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.

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

3.5 rating, based on 4 Class Central reviews

Start your review of Applied Regression Analysis

Prose Simian

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),…

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.)

I'm about half way through week 2 of this course, but "week 1" was mainly about installing Stata (more on that below). So these impressions are preliminary (in particular the 'easy' rating since obviously the first part is something of a stats review).

Installation on Stata on Linux was not straightforward, because the original instructions were 6 versions out of date. (Stata is on v13, the instructions were for v7!) Fortunately some better instructions including a critical workaround have now been posted on the forum. I hope these get adopted officially, because the debacle cost me several hours.

Prof Lemeshow says he teaches using Stata because it only takes a few hours to pick up. I'm yet to be convinced that it's significantly easier than R (in REPL mode). But I'm keeping an open mind.

The lectures are clearly recorded in class & lightly edited. Prof Lemeshow is a patient, good humoured teacher. But even this early on a glaring omission in the code shown during the lectures has made it impossible to follow along - without refering to the forum.

These kinds of rough edges, if repeated can be very time consuming and frustrating, leading to high drop out rates. Faced with more choices, I'd look elsewhere. But let's see if I can make it through the first week...
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EDIT: dropped. Maybe for someone who's recently followed on campus classes & has a textbook on hand, this would be doable. There's no doubt applied stats is hard to teach: because of a vast gap between the complex mathematical underpinnings & thumb in the air practice. But despite good intentions it's not being taught very effectively (in online format) here, at least not for my level of rustiness.
Matteo Ferrara @matteo
Michael A. Alcorn
Colin Khein

Taught by

Tags

Regression Analysis: Simplify Complex Data Relationships

Regression Analysis

Linear Regression Modeling for Health Data

Medical Statistics

Introduction to Statistical Analysis: Hypothesis Testing

Regression Analysis

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