This course is part of the Data Literacy Specialization

4.7
stars
16 ratings

Jennifer Bachner, PhD

2,043 already enrolled

Offered By

Data Literacy SpecializationJohns Hopkins University

About this Course

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This course will introduce you to the linear regression model, which is a powerful tool that researchers can use to measure the relationship between multiple variables.  We’ll begin by exploring the components of a bivariate regression model, which estimates the relationship between an independent and dependent variable.  Building on this foundation, we’ll then discuss how to create and interpret a multivariate model, binary dependent variable model and interactive model.  We’ll also consider how different types of variables, such as categorical and dummy variables, can be appropriately incorporated into a model.  Overall, we’ll discuss some of the many different ways a regression model can be used for both descriptive and causal inference, as well as the limitations of this analytical tool.  By the end of the course, you should be able to interpret and critically evaluate a multivariate regression analysis.

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Course 3 of 5 in the

Data Literacy Specialization

Intermediate Level

Approx. 11 hours to complete

English

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Skills you will gain

General Linear Model
Linear Regression
Statistical Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 5 in the

Data Literacy Specialization

Intermediate Level

Approx. 11 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Jennifer Bachner, PhD

Director

Data Analytics and Policy Program

8,295 Learners

5 Courses

Offered by

Johns Hopkins University

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Syllabus - What you will learn from this course

Week1

Week 1

3 hours to complete

Regression Models: What They Are and Why We Need Them

While graphs are useful for visualizing relationships, they don't provide precise measures of the relationships between variables. Suppose you want to determine how an outcome of interest is expected to change if we change a related variable. We need more than just a scatter plot to answer this question. What should you do, for example, if you want to calculate whether air quality changes when vehicle emissions decline? Or if you want to calculate how consumer purchasing behavior changes if a new tax policy is implemented? To calculate these predicted effects, we can use a regression model. This module will first introduce correlation as an initial means of measuring the relationship between two variables. The module will then discuss prediction error as a framework for evaluating the accuracy of estimates. Finally, the module will introduce the linear regression model, which is a powerful tool we can use to develop precise measures of how variables are related to each other.

3 hours to complete

5 videos (Total 28 min), 4 readings, 4 quizzes

5 videos

Welcome Video40sCorrelation6mPrediction Error6mIntroducing the Linear Regression Model7mInterpreting Regression Models7m

4 readings

Spurious Correlations15mCorrelation in Statistics15mWhat is a confusion matrix?15mLinear Regression and Correlation (Intro & Sections 12.1-12.3)30m

4 practice exercises

Correlation Practice Problems5mPrediction Error Practice Problems5mLinear Regression Practice Problems15mFinal Quiz on Regression Models: What They Are and Why We Need Them45m

Week2

Week 2

3 hours to complete

Fitting and Evaluating a Bivariate Regression Model

Now that you've got a handle on the basics of regression analysis, the next step is to consider how to evaluate and modify a basic regression model. This module will introduce you to a common measure of model fit and the three core assumptions of regression analysis. In addition, we'll explore the special circumstance of conducting a regression analysis with a binary (AKA dummy) treatment variable. Dummy variables, which take on two values, are frequently used in statistics. Understanding how to use and interpret dummy variables provides a foundation for developing a multivariate regression model, which we'll get to in the next module.

3 hours to complete

3 videos (Total 21 min), 4 readings, 4 quizzes

3 videos

Model Fit6mLinear Regression Assumptions6mRegression with a Binary Treatment Variable8m

4 readings

Measures of Fit10mThe Regression Equation20mThe Least Squares Assumptions20mDummy Variables15m

4 practice exercises

Model Fit Practice Problems5mLinear Regression Assumptions Practice Problems5mRegression with a Binary Treatment Variable Practice Problems15mFinal Quiz on Fitting and Evaluating a Bivariate Regression45m

Week3

Week 3

3 hours to complete

Multivariate Regression Models

The bivariate regression model is an essential building block of statistics, but it is usually insufficient in practice as a useful model for descriptive, causal or predictive inference. This is because there are usually multiple variables that impact a particular dynamic. Whether you are modeling political behavior, environmental processes or drug treatment outcomes, it is almost always necessary to account for multiple influences on an outcome of interest. This module will introduce the multivariate model of regression analysis and explain the appropriate ways to interpret and evaluate the results from a multivariate analysis.

3 hours to complete

4 videos (Total 26 min), 4 readings, 4 quizzes

4 videos

Constructing and Interpreting a Multivariate Model8mDummy Variable Sets7mLinear vs. Nonlinear Categorical Variables7mMultivariate Model Fit3m

4 readings

Introduction to Multivariate Regression Analysis30mInterpreting Regression Coefficients15mUnderstanding Dummy Variable Traps in Regression15mAdjusted R-Squared: What is it used for?10m

4 practice exercises

Multivariate Model Interpretation Practice Problems8mCategorical Variable and Dummy Sets Practice Problems20mMultivariate Model Fit Practice Problem10mFinal Assessment on Multivariate Regression45m

Week4

Week 4

3 hours to complete

Extensions of the Multivariate Model

Once you've mastered the OLS multivariate model, you're ready to learn about a wide array of regression modeling techniques. Remember, researchers should always employ modeling tools that best enable them to answer the question at hand. This module will focus on two tools in particular, interaction terms and models for binary dependent variables. Keep in mind, however, that there are numerous regression modeling tools that you can learn and implement based on the research question you're trying to answer. After you've developed a solid understanding of regression basics, you should feel capable of expanding this knowledge base as you move forward as a producer and consumer of analytics.

3 hours to complete

5 videos (Total 35 min), 2 readings, 3 quizzes

Reviews

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TOP REVIEWS FROM QUANTIFYING RELATIONSHIPS WITH REGRESSION MODELS

by MTJul 8, 2021

Great refresher on regression models. Simple and concise.

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About the Data Literacy Specialization

Frequently Asked Questions

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Quantifying Relationships with Regression Models

About this Course

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