MOOC Applied Regression Analysis

About the course

Statistical modeling is a fundamental element of analysis for statisticians, epidemiologists, biostatisticians and other professionals of related disciplines. People in the health sciences profession rely on regression modeling to gain insight on making decisions based on a continuous flow of response data.

Focusing on linear and multiple regression, this course will provide theoretical and practical training in statistical modeling.

This is a hands-on, applied course where students will become proficient at using computer software to analyze data drawn primarily from the fields of medicine, epidemiology and public health.

There will be many practical examples and homework exercises in this class to help you learn. If you fully apply yourself in this course and complete all of the homework, you will have the opportunity to master methods of statistical modeling when the response variable is continuous and you will become a confident user of the Stata* package for computing linear, polynomial and multiple regression.

*Access to Stata will be provided at no cost for the duration of this course.

Course team

Founding Dean of the Ohio State University’s College of Public Health


The 6-week class will consist of lecture videos, quiz questions and homework exercises.

The homework exercises are ungraded and students are expected to complete them using the statistical package Stata (Available to all course students during the six week period of this course).

Solutions to all homework problems will be provided to help students with the necessary Stata commands. (This process will be carefully explained in the course)

Discussion boards will be used in this class for conversation with your peers, the faculty and the course team.

The course slides will be provided to all students. If printed, these slides would serve as the course textbook and could serve as a useful reference for the future.

Course plan

Week 1

  • Review of basic statistical concepts
  • Regression and correlation

Week 2

  • Linear regression
  • Assumptions for linear regression
  • Hypothesis test and confidence intervals for model parameters

Week 3

  • The correlation coefficient
  • The ANOVA table for straight line regression

Week 4

  • Polynomial regression

Week 5

  • Multiple regression
  • The partial F-test

Week 6

  • Dummy (or indicator) variables
  • Statistical interaction
  • Comparing two straight line regression equations


Students and professionals working in fields requiring the use of statistical modeling: epidemiologists, biostatisticians, etc.

Economics & Finance, Statistics & Data Analysis

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