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 http://www.statistics.com/modelingr/.
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 statistics.com 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 statistics.com accepts registration for its courses
at special prices in Indian Rupees through its partner, the Center for
eLearning and Training (C-eLT), Pune.
Call: 020 66009116
Websites:
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