Calculations Shown

1. ) Refer to the Real Estate Data “Goodyear”, which reports information on homes sold in Goodyear,Arizona last year. To receive full credit you must use the Data Analysis option in Excel where applicable to answer the following questions. a) Let selling price be the dependent variable and size of the home the independent variable. Determine the regression equation. Estimate the selling price for a home with an area of 2,200 square feet. Determine the 95% confidence interval and the 95% prediction interval for the selling price of a home with 2,200 square feet. b) Let selling price be the dependent variable and distance from the center of the city the independent variable. Determine the regression equation. Estimate the selling price of a home 20 miles from the center of the city. Determine the 95% confidence interval and the 95% prediction interval for homes 20 miles from the center of the city. 2.) Refer to the Baseball 2012 data “Baseball”, which report information on the 30 Major League Baseball teams for the 2012 season. Let the number of games won be the dependent variable and the following variables be independent variables: team batting average, number of stolen bases, number of errors committed, team ERA, number of home runs, and whether the team plays in the American or National League. Clearly label each worksheet to reflect the question being answered (for example: Problem 3 part a). To receive full credit you must use the Data Analysis option in Excel where applicable to answer the following questions. a) Determine the multiple regression equation. Discuss each of the variables. For each variable, explain what the sign of the regression coefficient was, what the magnitude was (i.e., if x goes up by one unit how much does y increase by), and whether or not the results of your regression were consistent with what you believed the sign and magnitude to be. b) Find the coefficient of determination for this set of independent variables. c) Develop a correlation matrix. Which independent variables have strong or weak correlations with the dependent variable? Do you see any problems with multicollinearity? d) Conduct a global test on the set of independent variables. Interpret. e) Conduct a test of hypothesis on each of the independent variables. Would you consider deleting any of the variables? If so, which ones? Use a significance level of 5%. f) Rerun the analysis until only significant regression coefficients remain in the analysis. Identify these variables. Use a significance level of 5%. g) Write down the equation of your regression line found in part f including all variables. Calculate the average value for each of your explanatory (independent) variables using the average function. Finally make a prediction for the price if each of the variables take their average value (i.e., use the averages of each variable to get the predicted number of wins).