I am trying to understand the relations between the coefficient of determination $R^2$ and the $F$-distributions.
Hereafter are the notations I use :
Assume a simple linear regression model $y_i=ax_i+b + \varepsilon_i, 1\leq i\leq n$, where the errors $\varepsilon_i$ follow a normal distribution with mean $0$ and constant variance $\sigma^2$. The regression line calculated by OLS is $y=\hat{a}x+\hat{b}$ and $\hat{y}_i=\hat{a} x_i +\hat{b}$ is the predicted value (at point $x_i$) and $\hat{\varepsilon}_i=y_i-\hat{y}_i$ is the residual.
Let $SSR=\sum(\hat{y}_i - \bar{y})^2$ be the sum of squares explained by the regression, $SSE=\sum(y_i - \hat{y}_i)^2$ the sum of squared errors and $SST=\sum(y_i - \bar{y})^2$ the total sum of squares.
Then $R^2=\frac{SSR}{SST}=1-\frac{SSE}{SST}$.
What I understood from documents about linear regression is that, under the null hypothesis that $a=0$ (the slope of the true regression line is zero), the two statistics $ \frac{SSR}{\sigma^2}$ and $ \frac{SST}{\sigma^2}$ follow (respectively) a chi-squared distribution with $1$ df and a chi-squared distribution with $n-1$ df, and that they are independent.
So, in my mind, the ratio $\frac{\frac{SSR}{\sigma^2} / 1}{\frac{SST}{\sigma^2} / (n-1)}$ should follow $F(1, n-1)$, a Fisher-Snedecor distribution with $df$ $1$ and $n-1$...
Instead of finding this result, what I see written everywhere is that $(n-2) \frac{R^2}{1-R^2}$ follows $F(1, n-2)$...
Why do people use this latter form instead of the former ? Are they equivalent ? Something escapes me…