- Review the major goals of statistical learning
- Explain the difference between parametric and non-parametric methods
- Introduce linear models and ordinary least squares regression
- Demonstrate how to estimate a linear model in R using
`lm()`

- Demonstrate how to extract model statistics using
`broom`

and`modelr`

- Practice estimating and interpreting linear models
- Demonstrate the use of logistic regression for classification
- Identify methods for assessing classification model accuracy

- Read chapters 22-25 in R for Data Science
- If you want a more rigorous introduction to the fundamentals of statistical learning and linear models, read chapters 2 and 3 in
*An Introduction to Statistical Learning*. However this text assumes a much stronger knowledge of math, probability, and statistics.

- If you want a more rigorous introduction to the fundamentals of statistical learning and linear models, read chapters 2 and 3 in
- Read/skim chapter 4.1-3 in
*An Introduction to Statistical Learning*

- Install the
`titanic`

package using the command`install.packages("titanic")`

. We will be using this package in-class next time

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