# RegressionNoise

## RegressionNoise(Y, B, I, K, C)

When you have data, «Y[I]» and «B[I, K]», generated from an underlying model with unknown coefficients «C[K]» and «S» of the form:

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

This function computes an estimate for «S» by assuming that the sample noise is the same for each point in the data set.

When using in conjunction with RegressionDist, it is most efficient to provide the optional parameter «C» to both routines, where «C» is the expected value of the regression coefficients, obtained from calling Regression(Y, B, I, K). Doing so avoids an unnecessary call to the builtin Regression function.

## Library

Multivariate Distributions library functions (Multivariate Distributions.ana)