http://data.princeton.edu/wws509/
This course deals with statistical models for the analysis of quantitative and qualitative data, of the types usually encountered in social science research. The statistical methods studied are the general linear model for quantitative responses (including multiple regression, analysis of variance and analysis of covariance), binomial regression models for binary data (including logistic regression and probit models), models for count data (including Poisson regression and negative binomial models) and models for survival data (focusing on piecewise exponential models fitted via Poisson regression). All of these techniques are covered as special cases of the Generalized Linear Statistical Model, which provides a central unifying statistical framework for the entire course.
Author(s): German Rodriguez
Year: 2016
Language: English
Pages: 252
2. Linear Models for Continuous Data
3. Logit Models for Binary Data
4. Poisson Models for Count Data
4a*. Addendun on Overdispersed Count Data
5. Log-Linear Models for Contingency Tables
6. Multinomial Response Models
7. Survival Models
8*. Panel and Clustered Data
A. Review of Likelihood Theory
B. Generalized Linear Model Theory
