Reference no: EM131092744
Refer to Commercial properties Problem 6.18. Assume that regression model (6.5) for four predictor variables with independent normal error terms is appropriate. Three properties with the following characteristics did not have any rental information available.
Develop separate prediction intervals for the rental rates of these properties, using a 95 percent statement confidence coefficient in each case. Can the rental rates of these three properties be predicted fairly precisely? What is the family confidence level for the set of three predictions?
Problem 6.18
Commercial properties. A commercial real estate company evaluates vacancy rates, square footage, rental rates, and operating expenses for commercial properties in a large metropolitan area in order to provide clients with quantitative information upon which to make rental decisions. The data below are taken from 81 suburban commercial properties that are the newest, best located, most attractive, and expensive for five specific geographic areas. Shown here are the age (XI), operating expenses and taxes (Xl),vacancy rates (X3), total square footage (X4),
a. Prepare a stem-and-leaf plot for each predictor variable. What information do these plots provide?
b. Obtain the scatter plot matrix and the correlation matrix. Interpret these and state your principal findings.
c. Fit regression model (6.5) for four predictor variables to the data. State the estimated regression function.
d. Obtain the residuals and prepare a box plot of the residuals. Does the distribution appear to be fairly symmetrical?
e. Plot the residuals against 
, each predictor variable, and each two-factor interaction tenn on separate graphs. Also prepare a normal probability plot. Analyze your plots and summarize your findings.
f. Can you conduct a formal test for lack of fit here?
g. Divide the 81 cases into two groups. placing the 40 cases with the smallest fitted values 
into group I and the remaining cases into group 2. Conduct the Brown-Forsythe test for constancy of the error variance, using ex = .05. State the decision rule and conclusion.
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