A large Toyota car dealership offers purchasers of new Toyota cars the option to buy their used car as part of a trade-in. In particular, a new promotion promises to pay high prices for used Toyota Corolla cars for purchasers of a new car. The dealer then sells the used cars for a small profit. To ensure a reasonable profit, the dealer needs to be able to predict the price that the dealership will get for the used cars. For that reason, data were collected on all previous sales of used Toyota Corollas at the dealership. The data include the sales price and other information on the car, such as its age, mileage, fuel type, and engine size. A description of each of these variables is given in the table below: Variable Description Id Record ID Mode Model Description Price Offer Price Age Age in months KM Accumulated Kilometers on odometer Fuel_Type Fuel Type (Petrol, Diesel, CNG) HP Horse Power Metallic Metallic Color? (Yes=1, No=0) Automatic Automatic ( (Yes=1, No=0) CC Cylinder Volume in cubic centimeters Doors Number of doors QuartTax Quarterly road tax in EUROs Weight Weight in Kilograms The total number of records in the dataset is 1000 cars (see the attached access file: ioe373w22-hw10). Use multiple linear regression to develop a prediction model for the offer price. Use software of your choice to fit the model. Task and Deliverables: 1. Develop a query to prepare your data analysis table. Use indicator variables to transform the Fuel Type, which is a categorical value. 2. Divide the data into training and validation sets (at 50% and 50% respectively).Fit a multiple linear regression model between price (the dependent variable) and the other variables (as predictors) using the training set only. What factors are signicant? Use the validation set to test the regression model obtained in part 2, how does it compare? (e.g. R-Sq) Copy/Paste the Results of the Linear Regression from part 3 in a word document and briey discuss and interpret the results (e.g. what are the signicant predictors). Also attach the output of the software you used to t the linear regression with your analysis (6. g. excel, R, Python, Orange)