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.
This course is part of the Data Analysis and Interpretation Specialization
About this Course
Skills you will gain
- Logistic Regression
- Data Analysis
- Python Programming
- Regression Analysis
Syllabus - What you will learn from this course
Introduction to Regression
Basics of Linear Regression
- 5 stars61.90%
- 4 stars25.27%
- 3 stars6.22%
- 2 stars3.66%
- 1 star2.93%
TOP REVIEWS FROM REGRESSION MODELING IN PRACTICE
This is a great beginner level course for those have no programming experience. But I would suggest the content to be extended to 8 weeks instead of 4 weeks.
Good for understanding concepts and running code in SAS but still needs more depth to the coursework.
I really like this training. It's good if you want a good view of applied regression.
I enjoy this course so far. I like how the course entirely depends on peer grading. It will help us to get some honest feedback on our research.
About the Data Analysis and Interpretation Specialization
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