(a) Use multiple Linear Regression model to develop and estimated regression equation to predict the selling price of the home, based on Sq Footage and Age of the home. Clearly write the regression equation. (b) What % of variability in the selling price can be explained by the Sq Footage and age of the home (c) Use excel to construct plots of the residual (error) against dependent and Independent variables. Based on these plots, are conditions necessary for Valid inference are met? (d) Mainline reality management believes that condition of the house may also contribute substantially towards variability in the sale price of the homes. To capture this, company now collects data on another independent variable (whether home is in mint condition?). The value of this dummy variable is 1 if the house is in mint condition and value is 0 if it is not in mint condition. Include additional variable titled "Mint" in your model (in addition to Sq Footage and age) as listed below and redevelop the regression equation. Clearly write the Regression equation. (e) What % of variability in the selling price can now be explained by included "Mint" as additional independent variable. (f) Using this expanded model, predict the selling price of a home that has 1800 Sq footage, age is 28 years and is in Mint condition. 2. Data from sale of 14 homes by Mainline Reality, Inc is provided below: Sq Footage of Home 1926 2069 1720 1396 1706 1847 1950 2323 2285 Selling Price 95000 119000 124800 135000 142800 145000 159000 165000 182000 183000 3752 200000 2300 211000 2525 215000 3800 219000 1740 Age of Home 30 40 30 15 32 38 27 30 26 35 18 17 40 12