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.

"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 book and these notes are broken into six parts. Links to the parts are given on my main Applied Linear Statistical Models webpage.

Chapter 13. Introduction to Nonlinear Regression and Neural Networks.

• 13.1. Linear and Nonlinear Regression.
• 13.2. Least Squares Estimation in Nonlinear Regression.
• 13.3. Model Building and Diagnostics.
• 13.4. Inferences about Nonlinear Regression Parameters.
• 13.5. Learning Curve Example.
• 13.6. Introduction to Neural Network Modeling.
• Study Guide 13.

Chapter 14. Logistic Regression, Poisson Regression, and Genearalized Linear Models.

• 14.1. Regression Models with Binary Response Variable.
• 14.2. Sigmoidal Responce Functions for Binary Responses.
• 14.3. Simple Logistic Regression.
• 14.4. Multiple Logistic Regression.
• 14.5. Inferences about Regression Parameters.
• 14.6. Automatic Model Selection Methods.
• 14.7. Tests for Goodness of Fit.
• 14.8. Logistic Regression Diagnostics.
• 14.9. Inferences about Mean Response.
• 14.10. Prediction of a New Observation.
• 14.11. Polytomous Logistic Regression for Nominal Response.
• 14.12. Polytomous Logistic Regression for Ordinal Response.
• 14.13. Poisson Regression.
• 14.14. Generalized Linear Models.
• Study Guide 14.

Return to Bob Gardner's home page