Statistical Analysis: Linear and Logistic Regression

Welcome to our comprehensive guide on linear and logistic regression using R and Python, where we will explore these essential statistical techniques using two popular frameworks: tidymodels and base R and Python. Whether you’re a data science enthusiast or a professional looking to sharpen your skills, this tutorial will help you gain a deep understanding of linear and logistic regression and how to implement them in R and Python. Now, it is possible to perform linear and logistic regression. The issue here is that linear regression can only be performed on a single series of ungrouped data, and performing logistic regression is cumbersome and may require the use of external solver add-ins. Also, the process can only be performed against ungrouped or non-nested data. In R and Python, we do not have such limitations.

In this chapter, we will cover the following topics in both base R and Python and using the tidymodels framework...