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Probit Regression Calculator
Probit regression, also called the probit model, is used 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.

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References
This online resource may be cited as follows
MLA
"Quest GraphProbit Regression Calculator." AAT Bioquest, Inc.24 Sep2025https://www.aatbio.com/tools/probit-model-regression-analysis-calculator.
APA
AAT Bioquest, Inc. (2025September 24). Quest GraphProbit Regression Calculator. AAT Bioquest. https://www.aatbio.com/tools/probit-model-regression-analysis-calculator.
BibTeXEndNoteRefMan
This online resource has been cited in 1 publications, including
One-hour extraction-free loop-mediated isothermal amplification HPV DNA assay for point-of-care testing in Maputo, Mozambique
Authors: 
Barra, Maria J and Wilkinson, Alexis F and Ma, Ariel E and Goli, Karthik and Atif, Hira and Osman, Nafissa MRB and Lorenzoni, Cesaltina and Tivir, Guilhermina and Lathrop, Eva H and Castle, Philip E and others,
Journal: 
Nature Communications (2025): 7295

  • 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:
    Dependent VariableIndependent Variable
    X1Y1
    X2Y2
    X3Y3
    X4Y4
    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.
  • Verify your data is accurate in the table that appears.
  • 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.
  • 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.