Uploaded April 2022 | Updated September 2026, 2 weeks ago
In this last video tutorial in my seminar series on the fundamentals of linear models, I take a look at logistic regression. Unlike linear regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA), the data type of the dependent variable is binary categorical.
Please watch the first three tutorials in this seminar series. Topics discussed in these video tutorials are used in this tutorial.
Linear regression youtu.be/6gtEfIbx6ko
Analysis of variance youtu.be/yeRtgKtbUFU
Analysis of covariance youtu.be/FhZB1oGVrYc
In logistic regression we use numerical or categorical variables to calculate an estimate of the probability of one of the classes or levels of the binary categorical dependent variable.
In this video lecture I talk about probability, odds, and odds ratios before we build three different logistic regression models. You will see just how easy it is to do in Python and how simple it is to understand logistic regression and how to interpret the results.
00:00 Introduction
19:25 Probability Odds and Odds Ratios
24:54 Continuous numerical variable as independent variable
41:06 Categorical variable as independent variable
46:51 Continuous numerical and categorical variable as independent variables
In this last video tutorial in my seminar series on the fundamentals of linear models, I take a look at logistic regression. Unlike linear regression, analysis of variance (ANOVA), and analysis of covariance (ANCOVA), the data type of the dependent variable is binary categorical.
Please watch the first three tutorials in this seminar series. Topics discussed in these video tutorials are used in this tutorial.
Linear regression youtu.be/6gtEfIbx6ko
Analysis of variance youtu.be/yeRtgKtbUFU
Analysis of covariance youtu.be/FhZB1oGVrYc
In logistic regression we use numerical or categorical variables to calculate an estimate of the probability of one of the classes or levels of the binary categorical dependent variable.
In this video lecture I talk about probability, odds, and odds ratios before we build three different logistic regression models. You will see just how easy it is to do in Python and how simple it is to understand logistic regression and how to interpret the results.
00:00 Introduction
19:25 Probability Odds and Odds Ratios
24:54 Continuous numerical variable as independent variable
41:06 Categorical variable as independent variable
46:51 Continuous numerical and categorical variable as independent variables
