Task: To predict the Toyota car price by performing predictive analysis using the Regression technique in Excel. Given: Toyota Corolla data including historic price along with variables affecting the price. Determine the following and explain (add comments on the Excel tabs) each one of the following: 1. Apply Regression prediction technique and develop models. Then indicate which is the best model depending on R^2 values and p-values. Explain the results. (50 points) Note: Best Model selection criteria - First look at R^2 then look at p-values. If R^2 is not much different between models (Model 1 - 82% and Model 2 - 81%) then choose the one that has better p-values for input variables - that is the p value of all the variables should be less than 0.15. So basically we can keep doing regression, eliminating variables and getting new models till the p-values of all the input variables in the model are less than 0.15 2. Plot the Residual plot of the 'best' model and explain your observations. (20 point) Note: On the Regression window - Remember to check 'Residuals' option. You DO NOT need to select the 'Residual Plots' option. 3. Predict the Sales price for the two cars on the 'new data' tab. You have to use the formula using values from the 'best model'. (30 points)