When you do logistic regression you have to make sense of the coefficients. These are based on the log(odds) and log(odds ratio), but, to be honest, the easiest way to make sense of these are through examples. In this StatQuest, I walk you though two Logistic Regression Examples, step-by-step, and show you exactly how the coefficients are derived and how to interpret them.

NOTE: In statistics, machine learning and most programming languages, the default base for the log() function is ‘e’. In other words, when I write, “log()”, I mean “natural log()”, or “ln()”. Thus, the log to the base ‘e’ of 2.717 = 1.
ALSO NOTE: Starting at 15:21, the left hand side of the equation should be “log(odds Obesity)” instead of “size”.

0:00 Awesome song and introduction
1:13 Review of Logistic Regression Concepts
2:47 Coefficients for continuous variables
10:46 Coefficients for discrete variables
17:52 Coefficients for combinations of variable types

#logistic #machinelearning