Least squares from five summary sums

Consider the simple linear regression model $$y_{i}=\alpha+\beta x_{i}+\epsilon_{i}, i=1,2,…,n,$$ where $\epsilon_{i}$'s are i.i.d. random variables with mean $0$ and variance $\sigma^{2}\in(0,\infty)$. Suppose that we have a data set $(x_{1},y_{1}),…,(x_{n},y_{n})$ with $n=20$, $\sum_{i=1}^{n}x_{i}=100$, $\sum_{i=1}^{n}y_{i}=50$, $\sum_{i=1}^{n}x_{i}^{2}=600$, $\sum_{i=1}^{n}y_{i}^{2}=500$ and $\sum_{i=1}^{n}x_{i}y_{i}=400$. Then the least square estimates of $\alpha$ and $\beta$ are, respectively,