502 0.066545227 -0.040489372
## SE.COM.DURS 0.13713070 -0.279460293 -0.063901509 0.100802828
## SI.POV.LMIC SE.COM.DURS
## SP.DYN.LE00.IN -0.052929097 0.262130840
## EG.ELC.ACCS.ZS -0.033276501 0.359213146
## NY.ADJ.NNTY.KD.ZG -0.007203884 -0.026062482
## NY.ADJ.NNTY.PC.KD.ZG -0.005718627 0.002191125
## SH.HIV.INCD.14 0.087056251 -0.129190459
## SE.PRM.UNER 0.105991834 -0.084787295
## SE.PRM.CUAT.ZS 0.087733805 0.154937967
## SE.TER.CUAT.BA.ZS 0.057060926 0.148409003
## SP.DYN.IMRT.IN 0.021349529 -0.219257279
## SE.PRM.CMPT.ZS 0.077596070 0.184902534
## SE.ADT.LITR.ZS 0.133314885 0.115569005
## FR.INR.RINR -0.440532819 -0.013032653
## SP.POP.GROW 0.007249693 -0.241175497
## EN.POP.DNST -0.036656839 0.025269137
## SP.POP.TOTL 0.015940713 0.026753907
## SH.XPD.CHEX.PC.CD -0.075320690 0.241881119
## SH.XPD.CHEX.GD.ZS 0.077067539 0.268532666
## SL.UEM.TOTL.NE.ZS 0.201930795 0.063902825
## NY.GDP.MKTP.KD.ZG -0.130893634 -0.002794672
## NY.GDP.PCAP.CD -0.095200132 0.137130699
## SP.DYN.CBRT.IN 0.059076502 -0.279460293
## SH.HIV.INCD 0.066545227 -0.063901509
## SH.H2O.SMDW.ZS -0.040489372 0.100802828
## SI.POV.LMIC 1.000000000 0.077119535
## SE.COM.DURS 0.077119535 1.000000000model<-lm(log(SP.DYN.LE00.IN)~., x)
summary.aov(model)## Df Sum Sq Mean Sq F value Pr(>F)
