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 LROct 6, 2016
Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.
by DAMar 10, 2019
This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!
by JVOct 15, 2017
It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.
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.
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