Multiple Regression Calculator
This simple multiple linear regression calculator uses the least squares method to find the line of best fit for data comprising two independent X values and one dependent Y value, allowing you to estimate the value of a dependent variable (Y) from two given independent (or explanatory) variables (X1 and X2).
The line of best fit is described by the equation ŷ = b1X1 + b2X2 + a, where b1 and b2 are coefficients that define the slope of the line and a is the intercept (i.e., the value of Y when X = 0). This calculator will determine the values of b1, b2 and a for a set of data comprising three variables, and estimate the value of Y for any specified values of X1 and X2.
To begin, you need to add data into the three text boxes immediately below (either one value per line or as a comma delimited list), with your independent variables in the two X Values boxes and your dependent variable in the Y Values box. For example, if you wanted to generate a line of best fit for the association between height, weight and shoe size, allowing you to predict shoe size on the basis of a person's height and weight, then height and weight would be your independent variables (X1 and X1) and shoe size your dependent variable (Y).
This multiple regression calculator can estimate the value of a dependent variable (Y) for specified values of two independent predictor variables (X1 & X2). Simply add the X values for which you wish to generate an estimate into the Predictor boxes below (either one value per line or as a comma delimited list).
Note: If you just want to generate the regression equation that describes the line of best fit, leave the boxes below blank.