(Solved): Suppose we have the following simple linear regression (SLR) model: \( y=\beta_{0}+\beta_{1} x+u, E ...

Suppose we have the following simple linear regression (SLR) model: \( y=\beta_{0}+\beta_{1} x+u, E[u \mid x]=0 \). To estimate the intercept and slope parameters in this population model, you are given the following sample statistics computed from a random sample of \( \left\{x_{i}, y_{i}\right\} \) : - (sample) average of \( x(\bar{x}) \) is \( 3 . \) - (sample) average of \( y(\bar{y}) \) is 250 . - (sample) covariance between \( x \) and \( y \) is \( 20 . \) - (sample) standard deviation of \( x\left(s_{x}\right) \) is \( .5 \). - (sample) standard deviation of \( y\left(s_{y}\right) \) is \( .3 . \) A. Using the above statistics, compute an OLS estimate for the slope \( \operatorname{parameter}\left(\hat{\beta}_{1}\right) \) (Hint: The answer is an integer.) B. Using the above statistics and your answer in part A, compute an OLS estimate for the intercept parameter \( \left(\hat{\beta}_{0}{ }^{\prime}\right) \). (Hint: The answer is an integer.)