Age(Years) (x3) Selling Price (Y) Square Footage (x1) Bedrooms (x2) 30 84,000 1670 2 25 2 79000 1339 3 30 945000 1712 3 40 120000 1840 3 18 127500 2300 3 30 132500 2234 3 19 145000 2311 2377 3 164000 7 4 10 155000 2736 3 1 168000 2500 2500 172500 4 3 174000 2479 3 3 175000 2400 3 1 177500 3124 4 0 184000 2500 3 2 195500 4062 4 10 195000 2854 3 3 Develop a regression model using ONLY data for the selling price and square footage Develop a regression model using ONLY data for the selling price and number of bedrooms Develop a regression model using ONLY data for the selling price and age of the home Develop a repression model using data for the selling price, square footage, number of bedrooms and the age of the houses. Please be advised that you will create an output for EACH scenario listed above. DO NOT combine all's until the very last item in number 4. Each x variable should be done by itself on a separate output with its own model Using the multiple regression model output created for question 4 answer the questions below. What is the measure of the strength of the linear relationship between the selling price and the age of the houses, square footage and the ruumber of bedroom? What is the proportion of variability in the selling price that is explained by the square footage number of bedrooms and the age of the houses! What is the Mean Squared Error What is the Mean Square Repression What is the measure of the total variability in selling price about the mean? What is the variability in the selling price about the regression line . What is the total variability in the selling price explained by the regression modell . Give the statistic, long with the degrees of freedom and the value. Is the model useful . If you answered yes to question pive the selling price of the home if the age is 15 years, the square footage is 4382 and the number of bedrooms is 5