I have the following estimated model: $\hat{y} = 0.2857 + 0.8019x_1 - 0.0741x_2$ (the $t$-statistics are $1.8959$, $8.4198$, and $-3.7017$, respectively).

Furthermore, I know the sample size $N = 92$, the sum of squared residuals (SSR) $SSR = \sum_{i=1}^N[u_i^2]=39.3601$, and the standard deviation of the dependent variable $s_y = 0.8861$.

Lastly, in an earlier question I have (correctly) calculated the standard error of the regression:

- Mean of Squared Residuals (MSR) / Mean Squared Error (MSE): $MSE=\frac{1}{89} \cdot 39.3601 \approx 0.4422$
- Standard Error of the Regression: $SE_R=\sqrt{0.4422} \approx 0.6650$

*Question:* how to calculate $R^2$? (The answer should be (approximately) $0.4491$)

This (https://math.stackexchange.com/questions/834681/when-residual-standard-error-is-equal-to-standard-deviation-of-dependent-variabl) answer suggests using the formula $SE_R=(1-R^2)s_y$, but using it yields $R^2 \approx 0.4995$.

So: 

1. Is this the right formula to use? (If not, which one should I use?)
2. Is the 'residual standard error' (mentioned in the answer) the same as the 'standard error of the regression'?