Reviewing linear regression and framing it as a prototypical example and source of intuition for other machine learning methods.

Link | Type | Description |
---|---|---|

html pdf | Slides | Least-squares regression |

Rmd | Notebook | Regression analysis and simulations |

- ISLR Chapter 3: Linear Regression.

This chapter is long but should be mostly a review of material from previous courses.

First, create a GitHub using your LSE email address (or add your LSE email to your existing account if you have another one). You’ll need this account later when we start uploading completed notebooks to a GitHub Classroom.

Second, identify a dataset that you can use to fit a multiple regression model (one outcome variable, multiple predictor variables). This way you can work on an example dataset that you’re personally interested in. If you can’t find something or have trouble loading it into R in time there are backup options in these packages:

`fivethirtyeight`

https://fivethirtyeight-r.netlify.app/`palmerpenguins`

https://allisonhorst.github.io/palmerpenguins/`modeldata`

https://modeldata.tidymodels.org/`nycflights13`

https://nycflights13.tidyverse.org/

Just be sure to identify in advance which variable you’ll use as an outcome to predict, and which variables you might use as predictors

Regression, when conditioning on more than one predictor variable.

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

For attribution, please cite this work as

Loftus (2021, Oct. 10). machine learning 4 data science: 2 Linear regression. Retrieved from http://ml4ds.com/weeks/02-linear-regression/

BibTeX citation

@misc{loftus20212, author = {Loftus, Joshua}, title = {machine learning 4 data science: 2 Linear regression}, url = {http://ml4ds.com/weeks/02-linear-regression/}, year = {2021} }