## EG.ELC.ACCS.ZS 1 1.3544 1.3544 1014.488 < 2e-16 ***
## NY.ADJ.NNTY.KD.ZG 1 0.0038 0.0038 2.835 0.09386 .
## NY.ADJ.NNTY.PC.KD.ZG 1 0.0104 0.0104 7.817 0.00570 **
## SH.HIV.INCD.14 1 0.0362 0.0362 27.132 4.87e-07 ***
## SE.PRM.UNER 1 0.0032 0.0032 2.410 0.12218
## SE.PRM.CUAT.ZS 1 0.0073 0.0073 5.467 0.02041 *
## SE.TER.CUAT.BA.ZS 1 0.0196 0.0196 14.710 0.00017 ***
## SP.DYN.IMRT.IN 1 0.4710 0.4710 352.793 < 2e-16 ***
## SE.PRM.CMPT.ZS 1 0.0036 0.0036 2.682 0.10310
## SE.ADT.LITR.ZS 1 0.0037 0.0037 2.788 0.09658 .
## FR.INR.RINR 1 0.0004 0.0004 0.283 0.59557
## SP.POP.GROW 1 0.0048 0.0048 3.607 0.05905 .
## EN.POP.DNST 1 0.0006 0.0006 0.450 0.50325
## SP.POP.TOTL 1 0.0009 0.0009 0.646 0.42238
## SH.XPD.CHEX.PC.CD 1 0.0531 0.0531 39.798 1.90e-09 ***
## SH.XPD.CHEX.GD.ZS 1 0.0003 0.0003 0.207 0.64994
## SL.UEM.TOTL.NE.ZS 1 0.0026 0.0026 1.917 0.16776
## NY.GDP.MKTP.KD.ZG 1 0.0010 0.0010 0.727 0.39482
## NY.GDP.PCAP.CD 1 0.0012 0.0012 0.872 0.35152
## SP.DYN.CBRT.IN 1 0.0024 0.0024 1.764 0.18572
## SH.HIV.INCD 1 0.0089 0.0089 6.677 0.01051 *
## SH.H2O.SMDW.ZS 1 0.0003 0.0003 0.252 0.61611
## SI.POV.LMIC 1 0.0003 0.0003 0.242 0.62319
## SE.COM.DURS 1 0.0000 0.0000 0.020 0.88722
## Residuals 192 0.2563 0.0013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model)##
## Call:
## lm(formula = log(SP.DYN.LE00.IN) ~ ., data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.094563 -0.020273 0.001651 0.021741 0.074856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.279e+00 2.061e-02 207.597 < 2e-16 ***
## EG.ELC.ACCS.ZS 6.048e-04 1.797e-04 3.366 0.000922 ***
## NY.ADJ.NNTY.KD.ZG -3.080e-03 3.897e-03 -0.790 0.430293
## NY.ADJ.NNTY.PC.KD.ZG 3.781e-03 4.094e-03 0.924 0.356824
## SH.HIV.INCD.14 7.412e-07 1.950e-06 0.380 0.704304
## SE.PRM.UNER 1.278e-09 1.511e-08 0.085 0.932689
## SE.PRM.CUAT.ZS 2.036e-04 1.309e-04 1.556 0.121303
## SE.TER.CUAT.BA.ZS -3.292e-04 4.810e-04 -0.684 0.494497
## SP.DYN.IMRT.IN -3.577e-03 2.912e-04 -12.285 < 2e-16 ***
## SE.PRM.CMPT.ZS 5.111e-05 6.058e-05 0.844 0.399910
## SE.ADT.LITR.ZS -4.260e-05 1.113e-04 -0.383 0.702335
## FR.INR.RINR 4.369e-04 3.699e-04 1.181 0.239007
## SP.POP.GROW 1.264e-02 4.691e-03 2.695 0.007666 **
## EN.POP.DNST 4.028e-08 1.514e-06 0.027 0.978801
## SP.POP.TOTL 1.277e-11 1.858e-11 0.688 0.492582
## SH.XPD.CHEX.PC.CD 8.382e-06 2.553e-06 3.283 0.001222 **
## SH.XPD.CHEX.GD.ZS 5.406e-04 9.758e-04 0.554 0.580217
## SL.UEM.TOTL.NE.ZS 1.070e-03 5.028e-04 2.128 0.034600 *
## NY.GDP.MKTP.KD.ZG -1.272e-03 1.048e-03 -1.214 0.226253
## NY.GDP.PCAP.CD 1.154e-07 1.494e-07 0.773 0.440639
## SP.DYN.CBRT.IN -7.858e-04 5.433e-04 -1.446 0.149688
