Select the regressors (or explanatory variables).
Choose only numeric variables that you would like to treat as continuous. In the next stage you will choose the numeric variables
to encode as dummies.

These are the variables used as continuous regressors by the multiple regression calculator.

Select the variables to encode as dummies

Choose the variables that you want to include in your regression after encoding them as groups of dummies
(i.e., explanatory variables that can take only two values, either 1 or 0).

When you select a variable that takes n different values (indicated between parentheses below), n dummies are created and included in your regression.

Unable to run: too many regressors. The number of regressors is greater than or equal to the sample size. Please reduce the number of regressors and try again.

Results

The calculator is running your regression. Please wait ...

Here are the results from your regression.

Multicollinearity

Sample size and degrees of freedom

Here is the calculation of the degrees of freedom (sample size - number of parameters).

Numerosity, number of parameters and degrees of freedom.

Coefficient estimates and their significance

In the next table, you can find the OLS coefficient estimates, their standard errors and other statistics.

A p-value is derived from the t-statistic and can be used to decide whether to:

reject the null hypothesis (p-value < level of significance)

or not to reject it (p-value >= level of significance).

The p-value is two-sided if the null is tested against the alternative hypothesis that the coefficient can be either significantly negative or significantly positive. The p-value is one-sided if only one of the two alternatives is deemed possible.

Rows correspond to regressors.

Goodness of fit

And here are some statistics about the fit of the regression model (details).

Global statistics

Scatter plot of actual vs fitted values

In a well-specified regression model, the points in an actual-vs-fitted scatter plot should be evenly distributed around the 45-degrees line.

In a model that fits the data very well, the points should be very close to the 45-degrees line.

Scatter plot here

Playground display

Data privacy and security

Your data remains on your computer and is analyzed by your browser.

The multiple regression calculator does not send data over the Internet and does not use remote servers to perform computations. Therefore, your data remains completely private and secure.

Scientific standards

SimpleR is intended to meet the highest scientific standards. It is often tested on new data sets to make sure that the results from the regression calculator coincide with those provided by well-tested scientific and statistical software such as R and Python (with NumPy and Scikit-learn).

Disclaimer

The copyright owners of SimpleR and the owners of this website do not warrant or guarantee the accuracy of the SimpleR multiple regression calculator and shall have no liability whatsoever (including but not limited to) for any direct, indirect, special or consequential damages and economic losses arising in connection with the use of SimpleR.

How to cite

Please cite as:

Taboga, Marco (2022). "SimpleR: multiple linear regression calculator", StatLect. https://www.statlect.com/fundamentals-of-statistics/SimpleR.

The books

Most of the learning materials found on this website are now available in a traditional textbook format.