Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.
Offered By
Johns Hopkins UniversityAbout this Course
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Coursera Labs
Includes hands on learning projects.
Learn more about Coursera Labs Approx. 53 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat you will learn
Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing
Skills you will gain
- Model Selection
- Generalized Linear Model
- Linear Regression
- Regression 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 Approx. 53 hours to complete
English
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
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Syllabus - What you will learn from this course
13 hours to complete
Week 1: Least Squares and Linear Regression
13 hours to complete
9 videos (Total 74 min), 11 readings, 4 quizzes
11 hours to complete
Week 2: Linear Regression & Multivariable Regression
11 hours to complete
10 videos (Total 70 min), 5 readings, 4 quizzes
14 hours to complete
Week 3: Multivariable Regression, Residuals, & Diagnostics
14 hours to complete
14 videos (Total 168 min), 5 readings, 5 quizzes
16 hours to complete
Week 4: Logistic Regression and Poisson Regression
16 hours to complete
7 videos (Total 95 min), 6 readings, 6 quizzes
Reviews
- 5 stars64.16%
- 4 stars23.11%
- 3 stars7.56%
- 2 stars2.98%
- 1 star2.16%
TOP REVIEWS FROM REGRESSION MODELS
by SCMar 14, 2017
Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.
by DCMay 3, 2019
Very good course. Though basic, it provides you with the first tools and knowledge. The forums aren't what they used to be it seems, but you can find almost any answer there from past courses.
by BKFeb 9, 2016
This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.
by AWFeb 19, 2018
Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.
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