These parameters are sometimes referred to as the "intercept" and "slope", respectively, based on their relationship to the "log odds". The first thing that you'll see on the results sheet are the best fit value estimates along with standard errors and 95% confidence intervals for both β0 and β1. Results of simple logistic regression Parameter estimates Once you click "OK", you'll be taken to the main results sheet which will be discussed in the next section.
The results for some of these options are discussed below, but additional information can for these options can be found here. For the purposes of this walkthrough, we won't need to change any of the default options.
Alternatively, you can click on the "Analyze" button in the toolbar, then select "Simple logistic regression" from the list of available XY analyses.Īfter clicking the simple logistic regression button, the parameters dialog for this analysis will appear. To perform simple logistic regression on this dataset, click on the simple logistic regression button in the toolbar (shown below). This data represents a collection of 125 students and the amount of time that they spent preparing for a test along with the outcome of the test: did the student pass (entered as a 1 in the data table) or did the student fail (entered as a 0 in the data table)? Initiating the analysis You will then be shown a set of data with two columns: "Hours Studied" in the X column and "Test Passed?" in the Y column. To use this data, click on "Simple logistic regression" in the list, and then click "Create". To begin, we'll want to create a new XY data table from the Welcome dialogįor the purposes of this walkthrough, we will be using the Simple logistic regression sample data found in the "Correlation & regression" section of the sample files. Logistic regression was added with Prism 8.3.0 The data This guide will walk you through the process of performing simple logistic regression with Prism.
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