Simple Regression Analysis in Public Health

Johns Hopkins University via Coursera

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Overview

Simple Regression Analysis in Public Health is a foundational course designed to introduce learners to the principles and application of simple linear regression within the context of public health research. Targeted at students, health professionals, and researchers, this course focuses on analyzing the relationship between a single independent variable and a health-related outcome. The course begins by explaining the basics of regression analysis, including the interpretation of regression coefficients, the concept of the line of best fit, and assumptions underlying the...

Syllabus

Simple Regression Methods

Module one covers simple regression, the four different types of regression, commonalities between them, and simple linear aggression. Before completing the graded quiz, you can test your knowledge with the practice quiz.

Simple Logistic Regression

Within module two, we will look at logistic regression, create confidence intervals, and estimate p-values. You will have the opportunity to test your knowledge in both a practice quiz and a graded quiz.

Simple Cox Proportional Hazards Regression

Module three focuses on Cox regression with different predictors. You will have the opportunity to test your knowledge first with the practice quiz and, then, with the graded quiz.

Confounding, Adjustment, and Effect Modification

Within module four, you will look at confounding and adjustment, and unadjusted and adjusted association estimates. Additionally, you will learn about effect modification. Similar to previous modules, you will first take a practice quiz before completing the graded quiz.

Course Project

During this module, you get the chance to demonstrate what you've learned by putting yourself in the shoes of biostatistical consultant on two different studies, one about self-administration of injectable contraception and one about medical appointment scheduling in Brazil. The two research teams have asked you to them interpret previously published results in order to inform the planning of their own studies. If you've already taken other courses in this specialization, then this scenario will be familiar.