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Applied Linear Statistical Models (5E) Soluton by Michael Kutner, Christopher Nachtsheim, John Neter, William Li
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**About this book :- **

**Applied Linear Statistical Models (5E) Soluton ** written by
** Michael Kutner, Christopher Nachtsheim, John Neter, William Li **

This is solution of this books. Its text book long established leading authoritative text and reference on statistical modeling, analysis of variance, and the design of experiments. All topics are presented in a precise and clear style supported with solved examples, numbered formulas, graphic illustrations, and "Comments" to provide depth and statistical accuracy and precision.

**Book Detail :- **
** Title: ** Applied Linear Statistical Models (Soluton)
** Edition: ** Fifth Edition
** Author(s): ** Michael Kutner, Christopher Nachtsheim, John Neter, William Li
** Publisher: ** McGraw-Hill Irwin
** Series: ** McGraw-Hill Irwin Series Operations and Decision Sciences
** Year: ** 2004
** Pages: ** 111
** Type: ** PDF
** Language: ** English
** ISBN: ** 0072386886,9780072386882
** Country: ** US

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**About Author :- **

The author John Neter is a German statistician who move to America and become university professor, and widely published author.

He spent much of his career teaching statistics at University of Georgia in Athens, Georgia. In 1965 he was elected as a Fellow of the American Statistical Association. He served as President of the American Statistical Association in 1985.

The author Michael H. Kutner is PhD from Texas A & M University 1971. He Professor of Biostatistics and Chair, Department of Biostatistics and Bioinformatics, Emory School of Public Health.

** Christopher J. Nachtsheim**, University of Minnesota and

** John Neter**, University of Georgia and

** William Li**, Universlty of Minnesota.

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**Book Contents :- **
** Applied Linear Statistical Models (5E) Soluton ** written by
** Michael Kutner, Christopher Nachtsheim, John Neter, William Li **
cover the following topics.
'

1. LINEAR REGRESSION WITH ONE PREDICTOR VARIABLE

2. INFERENCES IN REGRESSION AND CORRELATION ANALYSIS

3. DIAGNOSTICS AND REMEDIAL MEASURES

4. SIMULTANEOUS INFERENCES AND OTHER TOPICS IN REGRESSION ANALYSIS

5. MATRIX APPROACH TO SIMPLE LINEAR REGRESSION ANALYSIS

6. MULTIPLE REGRESSION – I

7. MULTIPLE REGRESSION – II

8. MODELS FOR QUANTITATIVE AND QUALITATIVE PREDICTORS

9. BUILDING THE REGRESSION MODEL I: MODEL SELECTION AND VALIDATION

10. BUILDING THE REGRESSION MODEL II: DIAGNOSTICS

11. BUILDING THE REGRESSION MODEL III: REMEDIAL MEASURES11-1

12. AUTOCORRELATION IN TIME SERIES DATA

13. INTRODUCTION TO NONLINEAR REGRESSION AND NEURAL NETWORKS

14. LOGISTIC REGRESSION, POISSON REGRESSION,AND GENERALIZED LINEAR MODELS

15. INTRODUCTION TO THE DESIGN OF EXPERIMENTAL AND OBSERVATIONAL STUDIES

16. SINGLE-FACTOR STUDIES

17. ANALYSIS OF FACTOR LEVEL MEANS

18. ANOVA DIAGNOSTICS AND REMEDIAL MEASURES

19. TWO-FACTOR STUDIES WITH EQUAL SAMPLE SIZES

20. TWO-FACTOR STUDIES – ONE CASE PER TREATMENT

21. RANDOMIZED COMPLETE BLOCK DESIGNS

22. ANALYSIS OF COVARIANCE

23. TWO-FACTOR STUDIES – UNEQUAL SAMPLE SIZES

24. MULTIFACTOR STUDIES

25. RANDOM AND MIXED EFFECTS MODELS

26. NESTED DESIGNS, SUBSAMPLING, AND PARTIALLY NESTED DESIGNS

27. REPEATED MEASURES AND RELATED DESIGNS

28. BALANCED INCOMPLETE BLOCK, LATIN SQUARE, AND RELATED DESIGNS

29. EXPLORATORY EXPERIMENTS – TWO-LEVEL FACTORIAL AND FRACTIONAL FACTORIAL DESIGNS

30. RESPONSE SURFACE METHODOLOGY

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