Week 
Date 
Topic 
Readings 
1 
Jan 9, 11, 13 
Multiple regression 
Multiple regression in general
 Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 5, "Multiple linear regression", pp. 125–149. UNC ebook
 Weisberg, Sanford. 2005. Applied Linear Regression. Wiley, Hoboken, NJ. Chapter 3, "Multiple regression", pp. 47–68. UNC ebook
Categorical predictors in regression
 Weisberg, Sanford. 2005. Applied Linear Regression. Wiley, Hoboken, NJ. Section 6.2, "Factors", pp. 122–130. UNC ebook
 DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 4, "Multiple regression with categorical predictors: ANOVA and ANCOVA models", pp. 126–154. UNC ebook
Introduction to R
 Allerhand, Mike. 2011. A Tiny Handbook of R. Springer, New York. UNC ebook
 Zuur, Alain F., Elena N. Ieno, and Erik H. W. G. Meesters. 2009. A Beginner's Guide to R. Springer, New York. UNC ebook
For Friday lab session
 RIKZ data: Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 5, "Linear regression", pp. 49–77. Chapter 27, "Univariate and multivariate analysis applied on a Dutch sandy beach community", pp. 485–489. UNC ebook

2 
Jan 18, 20 
Multiple regression (continued) 
R^{2} and confidence intervals
 King, Gary. 1986. How not to lie with statistics: Avoiding common mistakes in quantitative political science. American Journal of Political Science 30: 666–687. Available online Read pp. 675–678.
 Payton, M. E., Greenstone, M. H., and Schenker N. 2003. Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? 6pp. Journal of Insect Science 3: 34. Available online

3 
Jan 23, 25, 27 
Likelihood theory 
Maximum likelihood estimation
 DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 1, "Appendix: Statistical review", pp. 17–37. UNC ebook
 Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 2, "The maximum likelihood approach to ordinary regression", pp. 21–26. UNC ebook
 Myung, In Jae. 2003. Tutorial on maximum likelihood estimation. Journal of Mathematical Psychology 47: 90–100. UNC online journal
AIC
 Mazerolle, Marc J. 2006. Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. AmphibiaReptilia 27: 169–180. UNC online journal
 Anderson, D. R., K. P. Burnham and W. L. Thompson. 2000. Null hypothesis testing: problems, prevalence, and an alternative. Journal of Wildlife Management 64: 912–923. UNC online journal

4–5 
Jan 30, Feb 1, 3, 6, 8, 10 
Generalized linear models 
GLMs in general
 Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 12, "The generalization to nonnormal distributions", pp. 190–196. UNC ebook
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 7, "Logistic regression and generalised linear models", pp. 117–138. UNC ebook
 Vittinghoff, Eric, Stephen C. Shiboski, David V. Glidden and Charles E. McCulloch. 2005. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer, New York. Chapter 9, "Generalized linear models", 291–303. UNC ebook
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 6, "Generalised linear modelling", pp. 79–96. Chapter 21, "Analysing presenceabsence data for flatfish distribution in the Tagus estuary, Portugal", pp. 389–402. UNC ebook
Logistic regression
 Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 8, "Logistic regression", pp. 263–303. UNC ebook
 Kleinbaum, David G. 2010. Logistic Regression: A Selflearning Text. Springer, New York. Chapters 1–3, pp. 1–101. UNC ebook
Poisson regression
 Curran, James Michael. 2011. Introduction to Data Analysis with R for Forensic Scientists. CRC Press, Boca Raton, FL. Chapter 6, "Modeling count and proportion data", 211–256. UNC ebook
 DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Chapter 10, "Regression models for an event count", pp. 348–378. UNC ebook

6 
Feb 13, 15, 17 
Analysis of temporally correlated data 
 Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 9, "Serially Correlated Errors", pp. 305–329. UNC ebook
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 16, "Time series analysis—Introduction", pp. 265–288. UNC ebook
 Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 6, "Violation of independence—Part I", pp. 143–160. UNC ebook
 Fox, John. 2010. Timeseries regression and generalized least squares in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA.
http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/AppendixTimeseriesRegression.pdf

