Linear Regression Calculator

Multiple Variables

The calculator uses an unlimited number of variables, calculates the Linear equation, R, p-value, outliers and the adjusted Fisher-Pearson coefficient of skewness.
After checking the residuals' normality, multicollinearity, homoscedasticity and priori power, the program interprets the results.
Then, it draws a histogram, a residuals QQ-plot, a correlation matrix, a residuals x-plot and a distribution chart.
You may transform the variables, exclude any predictor or run backward stepwise selection automatically based on the predictor's p-value.

Multiple Regression tutorial


H0: Y=b0
H1: Y=b0+b1X1+...+bpXp

Test statistic

F statistic

F distribution

F distribution right tailed

Test calculation

Tails: using to check if the regression formula and parameters are statistically significant.

Statistic Data

The power is expected to identify the effect. If one exists, H0 will be rejected. effect size f2 - Cohen's effect size f2, the ratio between the explained variance and the unexplained variance. f2=R2/(1-R2)
0.02 - small effect, 0.15 - medium , 0.35 - large

R2 - the expected R Squared, the ratio of the explain variance
IterationsAutomatic will remove the most insignificant variable and run another iteration until all Xi variables are significant or only one variable left
α:Significant level (0-1), maximum chance allowed rejecting H0 while H0 is correct (Type1 Error)
Digits:When choosing 2 digits, 0.00001234 will be rounded to 0.000012

Enter raw data directly
Enter raw data from excel

Enter sample data directly

Header: You may change the groups' names to their real names.
Data: When entering data, press Enter or comma ',' after each value.
* All variables will be included in the automatic iterations mode.
** Normality colors based on α=0.05

Enter sample data from excel

You may copy data from Excel, Google sheets or any tool that separate data with Tab and Line Feed.
Copy the data, one block of consecutive columns includes the header, and paste below. Y must be the right column. . click to see example: example from excel

Correlation matrix
ANOVA table

validation message