"Applied Linear Statistical Models" Webpage

Applied Linear Statistical Models - Class Notes
From Applied Linear Statistical Models, by Michael Kutner, Christopher Nachtsheim, John Neter, and William Li (McGraw Hill, 2005) Kutner, Nachtsheim, Neter, and Li's Applied Linear Statistical Models book

Kutner, Nachtsheim, Neter, and Li's Applied Linear Statistical Models book

"Applied Linear Statistical Models" is not a formal class at ETSU, but the material here might overlap some with the Statistical Methods sequence (STAT 5710 and 5720). The catalogue description for Statistical Methods 1 (STAT 5710) is: "Population and samples, probability distributions, estimation and testing, regression and correlation analysis, and diagnostic methods for assessing the assumptions of such models." The prerequisites are Linear Algebra (MATH 2010) and Elementary Statistics (MATH 2050 or equivalent). The catalogue description for Statistical Methods 2 (STAT 5710) is: "Generalized linear models, analysis of variance and regression on dummy variables, analysis of covariance, random and mixed effects models, and repeated measures." The prerequisites are Statistical Methods 1 (STAT 5710) or permission of instructor. These descriptions are based on the ETSU 2020-21 Graduate Catalog.

Copies of the classnotes are on the internet in PDF format as given below. The notes and supplements may contain hyperlinks to posted webpages; the links appear in red fonts. The "Proofs of Theorems" files were prepared in Beamer. The "Printout of Proofs" are printable PDF files of the Beamer slides without the pauses. These notes have not been classroom tested and may have typographical errors.

The book and these notes are broken into six parts, as follows, where links are provided to notes on each part.

PART ONE. SIMPLE LINEAR REGRESSION

Chapter 1. Linear Regression with One Predictor Variable.

Chapter 2. Inferences in Regression and Correlation.

Chapter 3. Diagnostics and Remedial Measure.

Chapter 4. Simultaneous Inferences and Other Topics in Regression Analysis.

Chapter 5. Matrix Approach to Simple Linear Regression Analysis.

PART TWO. MULTIPLE LINEAR REGRESSION

Chapter 6. Multiple Regression I.
Chapter 7. Multiple Regression II.
Chapter 8. Regression Models for Quantitative and Qualitative Predictors.
Chapter 9. Building the Regression Model I: Model Selection and Validation.
Chapter 10. Building the Regression Model II: Diagnostics.
Chapter 11. Building the Regression Model III: Remedial Measures.
Chapter 12. Autocorrelation in Time Series Data.

PART THREE. NONLINEAR REGRESSION

Chapter 13. Introduction to Nonlinear Regression and Neural Networks.
Chapter 14. Logistic Regression, Poisson Regression, and Genearalized Linear Models.

PART FOUR. DESIGN AND ANALYSIS OF SINGLE-FACTOR STUDIES

Chapter 15. Introduction to the Design of Experimental and Observational Studies.
Chapter 16. Single-Factor Studies.
Chapter 17. Analysis of Factor Level Means.
Chapter 18. ANOVA Diagnostics and Remedial Measures.

PART FIVE. MULTI-FACTOR STUDIES

Chapter 19. Two-Factor Studies with Equal Sample Sizes.
Chapter 20. Two-Factor Studies - One Case per Treatment.
Chapter 21. Randomized Complete Block Designs.
Chapter 22. Analysis of Covariance.
Chapter 23. Two-Factor Studies with Unequal Sample Sizes.
Chapter 24. Multi-Factor Studies.
Chapter 25. Random and Mixed Effects Models.

PART SIX. SPECIALIZED STUDY DESIGNS

Chapter 26. Nested Designs, Subsampling, and Partially Nested Designs.
Chapter 27. Repeated Measures and Related Designs.
Chapter 28. Balanced Imcomplete Block, Latin, and Related Designs.
Chapter 29. Exploratory Experiments: Two-Level Factorial and Fractional Factorial Designs.
Chapter 30. Response Surface Methodology.

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