Thursday, 21 February 2013

Modeling in R

In "Modeling in R" you learn how to use R to build statistical models and use them to analyze data. Multiple regression is covered first, then logistic regression and the generalized linear model (multiple regression and logistic regression illustrated as special cases). The Poisson model for count data, and the concept of overdispersion are also covered. You learn how to analyze longitudinal data using straightforward graphics and simple inferential approaches, then mixed-effects models and the generalized estimating approach for such data.

The course emphasizes how to fit the models listed and interpret results, rather than how to derive the theoretical background of the models. Join Dr. Sudha Purohit in her online course "Modeling in R". For more details please visit at

Who Should Take This Course:
Anyone who is familiar with R and wants to learn how to use it to build and use statistical models.

Course Program:

Course outline: The course is structured as follows
SESSION 1: Linear Regression, Logistic Regression
  • Multiple linear regression with R
  • Simple examples, dummy explanatory variables, interpreting regression coefficients; finding a parsimonious model

SESSION 2: The Generalized Model With R
  • Logistic regression with R
  • The need for a different model when the response variable is binary, the logistic transform and fitting the model to some simple examples, deviance residuals
  • Multiple regression and logistic regression as special cases of the generalized linear model
  • The Poisson model for count data.
  • The problem of overdispersion

SESSION 3: Analysing Longitudinal Data Using R
  • Examples of longitudinal data
  • Simple graphics for longitudinal data and simple inference using the summary measure approach
  • The 'long form' of longitudinal data
  • Models for longitudinal data when independence of the repeated measurements is assumed
  • Mixed-effects models for longitudinal data

SESSION 4:  Generalized Estimating Equations
  • Modeling the correlational structure of the repeated measurements
  • The generalized estimating equation approach for non-normal response variables in longitudinal data
  • The dropout problem

Dr. Sudha Purohit, Instructor, is a Visiting Lecturer in Statistics at the University of Pune and, before her retirement in 2000, was Head of the Department of Statistics at A. G. College, Pune, India. She is a co-author of "Statistics Using R" (jointly with Prof. Shailaja Deshmukh and Dr. Sharad Gore), as well as "Life-Time Data: Statistical Models and Methods", "Introduction to Biometry", and (with Dr. Shailaja Deshmukh) "Microarray Data: Statistical Analysis Using R". 

Participants can ask questions and exchange comments with Dr. Purohit via a private discussion board throughout the period. The course takes place online at in a series of 4 weekly lessons and assignments, and requires about 15 hours/week. Participate at your own convenience; there are no set times when you are required to be online.

For Indian participants accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune.

For India Registration and pricing, please visit us at

Call: 020 66009116


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