BivariateSampleLinearRegression Method

public LinearRegressionResult LinearRegression()

Public Function LinearRegression As LinearRegressionResult

public:
LinearRegressionResult^ LinearRegression()

member LinearRegression : unit -> LinearRegressionResult 

Return Value

Linear regression assumes that the data have been generated by a function y = a + b x + e, where e is normally distributed noise, and determines the values of a and b that best fit the data. It also determines an error matrix on the parameters a and b, and does an F-test to

The fit result is two-dimensional. The first parameter is the intercept a, the second is the slope b. The goodness-of-fit test is a F-test comparing the variance accounted for by the model to the remaining, unexplained variance.

Reference