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.\"I Use the Linear Regression operator to build a linear regression model to predict tip percent, setting the feature selection parameter 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. using Net Sales, Qty, Percent Coffee, and Percent Gelato to predict Tip Percent is clearly better than simply predicting the average Tip Percent for 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