Multiple Regression Analysis in Public Health

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Overview

Biostatistics is the application of statistical reasoning to the life sciences, and it's the key to unlocking the data gathered by researchers and the evidence presented in the scientific public health literature. In this course, you'll extend simple regression to the prediction of a single outcome of interest on the basis of multiple variables. Along the way, you'll be introduced to a variety of methods, and you'll practice interpreting data and performing calculations on real data from published studies. Topics include multiple logistic regression, the Spline approach, confidence intervals, p-values, multiple Cox regression, adjustment, and effect modification.

Syllabus

An Overview of Multiple Regression for Estimation, Adjustment, and Basic Prediction, and Multiple Linear Regression
-Within this module, an overview of multiple regression will be provided. Additionally, examples and applications will be examined. A practice quiz is provided to test your knowledge before completing the graded quiz.

Multiple Logistic Regression
-Module two covers examples of multiple logistic regression, basics of model estimates, and a discussion of effect modification. In addition to lectures, you will also be completing a practice quiz and graded quiz.

Multiple Cox Regression
-The last module for this class focuses on multiple Cox regression, the “Linearity” assumption, examples, and applications. You will complete a practice quiz, graded quiz, and project.

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 help 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.

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