Problem 2 Predicting Software Reselling Profits. Tayko Software is a software catalog firm that sells games and educational software. It started out as a software manufacturer and then added third-party titles to its offerings. It recently revised its collection of items in a new catalog which it mailed out to its customers. This mailing yielded purchases. Based on these data, Tayko wants to devise a model for predicting the spending amount that a purchasing customer will yield. The file Tayko2.cvs contains information on purchases. a. (10 points) Explore the spending amount by creating a pivot table for the frequency and spending. Compute the average and standard deviation of spending in each category. b. (10 points) Explore the relationship between spending and each of the two continuous predictors by creating two scatterplots (SPENDING vs. FREQ, and SPENDING vs. LAST UPDATE). Does there seem to be a linear relationship? Why? Why not? Explain briefly. c. To fit a predictive model for SPENDING: i. (10 points) Partition the records into training and validation sets. ii. (10 points) Run a multiple linear regression model for SPENDING vs. the two continuous predictors. Give the estimated predictive equation. iii. (10 points) Based on this model, what type of purchaser is most likely to spend a large amount of money? iv. (10 points) If we used backward elimination to reduce the number of predictors, which predictor would be dropped first from the model? v. (10 points) Show how the prediction and the prediction error are computed for the first purchase in the validation set. vi. (10 points) Evaluate the predictive accuracy of the model by examining its performance on the validation set. vii. (10 points) Create histogram of the model residuals. Do they appear to follow a normal distribution? How does this affect the predictive performance of the model? The submission must include the codes, visuals, outputs and your comments (No screenshots allowed for anything than the visuals (graphs and plots)). Ensure that you clearly explain your thought process and provide the necessary output from your R analysis. Do not employ any methods we have not covered in our lectures. If you use an AI-tool, cite the entire conversation with all the prompts you have used https://1drv.ms/x/s!Al38sdz7RjyohmPKVbQPDBG8afho?e=oHzTXS&nav=MTVfezY3OTg5QkQxLTQyRjYtNERCNC1BNjc1LTk0NDZDQ0ZBNUJBRH0 You can download the tayko2.csv fle from this link