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Probit Regression Calculator
Probit regression, also called the probit model, is used to for the modelling of dichotomous or binary outcome variables (0 or 1). The inverse standard normal distribution of the probability is modeled as a linear combination of the predictors by transforming sigmoid-response curves to a straight line that can be analyzed by non-linear regression with techniques like least squares or maximum likelihood estimator. Probit analysis is primarily used in the understanding of dose-response relationships but has applications in other fields as well. The statistical analysis generates estimations for beta coefficients, p values, standard errors, log likelihood, residual deviance, null deviance, and AIC, which can be used in making predictions about dichotomic responses.
How to use this tool
1. Enter the data into the box on the right. Data can be directly copied from Excel or pasted as values in comma-separated, tab-separated, or space-separated formats. If the data is being entered manually, only place one value per line. The format should be as follows:
Place the dependent variable into the first column. This has to be a binary value (1 or 0). The subsequent columns (Data Set 2 and onwards) should hold independent variables. Users can insert up to ten independent variables per analysis. To add a new data set, press on the ‘+’ tab above the data entry area. Variables can be named by double clicking the tab but is optional.
| Dependent Variable | Independent Variable | … |
| X1 | Y1 | … |
| X2 | Y2 | … |
| X3 | Y3 | … |
| X4 | Y4 | … |
| … | … | … |
Place the dependent variable into the first column. This has to be a binary value (1 or 0). The subsequent columns (Data Set 2 and onwards) should hold independent variables. Users can insert up to ten independent variables per analysis. To add a new data set, press on the ‘+’ tab above the data entry area. Variables can be named by double clicking the tab but is optional.
2. Verify your data is accurate in the table that appears.
3. Press the "Calculate Probit Regression" button to display results. Each dataset will generate an output in the form of a summary table comprising of beta coefficients, p values, standard errors, log likelihood, residual deviance, null deviance, and AIC.
4. In the probability equation solver, insert values for independent variables in the input boxes to obtain probability of the event occurring. Click on the box for the independent variable you want to observe. This plots the probability distribution of the dependent variable with respect to that independent variable while keeping the remaining parameters fixed.
Data Entry
+
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This online tool may be cited as follows
MLA | "Quest Graph™ Probit Regression Calculator." AAT Bioquest, Inc., 24 Apr. 2025, https://www.aatbio.com/tools/probit-model-regression-analysis-calculator. | |
APA | AAT Bioquest, Inc. (2025, April 24). Quest Graph™ Probit Regression Calculator. AAT Bioquest. https://www.aatbio.com/tools/probit-model-regression-analysis-calculator. | |
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