Multiple Regression calculates linear-regression equations with keyed or labeled data matrices. The result is an equation expressing the variable at the head of the first column as a linear combination of the variables heading the remaining columns, plus a constant (that is missing if Multiple Regression (no constant) was chosen). The equation produced is the best fit to the data in the least-squares sense.
Statistics + Fit Curve to Data + Multiple Regression
, Regression is: y = - 1.1703 + 1.1073x
, Regression is: x =
-
y
Statistics + Fit Curve to Data + Multiple Regression
, Regression is: z = - .126 + 2.09x + 1.03y
The choice Multiple Regression (no constant) gives the following linear equations.
Statistics + Fit Curve to Data + Multiple Regression (no constant)
, Regression is: u = .56733v
, Regression is: z = 2.1829x + .91245y
, Regression is: x =
y