STUSOLNS

# STUSOLNS - Student Solutions Manual to accompany Applied...

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Student Solutions Manual to accompany Applied Linear Regression Models Fourth Edition Michael H. Kutner Emory University Christopher J. Nachtsheim University of Minnesota John Neter University of Georgia 2004 McGraw-Hill/Irwin Chicago, IL Boston, MA

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PREFACE This Student Solutions Manual gives intermediate and fnal numerical results For all starred (*) end-oF-chapter Problems with computational elements contained in Applied Linear Regression Models , 4th edition. No solutions are given For Exercises, Projects, or Case Studies. In presenting calculational results we Frequently show, For ease in checking, more digits than are signifcant For the original data. Students and other users may obtain slightly di±erent answers than those presented here, because oF di±erent 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 fnding the needed value. Again, slightly di±erent values may be obtained than the ones shown here. 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. ²or 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/KutnerALRM4e. We acknowledge with thanks the assistance oF Lexin Li and Yingwen Dong in the checking oF this manual. We, oF course, are responsible For any errors or omissions that remain. Michael H. Kutner Christopher J. Nachtsheim John Neter i
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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 REGRES- SION ANALYSIS 4-1 5 MATRIX APPROACH TO SIMPLE LINEAR REGRESSION ANALY- SIS 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 NET- WORKS 13-1 14 LOGISTIC REGRESSION, POISSON REGRESSION,AND GENERAL- IZED LINEAR MODELS 14-1 iii
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Chapter 1 LINEAR REGRESSION WITH ONE PREDICTOR VARIABLE 1.20. a.
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STUSOLNS - Student Solutions Manual to accompany Applied...

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