7 
Feb 20, 22, 24 
Mixed effects models 
 Sheather, Simon J. 2009. A Modern Approach to Regression with R. Springer, New York. Chapter 10, "Mixed Models",
pp. 331–369. UNC ebook
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 12, "Analyzing longitudinal data I", pp. 213–230. UNC ebook
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 8, "Introduction to mixed modelling", pp. 125–142. UNC ebook
 Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 5, "Mixed effects modelling for nested data", pp. 101–142. UNC ebook
 Robinson, Andrew P., and Jeff D. Hamann. 2011. Forest Analytics with R: An Introduction. Springer, New York. Chapter 7, "Fitting linear hierarchical models", pp. 219–273. UNC ebook

8 
Feb 27, 29, Mar 2 
Generalized estimating equations 
General discussion
 Palta, Mari. 2003. Quantitative Methods in Population Health: Extensions of Ordinary Regression. Wiley, Hoboken, NJ. Chapter 15, "Modeling correlated outcomes with generalized estimating equations", pp. 263–267. UNC ebook
 Fieberg, John, Randall H. Rieger, Michael C. Zicus, and Jonathan S. Schildcrout. 2009. Regression modelling of correlated data in ecology: subjectspecific and population averaged response patterns. Journal of Applied Ecology 46(5): 1018–1025. UNC online journal
 Vittinghoff, Eric, Stephen C. Shiboski, David V. Glidden and Charles E. McCulloch. 2005. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. Springer, New York. Section 8.4, "Generalized estimating equations", 266–273. UNC ebook
 Ziegler, A. and M. Vens. 2010. Generalized estimating equations: Notes on the choice of the working correlation matrix. Methods of Information in Medicine 49: 421–425. UNC online journal
R implementation
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 13, "Analyzing longitudinal data II", pp. 231–252. UNC ebook
 Halekoh, Ulrich, Søren Højsgaard, and Jun Yan. 2006. The R package geepack for generalized estimating equations. Journal of Statistical Software 15(2). http://www.jstatsoft.org/v15/i02/paper
 Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 12, "Generalised estimating equations", pp. 295–321. UNC ebook

9 
Mar 5, 7, 9 
Spring Break 
No Class 
10 
Mar 12, 14, 16 
Generalized additive models 
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 7, "Additive and generalized additive modelling", pp. 97–124. UNC ebook
 Zuur, Alain F., Elena N. Ieno, Neil J. Walker, Anatoly A. Savelieve, and Graham M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York. Chapter 3, "Things are not always linear; additive modelling", pp. 35–69. UNC ebook
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 9, "Smoothers and generalised additive models", pp. 177–196. UNC ebook
 Fox, John. 2010. Nonparametric regression in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA.
http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/AppendixNonparametricRegression.pdf
 Crawley, Michael J. 2002. Statistical Computing: An Introduction to Data Analysis Using SPlus. Wiley, New York. Extra chapters. Chapter 34: Generalized additive models.
http://www.bio.ic.ac.uk/research/mjcraw/statcomp/chapter34.pdf

11 
Mar 19, 21, 23 
Survival analysis 
 Kleinbaum, David G. and Mitchel Klein. 2005. Survival Analysis: A Selflearning Text. Springer, New York. Chapter 1, "Introduction to survival analysis", pp. 1–43. Chapter 3, "The Cox proportional hazards model and its characteristics", pp. 83–129. Chapter 7, "Parametric survival models", pp. 257–329. UNC ebook
 DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Chapter 11, "Introduction to survival analysis", pp. 382–401. UNC ebook
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 11, "Survival analysis", pp. 197–212. UNC ebook
 Fox, John. 2010. Coxproportionalhazards regression for survival data in R. Web appendix to An R Companion to Applied Regression. Sage, Thousand Oaks, CA. http://socserv.socsci.mcmaster.ca/jfox/Books/Companion/appendix/AppendixCoxRegression.pdf

