It is always good to adhere to model simplicity where you build a regression model that has the fewest number predictors needed to explain the variability found in the response variable. Because of this, we will not immediately use the k = 4 model (four predictors). 4. Obtain a correlation matrix plot (an array of scatterplots) and pairwise correlations for all quantitative variables, including the response variable. Use the variable order found in the data dictionary above. Reminder: Stat > Basic Stats > Correlation: Under Graphs select: Correlations When you look at the scatterplots of the response variable versus each predictor variable: A. Is there one predictor that shows a very strong linear relationship with the response variable? Yes B. Is there one predictor that shows a nonlinear relationship with the response variable which would suggest that it should not be included in the multiple regression model? Non lineaer relationship and should not be included in the multiple redgression model C. Are there two predictor variables that show a rather strong linear relationship which could suggest that both predictors may not be needed in the model? 1 and 3 show a strong linear suggest that both predictors may not be nedded in model 5. Fit the k = 3 multiple regression model where you include predictors: AudienceScore, GQpenWkend, and Budget. Reminder: Stat > Regression > Regression > Fit Regression Model: Under Results select: Basic Tables 6. Which predictors are effective at the 0.05 level? Which is the most effective predictor (Note: you will need to go beyond just looking at the p-value)? 7. Is there a plausible explanation as to why budget is not an effective predictor? 8. With the k = 3 fitted model, interpret the coefficient of G OpenWkend in context. 9. With the k = 3 fitted model, Interpret R-squared in context