## SH.HIV.INCD -6.304e-07 2.501e-07 -2.521 0.012522 *
## SH.H2O.SMDW.ZS 3.672e-05 7.632e-05 0.481 0.631005
## SI.POV.LMIC -2.872e-04 5.729e-04 -0.501 0.616716
## SE.COM.DURS 1.085e-04 7.641e-04 0.142 0.887221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03654 on 192 degrees of freedom
## Multiple R-squared: 0.8859, Adjusted R-squared: 0.8716
## F-statistic: 62.11 on 24 and 192 DF, p-value: < 2.2e-16par(mfrow=c(2,2))
plot(model)
vif(model)## EG.ELC.ACCS.ZS NY.ADJ.NNTY.KD.ZG NY.ADJ.NNTY.PC.KD.ZG
## 3.282301 79.519392 77.872386
## SH.HIV.INCD.14 SE.PRM.UNER SE.PRM.CUAT.ZS
## 3.209458 1.422803 3.067127
## SE.TER.CUAT.BA.ZS SP.DYN.IMRT.IN SE.PRM.CMPT.ZS
## 2.783986 5.103899 1.308061
## SE.ADT.LITR.ZS FR.INR.RINR SP.POP.GROW
## 1.763430 1.500228 4.245955
## EN.POP.DNST SP.POP.TOTL SH.XPD.CHEX.PC.CD
## 1.471774 1.065360 3.222259
## SH.XPD.CHEX.GD.ZS SL.UEM.TOTL.NE.ZS NY.GDP.MKTP.KD.ZG
## 2.032846 1.317309 1.819289
## NY.GDP.PCAP.CD SP.DYN.CBRT.IN SH.HIV.INCD
## 2.696223 5.265360 3.098953
## SH.H2O.SMDW.ZS SI.POV.LMIC SE.COM.DURS
## 1.736031 1.491778 1.323535cor(x)## SP.DYN.LE00.IN EG.ELC.ACCS.ZS NY.ADJ.NNTY.KD.ZG
## SP.DYN.LE00.IN 1.000000000 0.76210680 -0.142910265
## EG.ELC.ACCS.ZS 0.762106796 1.00000000 -0.128416559
## NY.ADJ.NNTY.KD.ZG -0.142910265 -0.12841656 1.000000000
## NY.ADJ.NNTY.PC.KD.ZG -0.056162396 -0.03028956 0.979376349
## SH.HIV.INCD.14 -0.446203601 -0.42648913 0.087064518
## SE.PRM.UNER -0.256622339 -0.32785330 0.125010790
## SE.PRM.CUAT.ZS 0.215687072 0.20552726 0.016104012
## SE.TER.CUAT.BA.ZS 0.270740889 0.20643856 0.004606558
## SP.DYN.IMRT.IN -0.897536450 -0.77029705 0.178174868
## SE.PRM.CMPT.ZS 0.254779994 0.23916812 0.173400190
## SE.ADT.LITR.ZS 0.025665668 0.11301552 0.026432587
## FR.INR.RINR 0.010883701 0.03707547 0.076887435
## SP.POP.GROW -0.510730030 -0.53582925 0.152914099
## EN.POP.DNST 0.156256779 0.09378245 -0.068611664
## SP.POP.TOTL -0.007261648 0.04139386 0.053456099
## SH.XPD.CHEX.PC.CD 0.514664184 0.29064840 -0.043109963
## SH.XPD.CHEX.GD.ZS 0.142205661 0.11463776 0.049262821
## SL.UEM.TOTL.NE.ZS 0.128832358 0.15413000 -0.036879594
## NY.GDP.MKTP.KD.ZG -0.178888192 -0.08168349 0.588836455
## NY.GDP.PCAP.CD 0.512433298 0.32146150 -0.118813043
## SP.DYN.CBRT.IN -0.773668023 -0.72101631 0.221088770
## SH.HIV.INCD -0.276740764 -0.22233468 0.003064933
## SH.H2O.SMDW.ZS 0.562674930 0.39719985 -0.094495706
## SI.POV.LMIC -0.052929097 -0.03327650 -0.007203884
## SE.COM.DURS 0.262130840 0.35921315 -0.026062482
## NY.ADJ.NNTY.PC.KD.ZG SH.HIV.INCD.14 SE.PRM.UNER
## SP.DYN.LE00.IN -0.056162396 -0.446203601 -0.256622339
## EG.ELC.ACCS.ZS -0.030289564 -0.426489132 -0.327853300
## NY.ADJ.NNTY.KD.ZG 0.979376349 0.087064518 0.125010790
## NY.ADJ.NNTY.PC.KD.ZG 1.000000000 0.016441256 0.060432454
## SH.HIV.INCD.14 0.016441256 1.000000000 0.324587555
## SE.PRM.UNER 0.060432454 0.324587555 1.000000000
## SE.PRM.CUAT.ZS 0.048854956 -0.056152689 0.018786885
## SE.TER.CUAT.BA.ZS 0.029828822 -0.092512725 -0.040564158
## SP.DYN.IMRT.IN 0.079410047 0.437707894 0.231833412
## SE.PRM.CMPT.ZS 0.169641996 -0.130004300 0.091049543
## SE.ADT.LITR.ZS 0.030979337 -0.005803928 0.064960517
## FR.INR.RINR 0.044304242 -0.003266622 -0.049280355
## SP.POP.GROW -0.008832112 0.347119666 0.278075448
## EN.POP.DNST -0.051531726 -0.052049273 -0.048907149
## SP.POP.TOTL 0.050762195 0.070763190 0.012513999
## SH.XPD.CHEX.PC.CD -0.022848088 -0.149894858 -0.081304866
## SH.XPD.CHEX.GD.ZS 0.064585263 -0.054259977 0.008203986
## SL.UEM.TOTL.NE.ZS -0.002452958 0.072924173 0.024317408
## NY.GDP.MKTP.KD.ZG 0.568636550 0.029707165 0.109136968
## NY.GDP.PCAP.CD -0.084400596 -0.171299544 -0.127957637
## SP.DYN.CBRT.IN 0.093976742 0.429349131 0.330169362
## SH.HIV.INCD -0.036949717 0.754966989 0.376347042
## SH.H2O.SMDW.ZS -0.040699986 -0.227881720 -0.165910563
## SI.POV.LMIC -0.005718627 0.087056251 0.105991834
## SE.COM.DURS 0.002191125 -0.129190459 -0.084787295
## SE.PRM.CUAT.ZS SE.TER.CUAT.BA.ZS SP.DYN.IMRT.IN
## SP.DYN.LE00.IN 0.21568707 0.270740889 -0.897536450
## EG.ELC.ACCS.ZS 0.20552726 0.206438564 -0.770297048
## NY.ADJ.NNTY.KD.ZG 0.01610401 0.004606558 0.178174868
## NY.ADJ.NNTY.PC.KD.ZG 0.04885496 0.029828822 0.079410047
## SH.HIV.INCD.14 -0.05615269 -0.092512725 0.437707894
## SE.PRM.UNER 0.01878689 -0.040564158 0.231833412
## SE.PRM.CUAT.ZS 1.00000000 0.750329267 -0.198892795
## SE.TER.CUAT.BA.ZS 0.75032927 1.000000000 -0.232458010
## SP.DYN.IMRT.IN -0.19889279 -0.232458010 1.000000000
## SE.PRM.CMPT.ZS 0.13077902 0.149201829 -0.209851628
## SE.ADT.LITR.ZS 0.57590047 0.452117947 -0.034411510
## FR.INR.RINR 0.02236831 -0.004049383 0.007352259
## SP.POP.GROW -0.23001122 -0.175342305 0.598182331
## EN.POP.DNST 0.02714290 0.002302600 -0.145610331
## SP.POP.TOTL -0.02163243 -0.045382539 0.027107184
## SH.XPD.CHEX.PC.CD 0.10211697 0.307751286 -0.367409327
## SH.XPD.CHEX.GD.ZS 0.09787465 0.176268758 -0.015888262
## SL.UEM.TOTL.NE.ZS 0.11149844 0.072319359 -0.096961532
## NY.GDP.MKTP.KD.ZG 0.01355567 0.019636195 0.212528659
## NY.GDP.PCAP.CD 0.04620446 0.161408769 -0.453428722
## SP.DYN.CBRT.IN -0.19214567 -0.208634697 0.817470114
## SH.HIV.INCD 0.07605194 -0.021144860 0.231153496
## SH.H2O.SMDW.ZS 0.19645745 0.209232909 -0.524703176
## SI.POV.LMIC 0.08773380 0.057060926 0.021349529
