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2. Statistical Learning
3. Linear Regression
4. Logistic Regression
5. Resampling Methods
6. Linear Model Selection and Regularization
7. Moving Beyond Linearity
8. Tree-Based Methods
9. Support Vector Machines
10. Unsupervised Learning
© 2019.
islr
notes and exercises from An Introduction to Statistical Learning
3. Linear Regression
Notes
Exercises
1-7. Conceptual Exercises
8. Simple Regression of
`mpg`
on
`horsepower`
in
`auto`
dataset
9. Multiple regression of
`mpg`
on numerical features in
`auto`
dataset
10. Multiple regression of
`Sales`
on
`Price`
,
`Urban`
, and
`US `
features in
`Carseats`
dataset
11. The t-statistic for null hypothesis in simple linear regression with no intercept
12. Simple regression without an intercept
13. Regression models on simulated data
14. The collinearity problem
15. Regression models for the
Boston
Dataset