General LS Linear Fit PtByPt (Not in Base Package)

Finds the Best Fit k-dimensional plane and the set of linear coefficients. Details

initialize, when TRUE, initializes the internal state of the VI.
H is the matrix which represents the formula you use to fit the data set{X,Y}. H[i][j] are the function values of X[i].
input data y is the number of elements in the set of y values. input data y should equal the number of rows in H.
Standard Deviation is the standard deviation for data point (xi, yi). The default is 1.0. If xi and yi are equal or you do not know their values, leave Standard Deviation empty.
covariance selector selects whether to compute the covariance matrix.

0do not compute Covariance
1compute the Covariance matrix
algorithm specifies the algorithm the VI uses to compute Best Fit.

0SVD (default)
1Givens
2Givens2
3Householder
4LU decomposition
5Cholesky
sample length is the length of each set of incoming data. The VI performs computation for each set of data. The default is 100. sample length must be greater than zero.
Coefficients is the set of coefficients that minimize chi squared.
Best Fit is the fitted data computed by using Coefficients.
Covariance is the matrix of covariances C, with k-by-k elements. cij is the covariance between ai and aj and cii is the variance of aj.
mse is the mean squared error.
error returns any error or warning condition from the VI. Refer to Point By Point Error Codes for more information about these conditions.

General LS Linear Fit PtByPt Details

The General LS Linear Fit PtByPt VI uses the least-chi-square method for observation data sets where i = 0, 1,…, n - 1, where n is the number of your observation data sets.

The General LS Linear Fit PtByPt VI finds the k-dimensional linear curve values and the set of k-dimensional linear fit coefficients, which describe the k-dimensional linear curve that best represents the input data set using the least-squares solution. The general form of the k-dimensional linear fit is

z[i] = b[0] + Sum{ b[j] H(X[i],j)} for j = 1, 2, …, k-1; i = 0, 1, …, n-1,

where H is the input matrix which represents the formula you use to fit the data set{X,Y}. H[i][j] are the function values of X[i].

b is the set of Coefficients

k is the number of elements in Coefficients.

n is the number of elements in data set{X,Y}

Z is the output Best Fit.