## SE.COM.DURS 0.15493797 0.148409003 -0.219257279
## SE.PRM.CMPT.ZS SE.ADT.LITR.ZS FR.INR.RINR SP.POP.GROW
## SP.DYN.LE00.IN 0.254779994 0.025665668 0.010883701 -0.510730030
## EG.ELC.ACCS.ZS 0.239168125 0.113015521 0.037075469 -0.535829248
## NY.ADJ.NNTY.KD.ZG 0.173400190 0.026432587 0.076887435 0.152914099
## NY.ADJ.NNTY.PC.KD.ZG 0.169641996 0.030979337 0.044304242 -0.008832112
## SH.HIV.INCD.14 -0.130004300 -0.005803928 -0.003266622 0.347119666
## SE.PRM.UNER 0.091049543 0.064960517 -0.049280355 0.278075448
## SE.PRM.CUAT.ZS 0.130779021 0.575900473 0.022368312 -0.230011218
## SE.TER.CUAT.BA.ZS 0.149201829 0.452117947 -0.004049383 -0.175342305
## SP.DYN.IMRT.IN -0.209851628 -0.034411510 0.007352259 0.598182331
## SE.PRM.CMPT.ZS 1.000000000 0.079713358 0.059859475 -0.021483774
## SE.ADT.LITR.ZS 0.079713358 1.000000000 0.081273251 -0.017439763
## FR.INR.RINR 0.059859475 0.081273251 1.000000000 0.120174771
## SP.POP.GROW -0.021483774 -0.017439763 0.120174771 1.000000000
## EN.POP.DNST 0.038814236 0.015301532 -0.015265318 -0.013068676
## SP.POP.TOTL -0.024439184 0.006225201 0.048872841 -0.028318345
## SH.XPD.CHEX.PC.CD 0.187678934 -0.094097434 -0.083782042 -0.182129566
## SH.XPD.CHEX.GD.ZS 0.232380298 -0.016526855 0.029063560 -0.104095969
## SL.UEM.TOTL.NE.ZS 0.130919946 0.180639536 -0.050424133 -0.177967657
## NY.GDP.MKTP.KD.ZG 0.154547908 0.081060424 0.221721299 0.217856451
## NY.GDP.PCAP.CD 0.057775008 -0.102056423 -0.111416728 -0.214110218
## SP.DYN.CBRT.IN -0.141807397 -0.007151255 0.076305330 0.730707788
## SH.HIV.INCD -0.009808778 0.141938861 0.015335516 0.185150355
## SH.H2O.SMDW.ZS 0.194050064 -0.007511715 -0.072686896 -0.324549792
## SI.POV.LMIC 0.077596070 0.133314885 -0.440532819 0.007249693
## SE.COM.DURS 0.184902534 0.115569005 -0.013032653 -0.241175497
## EN.POP.DNST SP.POP.TOTL SH.XPD.CHEX.PC.CD
## SP.DYN.LE00.IN 0.15625678 -0.0072616479 0.51466418
## EG.ELC.ACCS.ZS 0.09378245 0.0413938574 0.29064840
## NY.ADJ.NNTY.KD.ZG -0.06861166 0.0534560995 -0.04310996
## NY.ADJ.NNTY.PC.KD.ZG -0.05153173 0.0507621947 -0.02284809
## SH.HIV.INCD.14 -0.05204927 0.0707631898 -0.14989486
## SE.PRM.UNER -0.04890715 0.0125139990 -0.08130487
## SE.PRM.CUAT.ZS 0.02714290 -0.0216324293 0.10211697
## SE.TER.CUAT.BA.ZS 0.00230260 -0.0453825387 0.30775129
## SP.DYN.IMRT.IN -0.14561033 0.0271071837 -0.36740933
## SE.PRM.CMPT.ZS 0.03881424 -0.0244391840 0.18767893
## SE.ADT.LITR.ZS 0.01530153 0.0062252012 -0.09409743
## FR.INR.RINR -0.01526532 0.0488728406 -0.08378204
## SP.POP.GROW -0.01306868 -0.0283183449 -0.18212957
## EN.POP.DNST 1.00000000 -0.0274392727 0.02820049
## SP.POP.TOTL -0.02743927 