Free Tools › Logistic Regression Calculator

Logistic Regression Calculator: Adjusted Odds Ratios

Q: What is an adjusted odds ratio?

The odds ratio for one predictor while holding the other predictors in the model constant.

Q: How many predictors can I include?

A common rule of thumb is at least 10 events (of the rarer outcome) per predictor term.

Q: Can I use a Yes/No outcome?

Yes. Any column with exactly two values works; choose which value counts as the event.

Fit a binary logistic regression by pasting your data. Get adjusted odds ratios with 95% confidence intervals and p-values, the likelihood ratio test, pseudo-R² and a check of events per predictor.

FreeNo account neededYour data stays in your browser

Your data

group,age,sex,bmi,smoker,sbp,event
Control,58,Male,29.6,No,127,1
Intervention,70,Female,28.1,No,135,1
Control,69,Male,30.3,No,135,0
Intervention,53,Male,22.7,No,102,0
Control,55,Female,31.6,Yes,129,0
Intervention,53,Male,24.9,No,105,1
Control,63,Female,25.1,Yes,129,0
Intervention,57,Female,27.2,No,110,0
Control,65,Female,25.7,No,118,1
Intervention,41,Male,29.2,No,117,0
Control,72,Female,25.7,No,131,0
Intervention,57,Female,23.7,No,103,0
Control,64,Female,24.6,No,134,0
Intervention,57,Female,33.2,No,145,0
Control,55,Female,28.2,No,114,0
Intervention,62,Female,23.3,No,136,0
Control,65,Male,28.0,Yes,125,1
Intervention,56,Female,21.8,No,112,0
Control,57,Female,35.2,No,132,0
Intervention,64,Male,21.9,No,107,0
Control,50,Male,29.9,No,138,0
Intervention,44,Female,30.2,No,123,1
Control,62,Female,22.2,No,118,1
Intervention,52,Female,29.2,No,118,0
Control,41,Male,32.0,No,111,0
Intervention,51,Male,29.9,No,118,0
Control,54,Male,29.0,No,136,0
Intervention,47,Male,31.8,Yes,105,0
Control,45,Female,28.6,Yes,119,0
Intervention,58,Male,29.3,No,124,0
Control,66,Female,27.5,No,140,0
Intervention,56,Male,30.5,No,135,0
Control,51,Male,24.2,No,118,1
Intervention,61,Female,31.2,Yes,119,1
Control,64,Male,26.0,Yes,133,0
Intervention,55,Male,23.6,Yes,114,1
Control,63,Male,29.5,Yes,128,0
Intervention,67,Male,34.8,Yes,141,1
Control,56,Female,31.8,No,145,1
Intervention,51,Female,25.9,No,120,0
Control,61,Male,30.8,No,130,0
Intervention,60,Female,26.2,No,127,1
Control,68,Male,27.5,No,115,1
Intervention,46,Female,28.4,No,117,0
Control,52,Female,18.7,No,109,0
Intervention,50,Male,28.8,Yes,141,0
Control,42,Male,23.9,No,125,0
Intervention,59,Male,25.2,No,121,0
Control,63,Male,22.1,No,124,0
Intervention,51,Male,21.9,No,92,0
Control,70,Male,24.2,Yes,136,1
Intervention,65,Male,31.3,No,142,1
Control,64,Male,34.7,No,129,1
Intervention,62,Female,27.9,No,130,1
Control,67,Female,32.4,Yes,142,1
Intervention,46,Male,26.9,No,102,1
Control,64,Female,20.4,No,133,1
Intervention,63,Female,28.6,No,137,1
Control,42,Female,29.8,No,125,0
Intervention,61,Female,26.3,Yes,121,0
Control,56,Female,29.2,No,135,0
Intervention,65,Female,30.3,Yes,113,1
Control,54,Female,24.5,No,124,1
Intervention,58,Female,22.1,No,127,0
Control,61,Male,27.1,No,117,1
Intervention,50,Female,27.2,No,127,0
Control,63,Male,26.6,No,125,0
Intervention,57,Male,31.9,No,106,0
Control,62,Male,34.9,No,134,1
Intervention,53,Male,26.3,No,110,1
Control,68,Female,30.8,No,137,1
Intervention,63,Female,31.8,Yes,116,1
Control,56,Male,30.9,No,139,0
Intervention,64,Male,32.2,No,121,0
Control,69,Female,26.4,No,134,1
Intervention,74,Female,31.0,Yes,125,1
Control,44,Male,36.1,No,126,0
Intervention,66,Male,31.2,No,138,0
Control,62,Female,25.5,Yes,130,1
Intervention,57,Female,30.4,Yes,120,0
Control,49,Female,33.0,No,117,1
Intervention,69,Male,29.5,No,138,1
Control,47,Male,25.7,No,128,0
Intervention,63,Female,34.2,Yes,126,0
Control,70,Male,23.0,Yes,131,0
Intervention,44,Male,30.0,Yes,109,0
Control,55,Male,27.9,Yes,118,0
Intervention,46,Male,23.2,No,106,0
Control,60,Male,32.9,No,136,0
Intervention,72,Male,29.4,Yes,131,1
Control,76,Female,23.3,No,125,1
Intervention,42,Female,30.4,No,124,0
Control,53,Female,26.3,No,132,0
Intervention,64,Male,20.4,No,120,1
Control,72,Female,25.2,No,141,1
Intervention,62,Female,29.1,No,122,0
Control,51,Male,30.5,No,126,0
Intervention,61,Female,28.7,Yes,128,0
Control,58,Female,28.2,Yes,128,1
Intervention,56,Male,29.9,Yes,130,0
Control,51,Male,27.2,No,113,1
Intervention,61,Female,32.7,No,140,0
Control,61,Female,28.6,Yes,123,0
Intervention,57,Female,29.2,No,121,0
Control,56,Male,30.2,Yes,117,0
Intervention,46,Male,23.8,No,119,0
Control,54,Male,25.2,No,136,0
Intervention,69,Male,34.1,No,141,1
Control,56,Female,29.3,Yes,136,1
Intervention,45,Male,26.9,Yes,102,1
Control,70,Male,29.4,No,127,1
Intervention,63,Male,26.1,No,118,0
Control,77,Male,29.1,No,128,1
Intervention,59,Male,25.3,No,130,0
Control,54,Female,29.1,Yes,129,0
Intervention,45,Female,21.7,No,112,0
Control,70,Female,33.3,Yes,141,1
Intervention,81,Male,25.9,No,121,1
Control,51,Male,21.7,No,112,0
Intervention,52,Female,27.1,No,111,0

