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.

g.make a histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model?

FREQ - Number of transactions in last year

LAST UPDATE - Number of days since last update to customer record

WEB - Whether customer purchased by web order at least once

GENDER - Male/Female

ADDRESS RES - whether it is a residential address

ADDRESS US - whether it is a US address

SPENDING (response) - Amount spent by customer in test mailing (in $)