SPSS has procedures where only significant predictors are entered into the regression equation. Instead, you would want to find variables which significantly influence the dependent variable. However, many variables are not necessarily the best. In multiple regression analysis, you may use any number of variables for use as predictors. Multiple Regression Analysis: SPSS can also perform multiple regression analysis, which shows the influence of two or more variables on a designated dependent variable. Do this by highlighting the variable in the left-hand column, * on the right arrow next to Dependent or Independent(s). Here, enter the independent and dependent variables you wish to use.

A new dialog box opens which allows you to conduct regression analysis.

To perform simple linear and curvilinear regression:

The constant and the coefficient (called B-values) for the regression equation.

R square, which is the proportion of variance in one variable accounted for by the other variable.

A probability value (p) associated with R which indicates the significance of that assoc.

This is designated with a capital R (the same as the bivariate correlation 'r'). A score which measures the strength of the relationship between the DV and the IV.The output produced by the Regression command includes four different values: SPSS allows you to perform both simple and multiple regression. **= A double-click of the left mouse button * = A single click of the left mouse button

Spss correlation