Regression Modeling in Practice
Become proficient in linear, multiple, and logistic regression analysis using SAS or Python to derive valuable insights from data.
Become proficient in linear, multiple, and logistic regression analysis using SAS or Python to derive valuable insights from data.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Data Analysis and Interpretation Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
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What you'll learn
Apply linear regression for quantitative response variables
Implement multiple regression with multiple predictors
Perform logistic regression for binary outcomes
Evaluate model fit using diagnostic techniques
Handle confounding variables and polynomial regression
Skills you'll gain
This course includes:
3.05 Hours PreRecorded video
1 peer review per module
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course focuses on regression analysis as a powerful tool for data analysis. Students learn various regression techniques, from basic linear regression to multiple regression and logistic regression. The curriculum covers model assumptions, interpretation of coefficients, regression diagnostics, and handling of confounding variables. Through hands-on practice with either SAS or Python, learners develop practical skills in building and evaluating regression models for different types of data and research questions.
Introduction to Regression
Module 1 · 2 Hours to complete
Basics of Linear Regression
Module 2 · 3 Hours to complete
Multiple Regression
Module 3 · 2 Hours to complete
Logistic Regression
Module 4 · 2 Hours to complete
Fee Structure
Instructors
Statistics Education Innovator Advancing Data-Driven Research Method
Jennifer Rose serves as Director of the Center for Pedagogical Innovation and Professor of the Practice at Wesleyan University's Quantitative Analysis Center. Along with colleague Lisa Dierker, she co-developed the "Passion-Driven Statistics" curriculum, which has transformed statistics education through project-based learning approaches. Her innovative teaching methods have earned multiple National Science Foundation grants to disseminate their educational model nationwide. As Director of the Institutional Review Board and Professor of the Practice in the Center for Pedagogical Innovation, she has significantly influenced how statistics is taught to undergraduate students. Her course "Intro to Statistical Consulting" provides students with real-world experience by connecting them with nonprofits for data analysis projects. Her work has extended the reach of this innovative teaching approach to thousands of students globally through online platforms and institutional partnerships.
Pioneering Statistician Revolutionizing Undergraduate Data Science Education
Lisa Dierker, Professor of Psychology at Wesleyan University, has transformed statistical education through her innovative "Passion Driven Statistics" curriculum. Her expertise spans chronic disease epidemiology and advanced statistical methods, leading to significant National Science Foundation funding for developing accessible, project-based teaching approaches. Her collaborative work across disciplines including public health, medicine, engineering, and neuroscience has produced groundbreaking research while making statistics more engaging for diverse student populations. Through her leadership in curriculum development and cross-disciplinary research, she has significantly influenced how quantitative methods are taught at the undergraduate level, while maintaining active research in epidemiology and public health.
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4.4 course rating
273 ratings
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