Adjusted $R^2$ calculations I'm struggling to figure out how these adjusted $R^2$ values for linear regression were calculated with $n=8$ observations:

Footnote 124 says that for a model with just an intercept, $RSS$ (residual sum of squares) equals $TSS$ (total sum of squares). So using $R^2=1-\frac{RSS}{TSS}$, we get $R^2=0$ for the model with just an intercept. Then I use the formula
$$R^2_{adj} = 1-\left((1-R^2)\frac{n-1}{n-k-1}\right)$$
where $n$ is the number of observations (here $n=8$), and $k$ is the number of slopes (not including the intercept). So for the model with just the intercept, I get
$$R^2_{adj} = 1-\left((1-0)\frac{8-1}{8-1}\right) = 0$$
whereas the book has $0.4077$.
I get a different answer for the other models as well. For instance, for the model only using $X_2$ I get
$$R^2_{adj} = 1-\left(\frac{6981.58}{10693.5}\cdot \frac{8-1}{8-2}\right)=0.2383.$$
For the model with $X_1$ and $X_2$:
$$R^2_{adj} = 1-\left(\frac{915.375}{10693.5}\cdot\frac{8-1}{8-3}\right) = 0.8802$$
For the model using all three predictors:
$$R^2_{adj} = 1-\left(\frac{908.166}{10693.5}\cdot\frac{8-1}{8-4}\right) = 0.8514.$$
What am I missing or doing wrong?
 A: Not a definitive answer but from what I gathered, there are different formulas for calculating the adjusted R-squared. The adjusted R-squared tries to express the proportion of variance explained by a model on a population level. Since this is not an easy thing to estimate, there have been different proposals for calculating the adjusted R-squared. Some of the different versions include:

*

*Wherry’s formula: $1-(1-R^2)\frac{(n-1)}{(n-v)}$

*McNemar’s formula: $1-(1-R^2)\frac{(n-1)}{(n-v-1)}$

*Lord’s formula: $1-(1-R^2)\frac{(n+v-1)}{(n-v-1)}$

*Stein's formula: $1-\big[\frac{(n-1)}{(n-k-1)}\frac{(n-2)}{(n-k-2)}\frac{(n+1)}{n}\big](1-R^2)$
An often cited study in this context is Yin and Fan (2001), which is a comparison study of different R-squared versions based on simulated data. See also these three questions about this issue:
What is the adjusted R-squared formula in lm in R and how should it be interpreted?
Would the real adjusted R-squared formula please step forward?
What is an unbiased estimate of population R-square?
For your specific example I did not get the shown solutions with any of the above listed formulas, but I guess it is possible that the author used yet another formula? Perhaps footnot/reference 125 in your passage gives some indication of what was used?
Reference:

*

*Yin, P., & Fan, X. (2001). Estimating $R^2$ shrinkage in multiple regression: A comparison of different analytical methods. The Journal of Experimental Education, 69(2), 203-224.

A: The text is almost certainly in error. This is a danger with self-published works, as this appears to be. Without peer review and editors, mistakes easily creep in.
As the Wikipedia page says:

The adjusted $R^2$ can be negative, and its value will always be less than or equal to that of $R^2$.

There is no way that an intercept-only model can have an $R^2$ other than 0, so for the text to claim a substantial positive adjusted $R^2$ for that model (0.4077) must be an error.
We can also examine the implications of the claim that the 3-predictor model has an adjusted $R^2$ of 0.912. That is lower than the unadjusted $R^2$ of 0.915 so it can't be completely ruled out. But what would that mean for the relationship between $n$ and $p$? With those values and the adjusted $R^2$ formula, I get:
$$ \frac{n-1}{n-p-1}= 1.035$$
or $n \approx 1 +30p$. That's not compatible with the assumed $n=8$, particularly not if $p=3$.
I suppose it's possible that there's some explanation for such discrepancies hidden in portions of the text, but I doubt it. Get in touch with the author to clarify,
