Life Expectancy Regression Analysis

Load Data

data <- read_excel("D:/data for my project/Abhi11.xlsx")

# View structure
head(data)

## # A tibble: 6 × 10
##   Country             `GDP per capita` `Life Expectancy` `Infant Mortality`
##   <chr>                          <dbl>             <dbl>              <dbl>
## 1 Afghanistan                     414.                63               36.5
## 2 Albania                        8575.                79                8.8
## 3 Algeria                        5364.                77               19.2
## 4 Angola                         2310.                62               51.1
## 5 Antigua and Barbuda           21495.                78                6.8
## 6 Argentina                     13466.                76                8.2
## # ℹ 6 more variables: `Under-5 Mortality` <dbl>, `DPT Immunization` <dbl>,
## #   `Measles Immunization` <dbl>, `HIV Prevalence` <dbl>,
## #   `Tuberculosis Incidence` <dbl>, `Anemia Prevalence` <dbl>

str(data)

## tibble [181 × 10] (S3: tbl_df/tbl/data.frame)
##  $ Country               : chr [1:181] "Afghanistan" "Albania" "Algeria" "Angola" ...
##  $ GDP per capita        : num [1:181] 414 8575 5364 2310 21495 ...
##  $ Life Expectancy       : num [1:181] 63 79 77 62 78 76 75 84 82 74 ...
##  $ Infant Mortality      : num [1:181] 36.5 8.8 19.2 51.1 6.8 8.2 9.9 3 2.5 14.7 ...
##  $ Under-5 Mortality     : num [1:181] 48.3 10.8 21.8 68.1 8.2 9.5 11.4 3.5 3.1 16.3 ...
##  $ DPT Immunization      : num [1:181] 60 97 92 42 95 93 96 95 97 92 ...
##  $ Measles Immunization  : num [1:181] 55 97 86 42 94 92 95 94 96 91 ...
##  $ HIV Prevalence        : num [1:181] 0.1 0.1 0.1 1.6 1.1 0.3 0.1 0.1 0.1 0.1 ...
##  $ Tuberculosis Incidence: num [1:181] 189 12 68 316 15 39 33 5 4 63 ...
##  $ Anemia Prevalence     : num [1:181] 45.4 24.7 31.6 43.8 20.3 21.1 25.6 13.7 14.1 28.4 ...

Regression Model

model <- lm(`Life Expectancy` ~ 
              `GDP per capita` +
              `Infant Mortality` +
              `Under-5 Mortality` +
              `DPT Immunization` +
              `Measles Immunization` +
              `HIV Prevalence` +
              `Tuberculosis Incidence` +
              `Anemia Prevalence`,
            data = data)

summary(model)

## 
## Call:
## lm(formula = `Life Expectancy` ~ `GDP per capita` + `Infant Mortality` + 
##     `Under-5 Mortality` + `DPT Immunization` + `Measles Immunization` + 
##     `HIV Prevalence` + `Tuberculosis Incidence` + `Anemia Prevalence`, 
##     data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -5.2689 -1.1101  0.0576  1.2817  5.0615 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               7.831e+01  2.607e+00  30.035  < 2e-16 ***
## `GDP per capita`          5.798e-05  1.044e-05   5.553 1.05e-07 ***
## `Infant Mortality`        4.136e-02  8.704e-02   0.475 0.635275    
## `Under-5 Mortality`      -1.997e-01  5.787e-02  -3.451 0.000702 ***
## `DPT Immunization`       -2.454e-01  2.048e-01  -1.199 0.232361    
## `Measles Immunization`    2.671e-01  2.006e-01   1.332 0.184748    
## `HIV Prevalence`         -2.409e-01  5.992e-02  -4.020 8.69e-05 ***
## `Tuberculosis Incidence` -3.729e-03  1.923e-03  -1.940 0.054045 .  
## `Anemia Prevalence`      -9.392e-02  4.285e-02  -2.192 0.029753 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.932 on 172 degrees of freedom
## Multiple R-squared:  0.9356, Adjusted R-squared:  0.9326 
## F-statistic: 312.3 on 8 and 172 DF,  p-value: < 2.2e-16

Diagnostic Plots

par(mfrow=c(2,2))
plot(model)

par(mfrow=c(1,1))

Actual vs Predicted Values

plot(data$`Life Expectancy`, predict(model),
     xlab="Actual Life Expectancy",
     ylab="Predicted Life Expectancy",
     col="blue", pch=19,
     main="Actual vs Predicted Life Expectancy")

abline(0,1,col="red",lwd=2)