#R-help to exercise 3.2 in BSS

 

 

# Read the data into a dataframe, give names to the variables, and inspect the data:

insurance<-read.table("http://www.math.uio.no/avdc/kurs/STK4900/data/exer3_2.dat")

names(insurance)<-c("income","riskave","amount")

insurance

 

# Check that the data correspond to those given in the exercise.

 

 

# Attach the dataframe:

attach(insurance)

 

 

# Compute summary measures for the variables:

summary(insurance)

 

# Make sure that you understand what the summary measures tell you!

 

 

# Make plots (side by side) of amount versus each of the other two variables:

par(mfrow=c(1,2))

plot(income,amount)

plot(riskave,amount)

par(mfrow=c(1,1))

 

# What do the plots tell you?

 

 

# Compute the correlation between the variables:

cor(insurance)

 

# Do the correlations agree with what you saw from the plots?

 

 

# Do univariate regression analyses of amount versus each of the other two variables:

fit1<-lm(amount~income)

fit2<-lm(amount~riskave)

summary(fit1)

summary(fit2)

 

# Which of the two variables, income and risk aversion, is most important for explaining the variation in the amount of life insurance carried?

# Does any of the variables (alone) have a significant effect?

 

 

# Do a regression analysis including both income and risk aversion:

fit3<-lm(amount~income+riskave)

summary(fit3)

 

# What does this model tell you? Does it look better than the best of the two models with only one covariate?

 

 

# Try yourself models with interaction and second order terms:

# For example you get a model with interaction and second order term for income by the command:

fit4<-lm(amount~income+riskave+riskave:income+I(income^2))