Logistic regression is a statistical method used for binary classification, a type of supervised learning. In binary classification, the goal is to predict the outcome of a categorical dependent variable that has two possible outcomes, usually coded as 0 and 1. For example, it can be used to predict whether an email is spam (1) or not spam (0), whether a student will pass (1) or fail (0) an exam, etc.

Despite its name, logistic regression is used for classification, not regression. The logistic regression model uses the logistic function (also called the sigmoid function) to model the probability that a given input belongs to a particular class. The logistic function “squashes” the output of a linear equation between 0 and 1, transforming it into a probability.

The logistic regression model is trained using a process called maximum likelihood estimation, where the goal is to maximize the likelihood of the observed data given the model parameters. The coefficients are adjusted during training to find the values that best fit the data.

Logistic regression is a widely used and interpretable method in machine learning and statistics. It’s relatively simple, computationally efficient, and performs well on many classification tasks.

To see a demonstration of this analysis technique using SPSS, click here: https://www.youtube.com/watch?v=zj15KUXtC7M&list=PLtx0cY9iho28Iw0o97hVjao2NB-LLd9wT&index=6