Instructor Solutions Manual

to accompany

Applied Linear Statistical Models Fifth Edition by 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 flnal numerical results for all end-of-chapter

Problems, Exercises, and Projects with computational elements contained in Applied Linear

Statistical Models, 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 signiflcant for the original data. Students and other users may obtain slightly

difierent answers than those presented here, because of difierent 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 flnding the needed value. Again, slightly difierent 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 difierent 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 1-1

2 INFERENCES IN REGRESSION AND CORRELATION ANALYSIS 2-1

3 DIAGNOSTICS AND REMEDIAL MEASURES 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 6-1

7 MULTIPLE REGRESSION { II 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 MEASURES11-1

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 16-1

17 ANALYSIS OF FACTOR LEVEL MEANS 17-1

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18 ANOVA DIAGNOSTICS AND REMEDIAL MEASURES 18-1

19 TWO-FACTOR ANALYSIS OF VARIANCE WITH EQUAL SAMPLE

SIZES 19-1

20 TWO-FACTOR STUDIES { ONE CASE PER TREATMENT 20-1

21 RANDOMIZED COMPLETE BLOCK DESIGNS 21-1

22 ANALYSIS OF COVARIANCE 22-1

23 TWO-FACTOR STUDIES WITH UNEQUAL SAMPLE SIZES 23-1

24 MULTIFACTOR STUDIES 24-1

25 RANDOM AND MIXED EFFECTS MODELS 25-1

26 NESTED DESIGNS, SUBSAMPLING, AND PARTIALLY NESTED DESIGNS 26-1

27 REPEATED MEASURES AND RELATED DESIGNS 27-1

28 BALANCED INCOMPLETE BLOCK, LATIN SQUARE, AND RELATED

DESIGNS 28-1

29 EXPLORATORY EXPERIMENTS { TWO-LEVEL FACTORIAL AND

FRACTIONAL FACTORIAL DESIGNS 29-1

30 RESPONSE SURFACE METHODOLOGY 30-1

Appendix D: RULES FOR DEVELOPING ANOVA MODELS AND TABLES

FOR BALANCED DESIGNS D.1

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Chapter 1

LINEAR REGRESSION WITH ONE

PREDICTOR VARIABLE