Binary Logistic Regression

Overview | How to | Example

 

 

Overview

Logistic regression is used to study the relationship between a response variable and its predictor(s), where the response variable is categorical. Binary Logistic Regression is used to perform logistic regression on a response variable that is binary. Examples of a binary variable is such as Yes/No, Fail/Pass, etc. This tool can also generate diagnostic plots which can be used to identify data that are not well fitted.

Response variables and terms must be presented in columns. Covariates must be numeric and factors must have at least 2 distinct levels. The Model can include factors, covariates, interactions and nested term. Multiple terms must be separated by commas. Use * to represent interactions and () to represent nesting. The model must be hierachical, if an interaction term is included, all lower order interactions and main terms must also be included.

ProcessMA uses maximum likelihood estimates to model parameters.

 

 

How to

At the Excel Menu (For Excel 2007, go to Add-ins first)

  1. Choose ProcessMA > Statistics > Regression > Binary Logistic Regression

  2. In Response, select the column containing the response data (Exactly 2 distinct values)

  3. In Term 1 to Term 10 , select the column containing data for the respective model terms and choose if the term is a covariate or a factor (Covariate: Numeric)

  4. In Model, enter the terms to be included in the model.

  5. In Event, choose the reference event of the response

  6. In Link Function, choose the function to fit the response model

  7. Check Plot Diagnostic, to display diagnostic plots of Delta chi-square, Delta deviance, Delta beta (Standardised) and Delta beta

  8. Click OK

 

 

Example

You want to understand the effect of number of hours of sleep and smoking on blood pressure. You classified the blood pressure into Good and No Good.

  1. Open data worksheet by choosing ProcessMA > Tools > Data

  2. Choose ProcessMA > Statistics > Regression > Binary Logistic Regression

  3. In Response, select AH - Pressure

  4. In Term 1, select AI - Sleep Hours and choose Covariate

  5. In Term 2, select AJ - Smoker and choose Factor

  6. In Model, enter Sleep Hours, Smoker

  7. In Event, choose Good

  8. Click OK

 

Results & Interpretation

From the logistic regression table, you can obtain the coefficients for the regression model. In additions, the p values for Sleep Hours and Smoker are less than 0.05 which indicates that there is sufficient evidence that their coefficients are not zero.

 

 

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