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

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5 modules

Gain insight into a topic and learn the fundamentals.

391 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.

391 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 videosTotal 148 minutes

Introduction3 minutes
Simple Regression: An Overview17 minutes
Simple Linear Regression with a Binary (or Nominal Categorical) Predictor14 minutes
Simple Linear Regression with a Categorical Predictor22 minutes
Simple Linear Regression with a Continuous Predictor26 minutes
Simple Linear Regression Model: Estimating the Regression Equation—Accounting for Uncertainty in the Estimates26 minutes
Measuring the Strength of a Linear Association23 minutes
Additional Examples17 minutes

2 readingsTotal 20 minutes

Solutions to Linear Regression Practice Quiz10 minutes
Quiz 1 Solution10 minutes

2 assignmentsTotal 30 minutes

Simple Linear Regression30 minutes
Practice Quiz: Simple Linear Regression0 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 videosTotal 114 minutes

Introduction3 minutes
Simple Logistic Regression: An Overview10 minutes
Simple Logistic Regression with a Binary (or Categorical) Predictor23 minutes
Simple Logistic Regression with a Continuous Predictor25 minutes
Simple Logistic Regression: Accounting for Uncertainty in the Estimates16 minutes
Estimating Risk and Functions of Risk from Logistic Regression Results17 minutes
Additional Examples21 minutes

2 readingsTotal 20 minutes

Solution Examples to Practice Quiz on Simple Logistic Regression10 minutes
Quiz 2 Solutions10 minutes

2 assignmentsTotal 30 minutes

Logistic Regression30 minutes
Practice Quiz: Simple Logistic Regression0 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 videosTotal 101 minutes

Introduction2 minutes
Simple Cox Regression: An Overview17 minutes
Simple Cox Regression with a Binary (or Categorical) Predictor18 minutes
Simple Cox Regression with a Continuous Predictor24 minutes
Simple Cox Regression: Accounting for Uncertainty in the Estimates15 minutes
Estimating Survival Curves from Cox Regression Results12 minutes
Additional Examples14 minutes

2 readingsTotal 25 minutes

Solutions for Practice Quiz on Simple Cox Regression15 minutes
Solutions to Summative Quiz 310 minutes

2 assignmentsTotal 30 minutes

Simple Cox Regression30 minutes
Practice Quiz: Simple Cox Proportional Hazards Regression0 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 videosTotal 117 minutes

Introduction1 minute
Confounding: A Formal Definition and Some Examples16 minutes
Adjusted Estimates: Presentation, Interpretation, and Utility for Assessing Confounding12 minutes
Adjusted Estimates: The General Idea Behind the Computations8 minutes
Additional Examples8 minutes
Effect modification: Introduction with an analytic example17 minutes
Effect modification: Tree damage and elevation example15 minutes
Effect modification: Examples from the literature17 minutes
Confounding versus effect modification: A review8 minutes
Additional examples15 minutes

1 readingTotal 10 minutes

Solutions to Summative Quiz 410 minutes

2 assignmentsTotal 60 minutes

Confounding and Effect Modification30 minutes
Practice Quiz: Confounding and Effect Modification30 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 Courses104,924 learners

Offered by

Johns Hopkins University

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Reviewed on Dec 9, 2020

Complex analyses clearly explained, with an emphasis on interpretation rather than on mechanics. Excellent examples from published literature used throughout. Highly recommended!

Reviewed on May 8, 2021

Another exceptional class by Dr. John McGready. Clear and concise with many real-world research reviews that make challenging subjects such as logistic regression much more easy to understand.

Reviewed on Jan 13, 2020

Really great course. Thank you for creating this. I'm an epidemiologist but haven't practiced biostatistics in years. This course series not only refreshed but also taught me new things.

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