How to find the discriminant?

If you are performing an algebraic multivariate statistical analysis, the discriminant is very easy to find. Once you have your data in a data table, you can use the SPSS Statistics menu to run a discriminant analysis. You will need to be able to enter your data in SPSS Statistics using the default data types.

How to find the discriminant for a matrix?

Let's say you have a matrix A. You want to find the discriminant of A. There are a few ways to accomplish this. One of the ways is by listing the entries of A as a list. Then, you can use a function to calculate the sum of the squares of the entries of the first column of the list, the sum of the squares of the entries of the second column, and so on. Finally, you take the square root of the sum of the squares of the

How to find the discriminant of a matrix?

Let’s say you want to find the discriminant of a matrix A. First, find its eigenvalues and eigenvectors. This will give you a value for lambda1, lambda2 and the associated eigenvectors for each. The discriminant is just the product of the square roots of the eigenvalues.

How to find the discriminant matrix?

If the matrix is upper triangular, then you can use the determinant or the cofactor method. Both methods are easy to use and fast. If the matrix is not upper triangular, you can use the LU decomposition. If you use this method, make sure to perform the decomposition on paper (or calculate it in your head) before you use your calculator to avoid making an error. The LU decomposition method is also very fast.

How to calculate the discriminant of a matrix?

The discriminant of a square matrix is the determinant of the upper triangular matrix obtained by removing the diagonal. The discriminant is non-zero iff the matrix is degenerate, i.e., if there exists a non-zero vector that is orthogonal to every column. The procedure to calculate the discriminant works as follows: first, you calculate the covariance matrix of the data. This is the square matrix whose entries are the covariances between each pair of variables. Now