INSTRUCTIONS Then, try the following steps for two different datasets. 1) Check the coefficients of all the predictor variables. Is at least one of the predictor variables significantly related to the response variable? List the values to be considered and explain. 2) Which predictor variable(s) is (are) significantly associated with the response variable? List the variable(s) and explain why. 3) Go through the model selection methods; the Backwards elimination and the Forward selection with the p-value approach (as introduced on the note). Explain the steps you took and the reason why you selected your final model. 4) List the multiple regression for your final model. 5) Check the adjusted R-squared of your final model and explain what it means. Datasets: a) Fish: Fish.csvDownload Fish.csv Response variable = Weight Other variables are: Weight: Weight of fish in gram Length1: Vertical length in cm Length2: Diagonal length in cm Length3: Cross length in cm Height: Height in cm Width: Diagonal width in cm b) Insurance: VehicleInsuranceData.csvDownload VehicleInsuranceData.csv Response variable = clv (Customer lifetime value; the total revenue or profit generated by a customer over the entire course of their relationship with your business.)