OLS Regression Part 2: Results Interpretation

Setting Up You will need to load the tidyverse package and also reload the data from Part 1 if you changed your R environment (new project). # load the tidyverse package library(tidyverse) We finished Part 1 looking at the linear relationships between two explanatory variables (bank orders and traffic controller orders), and our target variable (total orders). In the charts we created a formula for the linear regression line that resulted from our analyses in the format \(\hat{y} =161,000 + 1.

OLS Regression Part 1: Basics

I recommend this book to help you understand where ordinary least squares (OLS) regression fits within the most common statistical learning approaches: James et al., An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) 1st ed. 2013, Corr. 7th printing 2017 Edition With so many options available to students and practitioners it is rare to find a book that takes both an accessible-theoretical and practical approach to econometrics and machine learning.

One-Way ANOVA in Python

Installing and Loading the Reticulate Package This package allows us to run Python 3 inside R Markdown chunks. You will need Python 3 installed. There are details on how to do that in the Resources section of the blog. I also needed to change Reticulate’s settings to point it at Python3 on my Mac. You can find those details here. Of course, you do not need to run Python from Rstudio, or within the R framework.