Intercountry Life-Cycle Savings Data

Data on the savings ratio 1960-1970. A data frame with 50 observations on 5 variables.

  1. sr aggregate personal savings
  2. pop15 % of population under 15
  3. pop75 % of population over 75
  4. dpi real per-capita disposable income
  5. ddpi % growth rate of dpi

source "Sterling, Arnie (1977) Unpublished BS Thesis. Massachusetts Institute of Technology.""
"Belsley, D. A., Kuh. E. and Welsch, R. E. (1980) Regression Diagnostics. New York: Wiley.""

Data

library(datasets)
data(LifeCycleSavings)
head(LifeCycleSavings)

##              sr pop15 pop75     dpi ddpi
## Australia 11.43 29.35  2.87 2329.68 2.87
## Austria   12.07 23.32  4.41 1507.99 3.93
## Belgium   13.17 23.80  4.43 2108.47 3.82
## Bolivia    5.75 41.89  1.67  189.13 0.22
## Brazil    12.88 42.19  0.83  728.47 4.56
## Canada     8.79 31.72  2.85 2982.88 2.43

Exploring data

We can not conclude anything from the plot

pairs(~.,data=LifeCycleSavings, 
   main="Correlation matrix")

plot of chunk unnamed-chunk-2

Regression model

fit<- lm(sr~., data=LifeCycleSavings)
summary(fit)$coefficients

##                  Estimate   Std. Error    t value     Pr(>|t|)
## (Intercept) 28.5660865407 7.3545161062  3.8841558 0.0003338249
## pop15       -0.4611931471 0.1446422248 -3.1885098 0.0026030189
## pop75       -1.6914976767 1.0835989307 -1.5609998 0.1255297940
## dpi         -0.0003369019 0.0009311072 -0.3618293 0.7191731554
## ddpi         0.4096949279 0.1961971276  2.0881801 0.0424711387

Which variable coefficient's are significantly different from 0?


-pop15 and ddpi
-explanation Pr(>|t|)<<0.5%