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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Approx. 54 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
What 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.
Approx. 54 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
13 hours to complete
Week 1: Least Squares and Linear Regression
This week, we focus on 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
This week, we will work through the remainder of linear regression and then turn to the first part of 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
This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.
14 hours to complete
14 videos (Total 168 min), 5 readings, 5 quizzes
16 hours to complete
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
16 hours to complete
7 videos (Total 95 min), 6 readings, 6 quizzes
Reviews
TOP REVIEWS FROM REGRESSION MODELS
by KADec 16, 2017
Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.
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 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.
by ACAug 10, 2017
Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!
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