Regression Modeling in Practice
About this course
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.
90/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 35/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand the fundamentals of regression analysis
- perform linear regression
- adapt regression techniques for non-linear relationships
- identify and manage confounding variables
- interpret regression coefficients
- evaluate regression model quality using diagnostic plots
Course objectives
- to equip students with practical regression modeling skills
- to enable students to tell compelling data stories through their analysis
- to foster collaboration on model development and interpretation
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