12 
Mar 26, 28, 30 
Spatial statistics 
 Bivand, Roger. 2008. Applied Spatial Data Analysis with R. Springer, New York. Chapter 8, "Interpolation and geostatistics", pp. 191–209. UNC ebook
 Härdle, Wolfgang. 2007. Statistical Methods for Biostatistics and Related Fields. Springer, New York. Chapter 8, "Spatial statistics", 285–297. UNC ebook
 Schabenberger, Oliver and Carol A. Gotway. 2005. Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton, FL. Chapter 1, "Introduction", pp. 1–37. UNC ebook

13 
Apr 2, 4 
Spatiotemporal correlation 

Gumpertz, M. L., C.T. Wu, and J. M. Pye. 2000. Logistic regression for southern pine beetle outbreaks with spatial and temporal autocorrelation. Forest Science 46: 95–107. http://www.srs.fs.usda.gov/econ/pubs/misc/mlg001.pdf
 Schabenberger, Oliver and Carol A. Gotway. 2005. Statistical methods for spatial data analysis. Chapman & Hall/CRC, Boca Raton, FL. Chapter 9, "SpatioTemporal Process", pp. 431–435. UNC ebook

14 
Apr 9, 11, 13 
Machine learning methods 
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Chapter 9, "Univariate tree models", pp. 143–161. UNC ebook
 Berk, Richard A. 2008. Statistical Learning from a Regression Perspective. Springer, New York. Chapter 3, "Classification and regression trees", 103–167. UNC ebook
 Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, New York. Chapter 9, "Additive models, trees, and related methods", pp. 305–317. UNC ebook
 Everitt, Brian. 2010. A handbook of statistical analyses using R. CRC Press, Boca Raton, FL. Chapter 9, "Recursive partitioning", pp. 161–176. UNC ebook

15 
Apr 16, 18, 20 
Models for multinomial data 
 DeMaris, Alfred. 2004. Regression with Social Data: Modeling Continuous and Limited Response Variables. Wiley, Hoboken, NJ. Chapter 8, "Multinomial models", pp. 294–308. UNC ebook
 Kleinbaum, David G. 2010. Logistic Regression: A Selflearning Text. Springer, New York. Chapters 12–13, pp. 429–488. UNC ebook
 Zuur, Alain F., Elena N. Ieno, and Graham M. Smith. 2007. Analysing Ecological Data. Springer, New York. Section 9.4, "A detailed example: Ditch data", pp. 152–161. UNC ebook
 Guisan, Antoine and Frank E. Harrell. 2000. Ordinal response models in ecology. Journal of Vegetation Science 11(5): 617–626. UNC online journal
 Thompson, Laura. 2009. An S Manual to Accompany Agresti's Categorical Data Analysis, 2nd ed. (2002). pp 35–39, 117–147. https://home.comcast.net/~lthompson221/Splusdiscrete2.pdf

16 
Apr 23, 25 
Survey sampling 
 Köhl, Michael, S.S. Magnussen, M. Marchetti. 2006. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Springer, Berlin. Chapter 3, "Sampling in forest surveys", 71–112. UNC ebook
 Robinson, Andrew P., and Jeff D. Hamann. 2011. Forest Analytics with R: An Introduction. Springer, New York. Chapter 3, "Data analysis for common inventory methods", pp. 94–106. UNC ebook
 Lumley, Thomas. 2007. Survey Analysis in R. http://faculty.washington.edu/tlumley/survey/
This web site contains a wealth of documentation about the R survey package. Included are help pages, textbook examples, and slides from tutorials. Particularly useful are specifying a survey design, simple summary statistics, regression models, and the slides from a tutorial presented at useR 2006.


Apr 30 

Final Exam, Monday, 2 pm 