- The (rows) and 91 Problem 5: (15 pts regression analysis, visualization, and interpretation): indian_housing_data.csv dataset contains 27,900 observations (rows) variables/features (columns), whereby 9s are missing values in the survey. We are interested in which independent variables are significant for predicting the house price (exactPrice) by the other predictors. a. (5 points) Before running any regressions make sure to check for multicollinearity. How did you check for multicollinearity? If there is multicollinearity, how do you plan to resolve it? Are there any other issues with the dataset we must consider before running the regressions? b. Run a multiple regression of price on the variables listed above. i. (5 points) Run the model using an automatic method (i.e., stepwise, forward, backward). Explain why you chose the method. Comment on the overall significance of the regression fit. Which predictors have coefficients that are significantly different from zero at the .05 level? ii. (5 points) Using the variables above, create a visualization, which will provide an interesting story or insight within this data.