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10 - 5 I Ho:B3=0 v H1:B3≠0 II Since p-v=0.378>.05 we...

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Minitab #10 Part I. Fitting the Regression 1. High School graduates admitted to USC in the last 10 years who completed more than  one year of coursework at USC. 2. Representative for graduates who were admitted in the last 10 years, but not for current  or future students. 3. GPA=0.257+0.00213 SATMath+0.246 Hs Math + 0.176 HsEngl 4. C1=y, C2=x1, C3=x2, C4=x3 5. Mean of GPA= 2.593, Median of GPA=2.615 6. 1.985 is the fit 7. The Fit and the Mean can be compared using R^2 8. R-Sq=80.2% 9. R-Sq=80.2% 10. Point estimate has no warranty, so point estimate for a single GPA and the mean GPA  are the same. Part II. The Utility of the Model 1.  95% PI (1.3082, 2.6620) 2. 95% CI (1.7642, 2.2061) 3. The best GPA you are likely to get is 2.6620 (upperbound). 4. I. Ho: B1=B2=B3=0 v. H1:B1 or B2 or B3≠0 II. Since p-v=0.005<.05, we reject Ho III. Thus, the three variables are useful in predicting GPA in the population
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Unformatted text preview: 5. I. Ho:B3=0 v. H1:B3≠0 II. Since, p-v=0.378>.05, we accept Ho III. Thus, HsEngl is not a useful predictor of GPA 6. I. Ho:B2=0 v. H1:B2≠0 II. Since p-v=.024<.05, we reject Ho III. Thus, HsMath is a useful predictor og GPA over and above SATMath and Hs Engl 7. I. Ho:B2=<0 v B2>0 II. Since p-v/2=.012, we reject Ho III. Thus, My high school teacher is likely to be vindicated Part III. Residual Analysis 1. 2. Residuals vs. each of the Xs show no pattern 3. Minitab 4. Miniab 5. Durbin-Watson statistic = 2.52912, which is between 1<d<3 6. 4 did not show any pattern, but it fit the D-W statistic 7. Minitab 8. Minitab, It is unimodel 9. Yes, 8/20 10. There are no leverage points Part IV. Watch out for the Pitfalls 1. Range out of 20 2. Students get better and better 3. Correlation between Xs 4. Throw out higher...
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10 - 5 I Ho:B3=0 v H1:B3≠0 II Since p-v=0.378>.05 we...

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