Regression Analysis Solutions

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Instructor Solutions Manual to accompany Applied Linear Statistical Models Fifth Edition

Michael H. Kutner Emory University Christopher J. Nachtsheim University of Minnesota John Neter University of Georgia William Li University of Minnesota

2005 McGraw-Hill/Irwin Chicago, IL Boston, MA

PREFACE
This Solutions Manual gives intermediate and final numerical results for all end-of-chapter Problems, Exercises, and Projects with computational elements contained in Applied Linear Statistical M odels, 5th edition. This Solutions Manual also contains proofs for all Exercises that require derivations. No solutions are provided for the Case Studies. In presenting calculational results we frequently show, for ease in checking, more digits than are significant for the original data. Students and other users may obtain slightly different answers than those presented here, because of different rounding procedures. When a problem requires a percentile (e.g. of the t or F distributions) not included in the Appendix B Tables, users may either interpolate in the table or employ an available computer program for finding the needed value. Again, slightly different values may be obtained than the ones shown here. We have included many more Problems, Exercises, and Projects at the ends of chapters than can be used in a term, in order to provide choice and flexibility to instructors in assigning problem material. For all major topics, three or more problem settings are presented, and the instructor can select different ones from term to term. Another option is to supply students with a computer printout for one of the problem settings for study and class discussion and to select one or more of the other problem settings for individual computation and solution. By drawing on the basic numerical results in this Manual, the instructor also can easily design additional questions to supplement those given in the text for a given problem setting. The data sets for all Problems, Exercises, Projects and Case Studies are contained in the compact disk provided with the text to facilitate data entry. It is expected that the student will use a computer or have access to computer output for all but the simplest data sets, where use of a basic calculator would be adequate. For most students, hands-on experience in obtaining the computations by computer will be an important part of the educational experience in the course. While we have checked the solutions very carefully, it is possible that some errors are still present. We would be most grateful to have any errors called to our attention. Errata can be reported via the website for the book: http://www.mhhe.com/KutnerALSM5e. We acknowledge with thanks the assistance of Lexin Li and Yingwen Dong in the checking of Chapters 1-14 of this manual. We, of course, are responsible for any errors or omissions that remain. Michael H. Kutner Christopher J. Nachtsheim John Neter William Li

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Contents
1 LINEAR REGRESSION WITH ONE PREDICTOR VARIABLE 2 INFERENCES IN REGRESSION AND CORRELATION ANALYSIS 3 DIAGNOSTICS AND REMEDIAL MEASURES 1-1 2-1 3-1

4 SIMULTANEOUS INFERENCES AND OTHER TOPICS IN REGRESSION ANALYSIS 4-1 5 MATRIX APPROACH TO SIMPLE LINEAR REGRESSION ANALYSIS 5-1 6 MULTIPLE REGRESSION – I 7 MULTIPLE REGRESSION – II 6-1 7-1

8 MODELS FOR QUANTITATIVE AND QUALITATIVE PREDICTORS 8-1 9 BUILDING THE REGRESSION MODEL I: MODEL SELECTION AND VALIDATION 9-1 10 BUILDING THE REGRESSION MODEL II: DIAGNOSTICS 10-1

11 BUILDING THE REGRESSION MODEL III: REMEDIAL MEASURES 11-1 12 AUTOCORRELATION IN TIME SERIES DATA 12-1

13 INTRODUCTION TO NONLINEAR REGRESSION AND NEURAL NETWORKS 13-1 14 LOGISTIC REGRESSION, POISSON REGRESSION,AND GENERALIZED LINEAR MODELS 14-1 15 INTRODUCTION TO THE DESIGN OF EXPERIMENTAL AND OBSERVATIONAL STUDIES 15-1 16 SINGLE-FACTOR STUDIES 17 ANALYSIS OF FACTOR LEVEL MEANS iii 16-1 17-1

18 ANOVA DIAGNOSTICS AND REMEDIAL MEASURES

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19 TWO-FACTOR ANALYSIS OF VARIANCE...
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