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Linear Regression Calculator (Simple and Multiple)

Q: How do I include a categorical variable?

Just tick it. Text columns are converted to indicator variables automatically, with the first category alphabetically as the reference.

Q: What does R² mean?

The share of the variation in the outcome explained by the model. Adjusted R² penalises extra predictors and is better for comparing models.

Q: What happens to missing values?

Rows with a missing value in the outcome or any chosen predictor are left out (complete-case analysis), and the number dropped is reported.

Fit a simple or multiple linear regression by pasting your data. Get coefficients with standard errors, 95% confidence intervals and p-values, R², adjusted R², the F test and a scatter plot.

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 (dependent variable)

Coefficients

Term	B	SE	95% CI	t	p
(Intercept)	67.470	8.320	50.991 to 83.950	8.11	< 0.001
group: Intervention (vs Control)	-5.648	1.619	-8.854 to -2.442	-3.49	< 0.001
age	0.507	0.095	0.320 to 0.695	5.36	< 0.001
bmi	1.084	0.221	0.646 to 1.522	4.90	< 0.001

Model fit

R²	0.382Adjusted R² = 0.366
F test	F(3, 116) = 23.85, p < 0.001
Residual standard error	8.810116 degrees of freedom
Observations used	120No missing values

Each coefficient is the expected change in sbp for a one-unit increase in that predictor (or versus the reference category), holding the other predictors constant. Check residuals for linearity, constant variance and outliers before relying on p-values.

How to Use the Linear 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.

What linear regression tells you

Linear regression models a continuous outcome as a straight-line function of one or more predictors. Each coefficient is the expected change in the outcome for a one-unit increase in that predictor, holding the others constant.

Categorical predictors are entered as indicator (dummy) variables compared with a reference category, shown in the term names. Coefficients are estimated by ordinary least squares.

Check the assumptions before relying on the p-values: a linear relationship, independent observations, roughly constant residual variance, and residuals without extreme outliers.

How it is calculated

Ordinary least squares: b = (XᵀX)⁻¹Xᵀy
SE from the residual variance; t tests on n − p degrees of freedom
R² = 1 − RSS/TSS; adjusted R² = 1 − (1 − R²)(n − 1)/(n − p)
Results match Python statsmodels OLS

Sources

NIST/SEMATECH e-Handbook: Linear least squares regression

Linear Regression Calculator: FAQ

How do I include a categorical variable?

Just tick it. Text columns are converted to indicator variables automatically, with the first category alphabetically as the reference.

What does R² mean?

The share of the variation in the outcome explained by the model. Adjusted R² penalises extra predictors and is better for comparing models.

What happens to missing values?

Rows with a missing value in the outcome or any chosen predictor are left out (complete-case analysis), and the number dropped is reported.

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