To fit a predictive model for SPENDING: 1. Partition the 1000 records into training and validation sets. 2. Run a multiple linear regression model for SPENDING versus all six predictors. Give the estimated predictive equation. 3. Based on this model, what type of purchaser is most likely to spend a large amount of money? 4. If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model? 5. Show how the prediction and the prediction error are computed for the first purchase in the validation set. 6. Evaluate the predictive accuracy of the model by examining its performance on the validation set. 7. Create a histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model?