1.0000000000 0.01833672
## SH.XPD.CHEX.PC.CD 0.02820049 0.0183367163 1.00000000
## SH.XPD.CHEX.GD.ZS -0.17735731 -0.0000488106 0.50901728
## SL.UEM.TOTL.NE.ZS -0.07672291 0.0322618898 0.05851973
## NY.GDP.MKTP.KD.ZG -0.04856852 0.0825563419 -0.07685633
## NY.GDP.PCAP.CD 0.44979650 -0.0588816414 0.56653754
## SP.DYN.CBRT.IN -0.18603715 -0.0073325442 -0.37279116
## SH.HIV.INCD -0.04539133 0.0691063411 -0.05692940
## SH.H2O.SMDW.ZS 0.17551390 -0.0804710086 0.46258692
## SI.POV.LMIC -0.03665684 0.0159407128 -0.07532069
## SE.COM.DURS 0.02526914 0.0267539070 0.24188112
## SH.XPD.CHEX.GD.ZS SL.UEM.TOTL.NE.ZS NY.GDP.MKTP.KD.ZG
## SP.DYN.LE00.IN 0.1422056608 0.128832358 -0.178888192
## EG.ELC.ACCS.ZS 0.1146377641 0.154129999 -0.081683486
## NY.ADJ.NNTY.KD.ZG 0.0492628208 -0.036879594 0.588836455
## NY.ADJ.NNTY.PC.KD.ZG 0.0645852631 -0.002452958 0.568636550
## SH.HIV.INCD.14 -0.0542599774 0.072924173 0.029707165
## SE.PRM.UNER 0.0082039865 0.024317408 0.109136968
## SE.PRM.CUAT.ZS 0.0978746503 0.111498443 0.013555672
## SE.TER.CUAT.BA.ZS 0.1762687583 0.072319359 0.019636195
## SP.DYN.IMRT.IN -0.0158882622 -0.096961532 0.212528659
## SE.PRM.CMPT.ZS 0.2323802979 0.130919946 0.154547908
## SE.ADT.LITR.ZS -0.0165268552 0.180639536 0.081060424
## FR.INR.RINR 0.0290635604 -0.050424133 0.221721299
## SP.POP.GROW -0.1040959686 -0.177967657 0.217856451
## EN.POP.DNST -0.1773573073 -0.076722907 -0.048568523
## SP.POP.TOTL -0.0000488106 0.032261890 0.082556342
## SH.XPD.CHEX.PC.CD 0.5090172832 0.058519728 -0.076856334
## SH.XPD.CHEX.GD.ZS 1.0000000000 0.194374585 0.058839395
## SL.UEM.TOTL.NE.ZS 0.1943745848 1.000000000 -0.017663622
## NY.GDP.MKTP.KD.ZG 0.0588393952 -0.017663622 1.000000000
## NY.GDP.PCAP.CD -0.0183278394 -0.086422962 -0.122073583
## SP.DYN.CBRT.IN -0.1208045073 -0.078919525 0.197439828
## SH.HIV.INCD 0.0527881586 0.248560596 -0.026119423
## SH.H2O.SMDW.ZS 0.1510553934 0.109781117 -0.138196980
## SI.POV.LMIC 0.0770675389 0.201930795 -0.130893634
## SE.COM.DURS 0.2685326658 0.063902825 -0.002794672
## NY.GDP.PCAP.CD SP.DYN.CBRT.IN SH.HIV.INCD SH.H2O.SMDW.ZS
## SP.DYN.LE00.IN 0.51243330 -0.773668023 -0.276740764 0.562674930
## EG.ELC.ACCS.ZS 0.32146150 -0.721016313 -0.222334679 0.397199848
## NY.ADJ.NNTY.KD.ZG -0.11881304 0.221088770 0.003064933 -0.094495706
## NY.ADJ.NNTY.PC.KD.ZG -0.08440060 0.093976742 -0.036949717 -0.040699986
## SH.HIV.INCD.14 -0.17129954 0.429349131 0.754966989 -0.227881720
## SE.PRM.UNER -0.12795764 0.330169362 0.376347042 -0.165910563
## SE.PRM.CUAT.ZS 0.04620446 -0.192145665 0.076051940 0.196457453
## SE.TER.CUAT.BA.ZS 0.16140877 -0.208634697 -0.021144860 0.209232909
