Assuming that the Height vs Weight data posted in Week 10 is the bivariate population where the relation between the Weight (response) and Height (predictor) can be modeled by a simple linear regression, i.e. (Weight) = beta_ 0+ beta_ 1 * ( Height )+e,eN(0, sigma 2), do the followings: 1. Compute the population parameters: beta_0 and beta_1. 2. Select a random sample of size 20 from the population, i.e. 20 pair of numbers; then calculate hat(beta)_0, hat(beta)_1, hat(sigma), and s_(hat(beta)_1). 3. Create a 95% confidence interval for beta_1 and determine whether the beta_1 obtained in the step 1 is actually on the interval. 4. Repeat 10,000 times and determine the success rate to compare with 95%. SOCR Data - 68 Records of Heights (in) and Weights (Ibs) of Major League Baseball Players Assuming that the Height vs Weight data posted in Week 10 is the bivariate population where the relation between the Weight (response) and Height (predictor) can be modeled by a simple linear regression, i.e. (Weight) = beta_ 0+ beta_ 1 * ( Height )+e,eN(0, sigma 2), do the followings: 1. Compute the population parameters: beta_0 and beta_1. 2. Select a random sample of size 20 from the population, i.e. 20 pair of numbers; then calculate hat(beta)_0, hat(beta)_1, hat(sigma), and s_(hat(beta)_1). 3. Create a 95% confidence interval for beta_1 and determine whether the beta_1 obtained in the step 1 is actually on the interval. 4. Repeat 10,000 times and determine the success rate to compare with 95%. SOCR Data - 68 Records of Heights (in) and Weights (Ibs) of Major League Baseball Players