Tutorial 3 j eck 4! Multiple Regression Tutorial assignment: What are the assumptions of classical linear regression which give rise to the BLUE for ordinary least squares? Objective: Estimate Multiple Regression Model, Perform Ftest, Goodnessofflt There are 6660 observations of data on houses sold from 1999-2002 in Stockton California in the le \"hedonistxis \". L315; : ,61 + alarm, + asses + 648A ms, + gametes, + VACANT, + 5.140s: + u, Ln(Selling Price}=LSP which is a function of: - Size of Living Area (in square feet) -Number of Bedrooms -Number of Bathrooms -Number of Stories "Vacancy status (1 if vacant, 0 if not at the time of the sale) -Age of the house in years 1. Estimate the above hedonic model for the 166? houses sold in the year 2002 in Stockton California. (Note data is for houses sold from 1999-2002 but we are only interested in estimating the equation for those houses sold in 2002) Proc>Set sample> if year=2002 Quick>Estimate Equation Log(selh'ng_price} c sl'la beds baths stories vacant age Type in the window for sample: I 6660 if year=2002 2. Compute the appropriate slope for each explanatory variable at the mean, minimum and maximum value of selling price. Interpret your results. (Note- this is a log-linear model) 3. Discuss the results in relation to a. Goodness of t b. Individual signicance of the explanatory variables 4. Test the overall signicance of the model. What is the restricted model in this case? 5. What assumptions underlie the OLS regression model we just estimated? 1