In this problem we will be modeling how life expectancy changes across the 50 states. To get more information
about the data set you can do
First we need to convert the state.x77 matrix into a data frame.
state_data <- data.frame(state.x77)
(A) First look at the data using the command and report your initial visual conclusions.
(B) I am going to remove Alaska from our analysis. Why do you think I am removing Alaska from our analysis?
no_alaska <- state_data[-2,]
(C) Next, we will fit a final model using forward selection with AIC.
inter_model <- lm(Life.Exp ~ 1, no_alaska)
forward_model <- step(inter_model, direction="forward", scope=(~Population+Income+Illiteracy+Murder+HS.Grad+Frost+Area))
Provide which variables were included in the model and the order in which they were included. You do not need to include all the output from the step command to answer this question.
(D) Report the summary output for the final model. Is it valid to conclude that given the variables Murder, HS.Grad and Population that the variable Frost has a significant relationship (at a .05 level) with life expectancy?
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