Paste straight from Excel or Google Sheets. First row = column names. Blank, NA or . = missing.

Outcome (binary)Event (coded 1)

Coefficients and odds ratios

Term	B	SE	OR	95% CI for OR	z	p
(Intercept)	-7.248	1.742	–	–	-4.16	< 0.001
group: Intervention (vs Control)	-0.178	0.414	0.84	0.37 to 1.88	-0.43	0.666
age	0.116	0.029	1.12	1.06 to 1.19	4.06	< 0.001
smoker: Yes (vs No)	0.165	0.454	1.18	0.48 to 2.87	0.36	0.717

Model fit

Likelihood ratio test	χ²(3) = 22.63, p < 0.001Model versus intercept only
McFadden's pseudo-R²	0.141
Log-likelihood	-69.02AIC = 146.0
Observations used	120 (47 events: event = 1)No missing values
Events per predictor term	15.7Based on the rarer outcome category

An odds ratio above 1 means higher odds of event = 1 for each one-unit increase in the predictor (or versus the reference category), adjusted for the other predictors.

How to Use the Logistic Regression Calculator

1
Paste your data
Copy the columns from Excel or Google Sheets, with names in the first row.
2
Choose the variables
The results update instantly in your browser.
3
Copy the table
Paste straight into Word, or download a CSV.

How to read a logistic regression

Logistic regression models the log odds of a binary outcome, such as disease or no disease, as a linear function of the predictors. Exponentiating a coefficient gives an odds ratio adjusted for the other predictors.

Too few outcome events per predictor makes estimates unstable. In a well-known simulation study, fewer than 10 events per variable led to biased coefficients and unreliable confidence intervals, so the calculator warns you below that level.

If a predictor perfectly separates the outcome groups, maximum likelihood estimates do not exist; the calculator flags models that do not converge cleanly.

How it is calculated

Maximum likelihood by Newton-Raphson (iteratively reweighted least squares)
Wald z tests and 95% CIs; OR = exp(B)
Likelihood ratio test against the intercept-only model; McFadden pseudo-R² = 1 − LL/LL₀
Results match Python statsmodels Logit

Sources

Logistic Regression Calculator: FAQ

What is an adjusted odds ratio?

The odds ratio for one predictor while holding the other predictors in the model constant.

How many predictors can I include?

A common rule of thumb is at least 10 events (of the rarer outcome) per predictor term.

Can I use a Yes/No outcome?

Yes. Any column with exactly two values works; choose which value counts as the event.

Related Free Tools

Get a Free Quote Chat on WhatsApp

Reviews & Evidence

Research & Analysis

Capstone Projects