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4. In this problem we will be modeling how life expectancy changes across the ou states.
To get more information about the data set you can do
help (state. x77)
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 conclu
pairs (state_data)
(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 <- Im(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) Below is 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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