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Tadahito_Iguchi	CWS	Second_Baseman	69	185	32.24
Juan_Uribe	CWS	Shortstop	71	175	27.61
Alex_Cintron	CWS	Shortstop	74	199	28.2
Joe_Crede	CWS	Third_Baseman	73	200	28.85
Josh_Fields	CWS	Third_Baseman	73	215	24.21
Ryan_Sweeney	CWS	Outfielder	76	200	22.02
Brian_N._Anderson	CWS	Outfielder	74	205	24.97
Luis_Terrero	CWS	Outfielder	74	206	26.78
Pablo_Ozuna	CWS	Outfielder	70	186	32.51
Scott_Podsednik	CWS	Outfielder	72	188	30.95
Jermaine_Dye	CWS	Outfielder	77	220	33.09
Darin_Erstad	CWS	Outfielder	74	210	32.74
Rob_Mackowiak	CWS	Outfielder	70	195	30.69
Jim_Thome	CWS	Designated_Hitter	76	244	36.51
Jerry_Owens	CWS	Designated_Hitter	75	195	26.03
Charlie_Haeger	CWS	Starting_Pitcher	73	200	23.45
Heath_Phillips	CWS	Starting_Pitcher	75	200	24.94
Gavin_Floyd	CWS	Starting_Pitcher	76	212	24.09
Jose_Contreras	CWS	Starting_Pitcher	76	224	35.23
Jon_Garland	CWS	Starting_Pitcher	78	210	27.43
Javier_Vazquez	CWS	Starting_Pitcher	74	205	30.6

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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, e-N(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%. 5. Write a 1-2 pages long report for this simulation. 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, e-N(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%. 5. Write a 1-2 pages long report for this simulation.

Answer rating: 100% (QA)

Report for Linear Regression Simulation The given population represents a linear relationship betwee... View the full answer