The usual ordering is:

R2(prediction)

Take a closer look at the data matrix.

Next, check out the steps leading up to processing the R².

Hello Ibikunie,

1. Can an adjusted R-squared exceed 0.99? Yes.

2. Can an adjusted R-squared exceed the associated, unadjusted R-squared? No, though they can be equal (as pointed out by Debopam Ghosh )

Here's one version of the most commonly used formula for adjusted R-squared:

Adjusted R2 = 1 – [(1-R2)*(n-1)/(n-k-1)]

where:

R2 = unadjusted/observed R-squared

n = sample size used in model

k = number of IVs included in the model

Note that, the ratio (n - 1) / (n - k - 1) will always be less than one for k = 1, 2, ...n - 1. So, unless the unadjusted R-squared = 1, the adjusted R-squared will be less than the unadjusted R-squared. As N increases, relative to k, the difference between the two values will decrease as well.

So, what this all means is that no; if R-squared = .990, adjusted R-squared cannot be .992.

Good luck with your work.