Format: M[ultiple] [column [column]]This test measures how well one dependent variable (in one column) is predicted by a number of independent variables in other columns.
Example:
Enter command - m - Multiple linear regression - Enter column for dependent variable c27 Input number of independent variables: 4 Input 4 columns (one on each line): c18 c19 c20 c21Output:
Multiple linear regression with C27 (SEV) as dependent variable Regression equation: C27 = 0.146 + 0.093 * C19 SE(b) = 0.055 + 0.154 * C20 SE(b) = 0.075 + 0.029 * C21 SE(b) = 0.079 + 0.140 * C22 SE(b) = 0.065 Variance ratio F = (73.844/4)/0.717 = 25.747 df = 4,95 p = 0.0000 Multiple correlation coefficient R = 0.721 Significance of each measure (95 degrees of freedom): C19: t = 1.694 p = 0.0935 C20: t = 2.050 p = 0.0431 C21: t = 0.370 p = 0.7122 C22: t = 2.156 p = 0.0336This test outputs a multiple correlation coefficient and the best-fitting linear regression equation using all the independent variables. The coefficients for each variable are given and their standard errors. These are used to produce a t statistic and two-tailed significance for the independent correlation of each variable with the dependent variable. Note that this will vary according to which other variables are included in the analysis. An overall two-tailed probability derived from an F ratio of variances is also given, representing the probability of such a large multiple correlation coefficient occurring by chance.
If a second column name is given after the first, then it will be filled with the values which would be predicted from the regression equation with the coefficients arrived at. These are the values which the dependent variable would take if it was completely determined by the independent variables according to the regression equation.
Example:
Enter command - m c27 c28 Input number of independent variables: 4 Input 4 columns (one on each line): c18 c19 c20 c21In this case column 28 will be filled with the predicted values.