Quantifying Relationships with Regression Models
This course is part of Data Literacy Specialization.
Learn to quantify relationships between variables using regression analysis in this Johns Hopkins University course. You’ll explore bivariate, multivariate, and binary models, understand correlation and causation, and apply statistical techniques for real-world data interpretation. Perfect for intermediate learners aiming to strengthen their statistical and analytical skills.
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What you'll learn
Construct and interpret multivariate regression models
Evaluate model fit and regression assumptions
Work with categorical and dummy variables
Implement interaction terms effectively
Analyze binary dependent variable models
Skills you'll gain
This course includes:
1.8 Hours PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course introduces students to linear regression modeling, starting with basic bivariate analysis and progressing to complex multivariate models. The curriculum covers correlation analysis, prediction error, model fitting and evaluation, and advanced topics like interaction terms and binary dependent variables. Students learn to interpret and critically evaluate regression analyses while understanding both the power and limitations of these statistical tools.
Regression Models: What They Are and Why We Need Them
Module 1 · 2 Hours to complete
Fitting and Evaluating a Bivariate Regression Model
Module 2 · 2 Hours to complete
Multivariate Regression Models
Module 3 · 2 Hours to complete
Extensions of the Multivariate Model
Module 4 · 3 Hours to complete
Fee Structure
Individual course purchase is not available - to enroll in this course with a certificate, you need to purchase the complete Professional Certificate Course. For enrollment and detailed fee structure, visit the following: Data Literacy Specialization
Instructor
Leading Expert in Data Analytics and Policy at Johns Hopkins University
Dr. Jennifer Bachner is the Director of the Master of Science in Data Analytics and Policy program and the Certificate in Government Analytics program at Johns Hopkins University. With a robust academic background, she earned her Ph.D. in Government from Harvard University and holds undergraduate degrees in political science and social studies education from the University of Maryland, College Park. Dr. Bachner is a prolific author, having co-written significant works such as America’s State Governments: A Critical Look at Disconnected Democracies and What Washington Gets Wrong, alongside Benjamin Ginsberg. Her research focuses on the intersection of analytics, political behavior, and online education, contributing to reports like Predictive Policing: Preventing Crime with Data and Analytics, published by the IBM Center for the Business of Government. As an expert in her field, she has been featured in major media outlets including the Washington Post and NPR. Dr. Bachner's commitment to advancing data-driven policy solutions and her leadership in educational programs underscore her vital role at Johns Hopkins University, where she continues to shape the future of governance and analytics education.
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