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
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Address: Curriculum for the Environment and Ecology, Box 3275, University of North Carolina, Chapel Hill, 27599
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Last Revised--April 27, 2012