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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
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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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