I'm not used to work with multiple regression analysis, so I would be glad if anyone could help me interpreting this result:

I performed a hierarchical multiple regression in SPSS (forced entry) in two Blocks to predict to variable

**SM**:

**Block 1:**Predictors:

**IQ**,

**AGE**

**Block 2:**Predictors:

**PT**

Results for Block 1:

*R*: .730

*R Square / Adjusted R Square*: .533 / .492

*F Change:*13.125

*Sig. F Change:*.000

Results for Block 2:

*R*: .783

*R Square / Adjusted R Square*: .614 / .561

*R Square Change*: .081

*F Change:*4.602

*Sig. F Change:*.043

I'm a little bit confused because of the F-Change: Obviously, Model 2 explains more variance (because of the R Square Change) than Model 1. But the F-ratio is lower. Or does F Change mean that the models F-ratio did improve with 4.602?

(The ANOVA-Table gives me an F-ratio of 13.125 for Model A and 11.654 for Model B).

So is Model 2 really better than Model 1?

Thanks in advance,