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Applied Regression Analysis

Ohio State University via Canvas Network

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Overview

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 make 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.

Centre Virchow-Villermé logo

This course was developed in partnership with the Centre Virchow-Villermé for Public Health Paris-Berlin, a bi-national centre of the Université Sorbonne Paris Cité and Charité – Universitätsmedizin Berlin. Special support was contributed from the Université Paris Descartes that also belongs to the community of Université Sorbonne Paris Cité.



All lectures and instructional materials developed for this course by the Ohio State University are licensed under the Creative Commons AttributionNonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/

Syllabus

Week One
  • Review of basic statistical concepts
  • Regression and correlation
Week Two
  • Linear regression
  • Assumptions for linear regression
  • Hypothesis test and confidence intervals for model parameters
Week Three
  • The correlation coefficient
  • The ANOVA table for straight line regression
Week Four
  • Polynomial regression
Week Five
  • Multiple regression
  • The partial F-test
Week Six
  • Dummy (or indicator) variables
  • Statistical interaction
  • Comparing two straight line regression equations

Taught by

Stanley Lemeshow

Reviews for Canvas Network's Applied Regression Analysis
3.5 Based on 4 reviews

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  • 1
Prose S
2.0 4 years ago
by Prose dropped this course, spending 9 hours a week on it and found the course difficulty to be medium.
Applied regression courses are thin on the ground. And this course promises a follow up focusing on logistic regression. So despite misgivings about using Stata, the proprietary stats package the course uses (and provides a short term license for), I decided to give it a try.

Based on Prof Lemeshow's forum comments, the course is pitched at post-graduate level. I've seem regression before at this level, but my knowledge is extremely rusty (with a slight derust treatment applied in the form of Duke's excellent "Data Analysis and Statistical Inference" MOOC a few months back.)
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Michael A
4.0 3 years ago
by Michael completed this course.
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Colin K
4.0 2 years ago
by Colin completed this course.
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Matteo F
4.0 3 years ago
by Matteo completed this course.
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