## SP.DYN.IMRT.IN -0.45342872 0.817470114 0.231153496 -0.524703176
## SE.PRM.CMPT.ZS 0.05777501 -0.141807397 -0.009808778 0.194050064
## SE.ADT.LITR.ZS -0.10205642 -0.007151255 0.141938861 -0.007511715
## FR.INR.RINR -0.11141673 0.076305330 0.015335516 -0.072686896
## SP.POP.GROW -0.21411022 0.730707788 0.185150355 -0.324549792
## EN.POP.DNST 0.44979650 -0.186037151 -0.045391325 0.175513898
## SP.POP.TOTL -0.05888164 -0.007332544 0.069106341 -0.080471009
## SH.XPD.CHEX.PC.CD 0.56653754 -0.372791157 -0.056929400 0.462586918
## SH.XPD.CHEX.GD.ZS -0.01832784 -0.120804507 0.052788159 0.151055393
## SL.UEM.TOTL.NE.ZS -0.08642296 -0.078919525 0.248560596 0.109781117
## NY.GDP.MKTP.KD.ZG -0.12207358 0.197439828 -0.026119423 -0.138196980
## NY.GDP.PCAP.CD 1.00000000 -0.463579620 -0.111592544 0.432508796
## SP.DYN.CBRT.IN -0.46357962 1.000000000 0.230146870 -0.469135815
## SH.HIV.INCD -0.11159254 0.230146870 1.000000000 -0.165420992
## SH.H2O.SMDW.ZS 0.43250880 -0.469135815 -0.165420992 1.000000000
## SI.POV.LMIC -0.09520013 0.059076502 0.066545227 -0.040489372
## SE.COM.DURS 0.13713070 -0.279460293 -0.063901509 0.100802828
## SI.POV.LMIC SE.COM.DURS
## SP.DYN.LE00.IN -0.052929097 0.262130840
## EG.ELC.ACCS.ZS -0.033276501 0.359213146
## NY.ADJ.NNTY.KD.ZG -0.007203884 -0.026062482
## NY.ADJ.NNTY.PC.KD.ZG -0.005718627 0.002191125
## SH.HIV.INCD.14 0.087056251 -0.129190459
## SE.PRM.UNER 0.105991834 -0.084787295
## SE.PRM.CUAT.ZS 0.087733805 0.154937967
## SE.TER.CUAT.BA.ZS 0.057060926 0.148409003
## SP.DYN.IMRT.IN 0.021349529 -0.219257279
## SE.PRM.CMPT.ZS 0.077596070 0.184902534
## SE.ADT.LITR.ZS 0.133314885 0.115569005
## FR.INR.RINR -0.440532819 -0.013032653
## SP.POP.GROW 0.007249693 -0.241175497
## EN.POP.DNST -0.036656839 0.025269137
## SP.POP.TOTL 0.015940713 0.026753907
## SH.XPD.CHEX.PC.CD -0.075320690 0.241881119
## SH.XPD.CHEX.GD.ZS 0.077067539 0.268532666
## SL.UEM.TOTL.NE.ZS 0.201930795 0.063902825
## NY.GDP.MKTP.KD.ZG -0.130893634 -0.002794672
## NY.GDP.PCAP.CD -0.095200132 0.137130699
## SP.DYN.CBRT.IN 0.059076502 -0.279460293
## SH.HIV.INCD 0.066545227 -0.063901509
## SH.H2O.SMDW.ZS -0.040489372 0.100802828
## SI.POV.LMIC 1.000000000 0.077119535
## SE.COM.DURS 0.077119535 1.000000000x<-df[,-c(1:3,25)]
model1<-lm(SP.DYN.LE00.IN~EG.ELC.ACCS.ZS+SP.DYN.IMRT.IN+SP.POP.GROW+SH.XPD.CHEX.PC.CD+
SL.UEM.TOTL.NE.ZS+SH.HIV.INCD,x)
summary(model1)##
