RegressionFitProb

RegressionProbFit(Y,B,I,K,C)

Once you've obtained regression coefficients C (indexed by K) by calling the Regression function, this function returns the probability that a fit this poor would occur by chance, given the assumption that the data was generated by a process of the form:

Y = Sum( C*B,K) + Normal(0,S)

If this result is very close to zero, it probably indicates that the assumption of linearity is bad. If it is very close to one, then it validates the assumption of linearity.

Library

Multivariate Distributions.ana

This is not a distribution function - it does not return a sample when evaluated in Sample mode. However, it does complement the multivariate RegressionDist function also included in this library.

Example

To use, first call the Regression function, then you must either know the measurement knows a priori, or obtain it using the RegressionNoise function.

Var E_C := Regression(Y,B,I,K);
Var S := RegressionNoise(Y,B,I,K,C);
Var PrThisPoor := RegressionFitProb(Y,B,I,K,E_C,S)

See Also

• RegressionDist
• RegressionNoise
• Regression