Simple Regression Analysis in Public Health

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Simple Regression Analysis in Public Health

This course is part of Biostatistics in Public Health Specialization

Instructor: John McGready, PhD, MS

16,730 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

390 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

390 reviews

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Practice simple regression methods to determine relationships between an outcome and a predictor
Recognize confounding in statistical analysis
Perform estimate adjustments

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

9 assignments

Taught in English

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This course is part of the Biostatistics in Public Health Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 5 modules in this course

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, we'll focus on the use of simple regression methods to determine the relationship between an outcome of interest and a single predictor via a linear equation. 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 logistic regression, confidence intervals, p-values, Cox regression, confounding, adjustment, and effect modification.

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.

What's included

8 videos2 readings2 assignments

8 videos Total 148 minutes

Introduction 3 minutes
Simple Regression: An Overview 17 minutes
Simple Linear Regression with a Binary (or Nominal Categorical) Predictor 14 minutes
Simple Linear Regression with a Categorical Predictor 22 minutes
Simple Linear Regression with a Continuous Predictor 26 minutes
Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates 26 minutes
Measuring the Strength of a Linear Association 23 minutes
Additional Examples 17 minutes

2 readings Total 20 minutes

Solutions to Linear Regression Practice Quiz 10 minutes
Quiz 1 Solution 10 minutes

2 assignments Total 30 minutes

Simple Linear Regression 30 minutes
Practice Quiz: Simple Linear Regression 0 minutes

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.

What's included

7 videos2 readings2 assignments

7 videos Total 114 minutes

Introduction 3 minutes
Simple Logistic Regression: An Overview 10 minutes
Simple Logistic Regression with a Binary (or Categorical) Predictor 23 minutes
Simple Logistic Regression with a Continuous Predictor 25 minutes
Simple Logistic Regression: Accounting for Uncertainty in the Estimates 16 minutes
Estimating Risk and Functions of Risk from Logistic Regression Results 17 minutes
Additional Examples 21 minutes

2 readings Total 20 minutes

Solution Examples to Practice Quiz on Simple Logistic Regression 10 minutes
Quiz 2 Solutions 10 minutes

2 assignments Total 30 minutes

Logistic Regression 30 minutes
Practice Quiz: Simple Logistic Regression 0 minutes

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.

What's included

7 videos2 readings2 assignments

7 videos Total 101 minutes

Introduction 2 minutes
Simple Cox Regression: An Overview 17 minutes
Simple Cox Regression with a Binary (or Categorical) Predictor 18 minutes
Simple Cox Regression with a Continuous Predictor 24 minutes
Simple Cox Regression: Accounting for Uncertainty in the Estimates 15 minutes
Estimating Survival Curves from Cox Regression Results 12 minutes
Additional Examples 14 minutes

2 readings Total 25 minutes

Solutions for Practice Quiz on Simple Cox Regression 15 minutes
Solutions to Summative Quiz 3 10 minutes

2 assignments Total 30 minutes

Simple Cox Regression 30 minutes
Practice Quiz: Simple Cox Proportional Hazards Regression 0 minutes

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.

What's included

10 videos1 reading2 assignments

10 videos Total 117 minutes

Introduction 1 minute
Confounding: A Formal Definition and Some Examples 16 minutes
Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding 12 minutes
Adjusted Estimates: The General Idea Behind the Computations 8 minutes
Additional Examples 8 minutes
Effect modification: Introduction with an analytic example 17 minutes
Effect modification: Tree damage and elevation example 15 minutes
Effect modification: Examples from the literature 17 minutes
Confounding versus effect modification: A review 8 minutes
Additional examples 15 minutes

1 reading Total 10 minutes

Solutions to Summative Quiz 4 10 minutes

2 assignments Total 60 minutes

Confounding and Effect Modification 30 minutes
Practice Quiz: Confounding and Effect Modification 30 minutes

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.

What's included

1 reading1 assignment

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Instructor

Instructor ratings

(77 ratings)

John McGready, PhD, MS

Johns Hopkins University

6 Courses 102,406 learners

Offered by

Johns Hopkins University

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Reviewed on Oct 18, 2019

The course content was great. However, there was some technical problems.

Reviewed on Sep 5, 2020

Such complex concepts explained with ease. The section on confounding requires more than one reading. Really enjoyed it.

Reviewed on Mar 21, 2023

Very happy that all I requested for was attended to i.e explanation especially of the summative assessment and others as required.Thank you so very much for making the learning impactful

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