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Unformatted text preview: STA 3024 Summer A 2011 Exam 3 (Form Code X) Name: UFID: This is 75 minutes exam. You have to answer 20 multiple choice questions. Each question is worth 5 points that makes the whole exam worth 100 points. There is only one correct answer for each question. You need to bubble the correct answer on the scantron to get credit. Only scantron and not this exam will be graded. You have to turn in both exam and scantron. You can use a calculator but you cannot use any supplemented materials not included in this exam. On this paper , write your name and UFID . You also need to sign and date this exam below . On your scantron , bubble your name, UFID, and exam Form Code (please see upper right corner of this page for your form code.) You also need to bubble your section number which is 1053 . There are no special codes. A formula sheet and tables are provided on the last page. On my honor I have neither received or given or planning to give any aid on this exam as well as disclose its contents to the ones who is going to take it after me. Name: DATE: Question 8 has a typo. It has to be narrower NOT wider. 1. Multiple Linear Regression Applications. ( class example) Consider the output we discussed in class. We are interested in testing the hypothesis: H : 1 = 2 = 3 = 4 = 0 . Please provide the conclusion. (HINT: Please remember how we test the hypothesis.) Residuals: 1 2 3 4 5 6 7 2.283 -3.571 1.427 4.360 -1.909 -1.331 -1.259 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -21.28747 15.03519-1.416 0.29249 X1 3.91715 0.36283 10.796 0.00847 ** X2 0.15119 0.32980 0.458 0.69164 X3-0.06084 1.07269-0.057 0.95993 X4 21.33153 20.29970 1.051 0.40358--- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 4.796 on 2 degrees of freedom Multiple R-squared: 0.9894, Adjusted R-squared: 0.9681 F-statistic: 46.5 on 4 and 2 DF, p-value: 0.02116 (A) We reject H : 1 = 2 = 3 = 4 = 0 . (B) We fail to reject H : 1 = 2 = 3 = 4 = 0 . (C) We cannot come to the conclusion based on the information given since we do not have full ANOVA table to compute our ANOVA F obs statistic. We cannot come to the conclusion based on the information given since we have not specified level for this test. (D) We cannot come to the conclusion based on the information given since we do not know what statistic ( F obs or t obs ) we should use for this test. 2. SLR and MLR theory. The multiple regression equation picks the values of a as an estimate of and our b i as estimates of slopes i in the way that it makes (A) SS Tot as small as possible. (B) MS Tot as small as possible. (C) SS Regr as small as possible....
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This note was uploaded on 01/08/2012 for the course STA 3024 taught by Professor Ta during the Summer '08 term at University of Florida.
- Summer '08