R Notebook

str(LifeCycleSavings)

## 'data.frame':    50 obs. of  5 variables:
##  $ sr   : num  11.43 12.07 13.17 5.75 12.88 ...
##  $ pop15: num  29.4 23.3 23.8 41.9 42.2 ...
##  $ pop75: num  2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...
##  $ dpi  : num  2330 1508 2108 189 728 ...
##  $ ddpi : num  2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...

head(LifeCycleSavings)

summary(LifeCycleSavings)

##        sr             pop15           pop75            dpi         
##  Min.   : 0.600   Min.   :21.44   Min.   :0.560   Min.   :  88.94  
##  1st Qu.: 6.970   1st Qu.:26.21   1st Qu.:1.125   1st Qu.: 288.21  
##  Median :10.510   Median :32.58   Median :2.175   Median : 695.66  
##  Mean   : 9.671   Mean   :35.09   Mean   :2.293   Mean   :1106.76  
##  3rd Qu.:12.617   3rd Qu.:44.06   3rd Qu.:3.325   3rd Qu.:1795.62  
##  Max.   :21.100   Max.   :47.64   Max.   :4.700   Max.   :4001.89  
##       ddpi       
##  Min.   : 0.220  
##  1st Qu.: 2.002  
##  Median : 3.000  
##  Mean   : 3.758  
##  3rd Qu.: 4.478  
##  Max.   :16.710

cor(LifeCycleSavings)

##               sr       pop15       pop75        dpi        ddpi
## sr     1.0000000 -0.45553809  0.31652112  0.2203589  0.30478716
## pop15 -0.4555381  1.00000000 -0.90847871 -0.7561881 -0.04782569
## pop75  0.3165211 -0.90847871  1.00000000  0.7869995  0.02532138
## dpi    0.2203589 -0.75618810  0.78699951  1.0000000 -0.12948552
## ddpi   0.3047872 -0.04782569  0.02532138 -0.1294855  1.00000000

model1 <- lm(sr ~ ddpi, data = LifeCycleSavings)
summary(model1)

## 
## Call:
## lm(formula = sr ~ ddpi, data = LifeCycleSavings)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.5535 -3.7349  0.9835  2.7720  9.3104 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   7.8830     1.0110   7.797 4.46e-10 ***
## ddpi          0.4758     0.2146   2.217   0.0314 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 4.311 on 48 degrees of freedom
## Multiple R-squared:  0.0929, Adjusted R-squared:  0.074 
## F-statistic: 4.916 on 1 and 48 DF,  p-value: 0.03139

model2 <- lm(sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
summary(model2)

## 
## Call:
## lm(formula = sr ~ pop15 + pop75 + dpi + ddpi, data = LifeCycleSavings)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -8.2422 -2.6857 -0.2488  2.4280  9.7509 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 28.5660865  7.3545161   3.884 0.000334 ***
## pop15       -0.4611931  0.1446422  -3.189 0.002603 ** 
## pop75       -1.6914977  1.0835989  -1.561 0.125530    
## dpi         -0.0003369  0.0009311  -0.362 0.719173    
## ddpi         0.4096949  0.1961971   2.088 0.042471 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 3.803 on 45 degrees of freedom
## Multiple R-squared:  0.3385, Adjusted R-squared:  0.2797 
## F-statistic: 5.756 on 4 and 45 DF,  p-value: 0.0007904

mean(LifeCycleSavings$sr)

## [1] 9.671

median(LifeCycleSavings$sr)

## [1] 10.51

sd(LifeCycleSavings$sr)

## [1] 4.480407

plot(LifeCycleSavings)

cor(LifeCycleSavings$pop15, LifeCycleSavings$sr)

## [1] -0.4555381

cor(LifeCycleSavings$dpi, LifeCycleSavings$sr)

## [1] 0.2203589

cor(LifeCycleSavings$ddpi, LifeCycleSavings$sr)

## [1] 0.3047872