Regression Analysis Calculators
Fit a line or curve to data and measure how well it explains the outcome.
Regression fits an equation to data so that one variable can be described, or predicted, from another. The simplest form is a straight line, and the same idea extends to curves such as quadratic, cubic, and exponential models.
Fitting a model is only the first step. The residuals and the coefficient of determination tell you how well the model describes the data, and predictions outside the range of the observed data should be treated with caution.
Calculators in this category
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- RegressionCoefficient of Determination Calculator (R-squared)Coming soon
- RegressionCubic Regression CalculatorComing soon
- RegressionExponential Regression CalculatorComing soon
- RegressionLinear Regression CalculatorComing soon
- RegressionPolynomial Regression CalculatorComing soon
- RegressionQuadratic Regression CalculatorComing soon
- RegressionRaw Score CalculatorComing soon
- RegressionResidual CalculatorComing soon
Key concepts
- Least squares
- The standard fitting method. It chooses the line that minimises the sum of the squared vertical distances between the data and the line.
- Residual
- The difference between an observed value and the value the model predicts.
- Coefficient of determination
- R², the share of the variation in the outcome that the model accounts for.
- Extrapolation
- Predicting beyond the range of the data used to fit the model. It is less reliable than predicting inside that range.
Frequently asked questions
What does R-squared tell me?
R-squared is the proportion of the variation in the outcome that the model explains, from 0 to 1. A higher value means a closer fit to the observed data, but it does not show that the model is correct or that predictions will be accurate outside the observed range.