Logistic regression using R programming - using a single categorical variable in your model.

Here's some text for a UA-cam video description about using R for logistic regression, focusing on using a single categorical variable as your explainer variable and interpreting the results:
In this R tutorial, we dive deep into logistic regression, a fundamental machine learning algorithm in data science. We'll guide you through the process of incorporating a single categorical predictor variable into your logistic regression model.
Learn how to effectively handle categorical data in R, including techniques like one-hot encoding or creating dummy variables. We'll demonstrate how to build and fit the logistic regression model using R's powerful statistical functions.
Crucially, we'll focus on interpreting the model coefficients and odds ratios. Understand the impact of each category level on the log-odds of the outcome and how to translate these results into meaningful insights.
This video is perfect for beginners in statistics and R programming, as well as those looking to enhance their data science skills. We'll provide clear explanations, practical examples, and R code snippets that you can easily replicate and adapt to your own projects.
Learn more about R programming at Learn More 365: www.learnmore3...
Find me on LinkedIn: / drgregmartin

[@RProgramming101]

Get my FREE cheat sheets for R programming and statistics (including transcripts of these lessons) here: www.learnmore365.com/pages/membership-r-programming-data-visualization-and-research-methods

[@louiseweschler1746]

I cannot find the cheat sheet for this video.

[@leonardoschutz5]

Thank you! I watched this serie about regression logistic and I am ansious for the next steps.

[@RProgramming101]

Wonderful! Next video coming soon. Thanks for the lovely comment.

[@mjacfardk]

Thank you, great tutorial

[@RProgramming101]

You are welcome!

[@vanikmalhotra6586]

Ever thought of having same series in Python as well ?