Logistic regression estimates the probability of an event occurring based on a given dataset of independent variables. The model includes a dependent variable (also called response variable, outcome variable, or y-variable) which contains only two values (typically labelled as "0" and "1"), and a continuous independent variable (also called explanatory variables, covariates, features, or x-variables).
To perform logistic regression:
From the analysis window, click "+ New analysis" and choose "Logistic regression" from the dropdown menu.
Select the data model in the "Parameters" card on the right side. Select the series of interest from the dropdown menu if you choose to analyze series data.
Select the dependent variable. Only variables of type category are allowed. If the dependent variable in your data set is numeric with values 1 and 0, it is possible to convert the variable to a category variable to perform the analysis.
Select which category values represent a positive outcome (have the value 1) under "Positive outcome". It is possible to select multiple category values, and the category values not selected are mapped to zero.
Select an independent variable from the "Independent variable (numeric)" dropdown menu. This can be any numeric variable in your project.
Optionally select a "Grouping" variable to compare results between groups.
Open the "Formatting" card on the right side to choose whether to use category values or labels, and whether to display chart legends on your figure (only applies if you have set a grouping variable for your analysis).
The result from the regression analysis is shown in a table underneath the figure.
You can apply filters to the dataset to analyse subgroups (optional).
Export your results (Optional)