Assignment 4

Please submit your assignment via Blackboard, as a single Word file.

The file SalesData.xlsx contains five thousand rows of data. Each row summarizes a different customer that has been to CPCG, a local coffee and gelato shop.Net Salesis the dollar cost of the customer's order (not including tip),Qtyis the number of items purchased,Percent Coffeeis the percentage of items that were coffee,Percent Gelatois the percentage of items that were gelato, andTip Percentis how much tip the customer gave, as a percentage ofNet Sales.

(You might see a few strange values forQty, Percent Coffee, andPercent Gelato.There are a few items that have customizable sizes, are considered a combination of coffee and gelato, or are neither coffee nor gelato.You do not have to worry about any of these details for the sake of this assignment.)

1. Building a regression model

a) Use Excel to build a linear regression model predicting the tip percent using all of the other columns in the file.Output the results to a new sheet.Show a screenshot of the regression output.

b) Import the dataset into RapidMiner, making sure to change the role of Tip Percent to "label."Use the Linear Regression operator to build a linear regression model to predict tip percent, setting thefeature selectionparameter to "none."Show a screenshot of the regression output.

(Hint: the coefficients and p-values should be the same for a and b, except for differences in formatting and rounding.)

2. Evaluating regression models

a) Which independent variable(s) are significant in your regression model from Part 1?

b) The linear regression model in Part 1a is clearly significant, i.e. usingNet Sales,Qty,Percent Coffee, andPercent Gelatoto predict Tip Percent is clearly better than simply predicting the averageTip Percentfor every customer.However, R Square is only 0.0136.Why is R Square so low despite the model being significant; how can those two things both be true?

c) Build a process in RapidMiner that applies cross validationto a linear regression model on this dataset and evaluates its performance.Remember that Cross Validation is a nested operator, meaning it will have its own process within the Process panel.Show a screenshot of the Cross Validation process (NOT of the main process; your screenshot should not include the Cross Validation operator itself).You do not need to include the Parameters panel.

In the Cross Validation process, checkuse local random seed, and set thelocal random seedto 12345.

d) In the Linear Regression operator, setfeature selectionto none, as you did in Part 1.Run the process.What is the root mean squared error (RMSE) of this linear regression model?

e) In the Linear Regression operator, setfeature selectionto T-Test.Run the process.What is the root mean squared error (RMSE) of this linear regression model?

Net Sales 35.69 22.835 5.86 4.145 23.5 9.25 5.68 27.25 15.36 31.5 11.8 5.64772727 13.98 3.75 3.75 5.86 15.75 5.86 7.5 4.75 4.25 5.86 11.8 5 29.3 8 17.58 15.5 9.355 18.86 4.5 5.85 5.9 6.99 5.86 8.98333333 12.7875 5.9 5.9 12.4166667 5.9 9.9 Qty 7 4 1 1 3 3 1 4 3 4 2 2.13636364 2 1 1 1 4 1 2 1 1 1 2 1 5 1 3 1 1.5 2 1 1.33333333 1 1 1 2 3 1 1 2.66666667 1 2 Percent Coffee Percent Gelato Tip Percent 50 50 14.9881047 50 50 7.50221631 0 100 0 100 0 21.6786998 33 0 15.0140506 67 0 19.9796126 0 100 0 25 0 0 58 17 17.4958991 50 0 15.0044924 0 100 15.0279776 92 5 19.5053565 0 100 15.0195059 100 0 0 100 0 0 0 100 0 50 0 14.9790294 0 100 0 100 0 20 100 0 0 100 0 0 0 100 15.0387597 0 100 0 100 0 0 0 100 14.9860379 0 0 0 0 100 25.0258532 0 100 14.9726111 0 100 0 0 50 0 0 0 20.2020202 0 50 16.7101088 0 100 15.04 0 100 14.9797571 0 100 0 56 0 32.4019399 83 8 18.7491676 0 100 15.04 0 100 15.04 44 0 14.4505505 0 100 15.04 50 50 14.9666349 11.72 2 10.3847619 1.57142857 5.93333333 1.33333333 22.29 4 50.8 8 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