## Call:
## lm(formula = SP.DYN.LE00.IN ~ EG.ELC.ACCS.ZS + SP.DYN.IMRT.IN +
## SP.POP.GROW + SH.XPD.CHEX.PC.CD + SL.UEM.TOTL.NE.ZS + SH.HIV.INCD,
## data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.763 -1.590 0.158 1.659 7.585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.239e+01 1.251e+00 57.866 < 2e-16 ***
## EG.ELC.ACCS.ZS 4.576e-02 1.151e-02 3.977 9.61e-05 ***
## SP.DYN.IMRT.IN -2.619e-01 1.613e-02 -16.236 < 2e-16 ***
## SP.POP.GROW 4.216e-01 2.111e-01 1.998 0.047059 *
## SH.XPD.CHEX.PC.CD 8.612e-04 1.110e-04 7.762 3.62e-13 ***
## SL.UEM.TOTL.NE.ZS 7.201e-02 3.400e-02 2.118 0.035372 *
## SH.HIV.INCD -3.730e-05 1.113e-05 -3.351 0.000955 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.645 on 210 degrees of freedom
## Multiple R-squared: 0.8664, Adjusted R-squared: 0.8626
## F-statistic: 226.9 on 6 and 210 DF, p-value: < 2.2e-16plot(model1)
vif(model1)## EG.ELC.ACCS.ZS SP.DYN.IMRT.IN SP.POP.GROW SH.XPD.CHEX.PC.CD
## 2.567748 2.989028 1.640335 1.161115
## SL.UEM.TOTL.NE.ZS SH.HIV.INCD
## 1.149654 1.171668x<-df[,-c(1:2,25)]
x$Continent<-as.factor(x$Continent)
model2<-lm(SP.DYN.LE00.IN~Continent+EG.ELC.ACCS.ZS+SP.DYN.IMRT.IN+SP.POP.GROW+SH.XPD.CHEX.PC.CD+
SL.UEM.TOTL.NE.ZS+SH.HIV.INCD,x)
summary.aov(model2)## Df Sum Sq Mean Sq F value Pr(>F)
## Continent 5 6167 1233.3 179.098 < 2e-16 ***
## EG.ELC.ACCS.ZS 1 1119 1119.2 162.522 < 2e-16 ***
## SP.DYN.IMRT.IN 1 1806 1805.8 262.236 < 2e-16 ***
## SP.POP.GROW 1 61 60.7 8.814 0.00334 **
## SH.XPD.CHEX.PC.CD 1 360 360.0 52.282 9.36e-12 ***
## SL.UEM.TOTL.NE.ZS 1 10 10.0 1.459 0.22854
## SH.HIV.INCD 1 61 60.7 8.816 0.00334 **
## Residuals 205 1412 6.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(model2)##
## Call:
## lm(formula = SP.DYN.LE00.IN ~ Continent + EG.ELC.ACCS.ZS + SP.DYN.IMRT.IN +
## SP.POP.GROW + SH.XPD.CHEX.PC.CD + SL.UEM.TOTL.NE.ZS + SH.HIV.INCD,
## data = x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.601 -1.472 -0.045 1.537 7.073
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.265e+01 1.330e+00 54.612 < 2e-16 ***
## ContinentAsia 1.819e+00 7.258e-01 2.507 0.01297 *
## ContinentAustralia/Oceania 4.837e-01 8.618e-01 0.561 0.57526
## ContinentEurope 1.037e+00 9.037e-01 1.148 0.25244
## ContinentNorth America 1.243e+00 8.276e-01 1.502 0.13472
## ContinentSouth America 1.698e+00 1.010e+00 1.682 0.09403 .
## EG.ELC.ACCS.ZS 3.027e-02 1.278e-02 2.369 0.01879 *
## SP.DYN.IMRT.IN -2.579e-01 1.675e-02 -15.396 < 2e-16 ***
## SP.POP.GROW 4.011e-01 2.315e-01 1.733 0.08468 .
## SH.XPD.CHEX.PC.CD 8.929e-04 1.209e-04 7.388 3.71e-12 ***
## SL.UEM.TOTL.NE.ZS 7.021e-02 3.473e-02 2.022 0.04450 *
## SH.HIV.INCD -3.341e-05 1.125e-05 -2.969 0.00334 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.624 on 205 degrees of freedom
## Multiple R-squared: 0.8716, Adjusted R-squared: 0.8647
## F-statistic: 126.5 on 11 and 205 DF, p-value: < 2.2e-16plot(model2)