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# Homework5Key - Spring 2012 STAT 512 HW 5 Homework 5(25.5...

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Spring 2012 STAT 512 HW 5 1 Homework 5 (25.5 pts.) due Feb. 24 A reminder – Please do not hand in any unlabeled or unedited SAS output. Include in your write-up only those results that are necessary to present a complete solution (what you want the grader to grade). In particular, questions must be answered in order (including graphs), and all graphs must be fully labeled (main title should include the question number, and all axes should be labeled). Don’t forget to put all necessary information (see course policies) on the first page. Include the SAS input for all questions at the very end of your homework; this could be important even though it won’t be graded. You will often be asked to continue problems on successive homework assignments so save all your SAS code. The following problems are a continuation of Problem 3 in HW 4. Consider the data set from Problem 6.18, p. 251 about “Commercial Properties” (CH06PR18.DAT) which describes a data set (n = 81) used to evaluate the relation because rental rates (Y) and the age of the unit (X 1 ), operating expenses and taxes (X 2 ), vacancy rates (X 3 ), total square footage (X 4 ). SAS Code: data HW5P1; infile 'I:\My Documents\Stat 512\CH06PR18.DAT' ; input rental age expense vacancy area; run ; proc print data =HW5P1; run ; *a e; Title1 'Rental Rates' ; proc reg data =HW5P1; model rental = age expense vacancy area/ clb ; output out =rentalout r = resid p =pred; run ; *c; proc corr data =HW5P1; var age expense vacancy area; with rental; run ; *d; proc corr data =HW5P1; var age expense vacancy area; run ; *f; title2 'Residuals vs. predicted values' ; symbol1 v = circle; proc gplot data =rentalout; plot resid * pred/ vref = 0 ; run ; title2 'Residuals vs. age' ; symbol1 v = circle; proc gplot data =rentalout; plot resid * age/ vref = 0 ; run ; title2 'Residuals vs. expense' ; proc gplot data =rentalout; plot resid * expense/ vref = 0 ; run ; title2 'Residuals vs. vacancy' ; proc gplot data =rentalout; plot resid * vacancy/ vref = 0 ; run ; title2 'Residuals vs. area' ; proc gplot data =rentalout; plot resid * area/ vref = 0 ; run ; *g; title2 'Normalality of Residuals' ; proc univariate data =rentalout noprint ; var resid; histogram resid / normal kernel ( L = 2 ); qqplot resid / normal ( L = 1 mu =est sigma =est); run ; *h; data a2; age = 15 ; expense = 9.2 ; vacancy = 0.03 ; area= 90000 ; data predict; set HW5P1 a2; proc reg data =predict; model rental = age expense vacancy area/ cli ; id age expense vacancy area; run ; quit ;

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Spring 2012 STAT 512 HW 5 2 1. (12 pts.) This problem relates to material described in Chapter 6 about multiple regression. (a) Run the multiple linear regression with age, operating expenses, vacancy rates and total square footage as the explanatory variables and rental rate as the response variable. From the data from the last problem set, finish the summary of the regression results by giving the values of R 2 and the adjusted R 2 . This was done in Homework 4:
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Homework5Key - Spring 2012 STAT 512 HW 5 Homework 5(25.5...

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