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
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.
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.
Good course, worth taking. It points out the importance of looking deeper into the world of regression models and creates right mindset and anchors for future development.
This course has been the most difficult in the Dara Science track so far, but you get a more in depth knowledge in data analysis and interpretation based on statistical models.
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