Slope and Leftover Variation from Summary Statistics

A least-squares line $\hat{y} = b_0 + b_1 x$ is fitted to $n = 26$ paired observations. Only these summaries are available:

• sample standard deviation of $x$: $s_x = 4$
• sample standard deviation of $y$: $s_y = 10$
• correlation: $r = -0.6$
• total sum of squares: $SST = \sum (y_i - \bar{y})^2 = 2500$

Two standard facts you may use:

$b_1 = r\,\frac{s_y}{s_x}, \qquad r^{2} = \frac{SSR}{SST}, \quad \text{where } SST = SSR + SSE$

Here $SSR$ is the variation explained by the fitted line and $SSE = \sum (y_i - \hat{y}_i)^2$ is the leftover residual variation.

Which pair of values is correct?