Date

Nov 12, 2019

12:30 PM

Event

Location

Room 104, Stuart Hall, Chicago, IL

- 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
- This is not a math/stats class. In class we will
**briefly**summarize how these methods work and spend the bulk of our time on estimating and interpreting these models. That said, you should have some understanding of the mathematical underpinnings of statistical learning methods prior to implementing them yourselves. See below for some recommended readings:

- Chapters 7-8 of
*OpenIntro Statistics*- an open-source statistics textbook written at the level of an introductory undergraduate course on statistics

- Chapters 2-3, 4.1-3 in
*An Introduction to Statistical Learning*- a book on statistical learning written at the level of an advanced undergraduate/master’s level course