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Foundations of Linear and Generalized Linear Models by Alan Agresti

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APPENDIX A Supplemental Data Analysis Exercises

Note: One purpose of this appendix is to provide exercises for students that are not tied to methodology of a particular text chapter.

  1. Using an Internet search or viewing the datasets in the R MASS library (by entering the R commands library(MASS) and data()), download a dataset relating a quantitative response variable to at least two explanatory variables. Fit a GLM (a) using one explanatory variable, and (b) using all the explanatory variables in a model-building process. Interpret results.
  2. The MASS library of R contains the Boston data file, which has several predictors of the per capita crime rate, for 506 neighborhoods in suburbs near Boston. Prepare a four-page report describing a model-building process for these data. Attach edited software output as an appendix.
  3. The horseshoe crab dataset Crabs2.dat at www.stat.ufl.edu/∼aa/glm/data comes from a study of factors that affect sperm traits of males. One response variable is total sperm, measured as the log of the number of sperm in an ejaculate. Explanatory variables are the location of the observation, carapace width (centimeters), mass (grams), color (1 = dark, 2 = medium, 3 = light), the operational sex ratio (OSR, the number of males per females on the beach), and a subjective condition number that takes into account mucus, pitting on the prosoma, and eye condition (the higher the better). Prepare a report describing a model-building process for these data. Attach edited ...

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