1.Explore the spending amount by creating a pivot table for the categorical variables, and computing the average and standard deviation of spending in each category.

2.Explore the relationship between spending and each of the two continuous predictors by creating two scatter plots (SPENDING vs. FREQ, and SPENDING vs. LAST UPDATE). Does there seem to be a linear relationship there?

3. In order to fit a predictive model for SPENDING,

a. Partition the 1000 records into training and validation sets. Pre-process the 4 categorical variables by creating dummy variables.

b. Run a multiple linear regression model for SPENDING vs. all 6 predictors. Give the estimate predictive equation.

c. Based on this model, what type of purchaser is most likely to spend a large amount of money?

d. If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model?

e. Show how the prediction and the prediction error are computed for the first purchase in the validation set.

f. Evaluate the predictive accuracy of the model by examining its performance on the validation set.