Statistical Modeling

Basic information

Workload: 

60 hours

Prerequisite: 

Statistical inference

Syllabus: 

Simple linear regression, Fit analysis, Residual study, Multiple regression, Bayesian regression, Violations of basic hypotheses, Model selection, Multicollinearity, Variable transformations, Nonlinear regression, Generalized linear models.

 

Teaching Plan

Bibliography

Mandatory: 

•    Reinaldo Charnet, Clarice Azevedo de Luna Freire, Eugênia M. Reginato Charnet and Heloísa Bonvino. Analysis of Linear Regression Models with applications, 2011, 2nd Edition. Unicamp Publisher.
•    Annette J. Dobson, An Introduction to Generalized Linear Model, 1990, Chapman & Hall.
•    Morris DeGroot, Mark Schervish. Probability and Statistics. Fourth Edition, 2012
 

Complementary: 

•    Casella, G., Berger, R., Statistical Inference, 2010, Cengage Learning
•    Montgomery, D.C. & Peck, E.A., Introduction to Linear Regression, 1982, John Wiley & Sons
•    Draper, N.R. & Smith, H., Applied Regression Analysis - Third Edition, 1998, John Wiley & Sons
•    Wasserman, Larry. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.
•    Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. New York: Springer series in statistics, 2001.