# 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).

X1 Values
X2 Values
Y Values

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.

Predictor X1
Predictor X2
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