Lecture 1 (Monday, January 9): Review of simple linear regression. Multiple linear regression.

Lecture 2 (Wednesday, January 11): Confounding and interaction. Categorical predictors in regression.

Lecture 3 (Friday, January 13): Introduction to R. Data entry and manipulation. Fitting regression models. Plotting regression models using base graphics and lattice.

Lecture 4 (Wednesday, January 18): Statistical tests for a regression model

Lecture 5 (Friday, January 20): Error bar plots for regression models. Checking model predictions.

Lecture 6 (Monday, January 23): Error bar plots for pairwise comparisons

Lecture 7 (Wednesday, January 25): The regression model as a data generating mechanism

Lecture 8 (Friday, January 27): Maximum likelihood estimation of a Poisson regression model

Lecture 9 (Monday, January 30): Properties of maximum likelihood estimates. Likelihood ratio tests and Wald tests. AIC.

Lecture 10 (Wednesday, February 1): AIC as a method for model selection. The Poisson distribution.

Lecture 11 (Friday, February 3): Count regression models

Lecture 12 (Monday, February 6): Negative binomial distribution. Generalized linear models

Lecture 13 (Wednesday, February 8): Residual deviance. Binomial distribution. Logit link and odds ratios.

Lecture 14 (Friday, February 10): Logistic regression

Lecture 15 (Monday, February 13): Generalized least squares and temporal correlation

Lecture 16 (Wednesday, February 15): ARMA models

Lecture 17 (Friday, February 17): Temporal correlation in ordinary regression models

Lecture 18 (Monday, February 20): Introduction to mixed effects models

Lecture 19 (Wednesday, February 22): Multilevel models and random intercept models

Lecture 20 (Friday, February 24): Mixed effects models in R

Lecture 21 (Monday, February 27): Subject-specific and marginal interpretations of mixed effects models

Lecture 22 (Wednesday, February 29): Generalized estimating equations

Lecture 23 (Friday, March 2): Fitting generalized estimating equation models in R (gee and geepack packages)

Lecture 24 (Monday, March 12): Generalized additive models

Lecture 25 (Wednesday, March 14): Local polynomial regression and splines

Lecture 26 (Friday, March 16): Fitting GAMs and GAMMs using the mgcv package of R

Lecture 27 (Monday, March 19): Introduction to survival analysis

Lecture 28 (Wednesday, March 21): Log rank test and regression models for survival data

Lecture 29 (Friday, March 23): Survival analysis in R

Lecture 30 (Monday, March 26): Introduction to spatial statistics

Lecture 31 (Wednesday, March 28): Analytical tools for spatial analysis

Lecture 32 (Friday, March 30): Accounting for spatial correlation in regression models

Lecture 33 (Monday, April 2): Dealing with spatio-temporal correlation in regression models

Lecture 34 (Wednesday, April 4): Constructing a spatio-temporal correlation matrix

Lecture 35 (Monday, April 9): Classification and regression trees

Lecture 36 (Wednesday, April 11): Pruning a regression tree. Random forests

Lecture 37 (Friday, April 13): Tree-based methods in R

Lecture 38 (Monday, April 16): Regression models for multinomial data

Lecture 39 (Wednesday, April 18): Regression models for ordinal multinomial data

Lecture 40 (Friday, April 20): Fitting multinomial regression models in R

Lecture 41 (Monday, April 23): Introduction to survey sampling

Lecture 42 (Wednesday, April 25): Cluster samples and the R survey package

Jack WeissPhone: (919) 962-5930E-Mail: jack_weiss@unc.eduAddress: Curriculum for the Environment and Ecology, Box 3275, University of North Carolina, Chapel Hill, 27599Copyright © 2012 Last Revised--April 27, 2012 URL: https://sakai.unc.edu/access/content/group/2842013b-58f5-4453-aa8d-3e01bacbfc3d/public/Ecol562_Spring2012/docs/lectures.htm |