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 |
This chapter is long but should be mostly a review of material from previous courses.
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
(under construction)
Regression, when conditioning on more than one predictor variable.
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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}
}