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.
About this Course
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
Syllabus - What you will learn from this course
Week 1: Least Squares and Linear Regression
Week 2: Linear Regression & Multivariable Regression
Week 3: Multivariable Regression, Residuals, & Diagnostics
Week 4: Logistic Regression and Poisson Regression
- 5 stars64.16%
- 4 stars23.11%
- 3 stars7.56%
- 2 stars2.98%
- 1 star2.16%
TOP REVIEWS FROM REGRESSION MODELS
Great course to get the basics on Linear Models and Inference. Great Introduction to Logistic Regression and Poisson Regression. Good emphasis in Diagnostics of the main assumptions
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.
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!
I liked this because I have almost no background on this sort of thing and it forced me to go waaay back and revisit and deepen my knowledge of modeling and statistics as well. I loved it.
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