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Logistic Regression- Statistics.com

0-6 Month Online
Intermediate 4-Sep-2015
Online Business Analytics
Online Self Paced 2245
DescriptionProgram StructureEligibilityToolsFacultyContact

Logistic regression is one of the most commonly-used statistical techniques. It is used with data in which there is a binary (success-failure) outcome (response) variable, or where the outcome takes the form of a binomial proportion. Like linear regression, one estimates the relationship between predictor variables and an outcome variable. In logistic regression, however, one estimates the probability that the outcome variable assumes a certain value, rather than estimating the value itself.

This online course will cover the functional form of the logistic model and how to interpret model coefficients. The concepts of “odds” and “odds ratio” are examined, as well as “risk ratio” and the difference between the two statistics. Our emphasis is on model construction, interpretation, and goodness of fit. Exercises include hands-on computer problems.

Course Program:

  • Week 1: Basic Terminology and Concepts
  • Week 2: Logistic Model Construction
  • Week 3: Analysis, Fit, and Interpretation of the Logistic Model
  • Week 4: Binomial Logistic Regression and Over dispersion

Important Date:

September 04, 2015 to October 02, 2015


4 weeks

Time Requirement:

About 15 hours per week, at times of your choosing


INR 37,740 (assuming $ = INR 60)

Part Time/Full Time:

Part Time

These are listed for your benefit so you can determine for yourself, whether you have the needed background, whether from taking the listed courses, or by other experience.

  • Statistics 1 – Probability and Study Design
  • Statistics 2 – Inference and Association


Medical researchers, epidemiologists, forensic statisticians, environmental scientists, actuaries, data miners, industrial statisticans, sports statisticians, and fisheries, to name a few, will all find this course useful. It is an essential course for anyone who needs to model data with binary or categorical outcomes, and who need to estimate probabilities of given outcomes based on predictor variables.

  • Stata
  • R
  • SAS
  • SPSS
  • Dr. Joseph Hibe
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