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Linear Regression Calculator

By Jim Frost Leave a Comment

Use this linear regression calculator to analyze the relationship between two variables using simple linear, quadratic, or cubic models. It finds and graphs the best-fit line, displays the regression equation, and provides key model statistics. Enter your paired data in the spreadsheet-style table—either manually or by pasting multiple cells from a spreadsheet. Ensure that the first column contains the dependent variable (Y) and the second column is the independent variable (X). The table can handle up to 300 rows and will expand automatically when you paste in the data. Choose your regression model and click Analyze to generate the results. If you include the variable names in the header, they will appear in the results.

The linear regression calculator output includes the regression equation, a model summary, an analysis of variance (ANOVA) table, and a scatterplot with the fitted regression line. You’ll see values for R-squared, adjusted R-squared, and the F-statistic, helping you assess how well the model explains the variability in your data. This linear regression calculator is ideal for quickly visualizing trends and evaluating model fit. Below the calculator, you’ll find additional guidance and explanation. See all my Statistical Calculators!

Linear Regression Calculator
Linear Regression Calculator▼

Linear Regression Calculator Guidance

This linear regression calculator finds the best-fitting relationship between two variables with a linear, quadratic, or cubic model. It can fit polynomial regression models, such as quadratic regression. Evaluate the graph to determine whether the fitted line follows the data. Learn more about independent vs. dependent variables.

Learn more in-depth about linear regression in my Linear Regressionย article.

If the linear regression calculator’s graph indicates there is curvature in your data, try fitting a quadratic or cubic model. For quadratic and cubic models, assess the p-values in the Sequential Analysis of Variance table to determine whether adding the squared and cubed terms improve the model by a statistically significant amount. Assessing the the adjusted R-squared can also help with determine the model order.

Learn more about Fitting Curvature in Regression Models. Pay attention to the polynomials section!

Learn how to interpret the following results in the linear regression calculator:

  • Linear regression equation.
  • Regression coefficients and their p-values.
  • R-squared, Adjusted R-squared, S (standard error of the regression) in the Model Summary table.
  • Overall F-test of significance in the Analysis of Variance table.

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