Logistic Regression

Logistic regression is a statistical technique used to model the relationship between a binary dependent variable (i.e., a variable that can take on one of two values) and one or more independent variables. It is a type of generalized linear model that is widely used in many fields, including biology, economics, psychology, and epidemiology.

The logistic regression model is based on the logistic function, which is a type of S-shaped curve that can be used to model the probability of an event occurring. The logistic function is defined as:

p = e^(b0 + b1x1 + b2x2 + … + bnxn) / (1 + e^(b0 + b1x1 + b2x2 + … + bnxn))

where p is the probability of the event occurring, x1, x2, …, xn are the independent variables, b0 is the intercept, and b1, b2, …, bn are the regression coefficients.

The logistic regression model estimates the values of the regression coefficients that maximize the likelihood of observing the data, given the model. These estimates can be used to make predictions about the probability of the event occurring for different values of the independent variables.

To perform logistic regression analysis in SPSS, you can use the Binary Logistic Regression procedure. This procedure allows you to select the dependent and independent variables, specify the type of logistic regression model you want to use (e.g., binary, multinomial), and examine the significance and strength of the relationships between the variables. The output of the Binary Logistic Regression procedure includes regression coefficients, odds ratios, and other statistics.

Logistic regression can be useful in a variety of applications, such as predicting the likelihood of disease or mortality, modeling consumer behavior, and predicting election outcomes. It is a powerful statistical tool that allows researchers to model the complex relationship between a binary dependent variable and one or more independent variables.