To Construct: The least square prediction equation. To Describe: The nature of the curvature in the estimated model.

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Answer 1

Question

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

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Is CEO compensation related to a company's performance? To test whether CEO compensation and a company's stock performance are related, a financial analyst collected data on 12 randomly selected, publicly traded companies. For each company, the analyst looked at two variables: the percent change in stock price over the past five years (which she denoted x ) and the percent change in CEO compensation over the past five years (which she denoted y ). For these 12 companies, the least-squares regression equation relating the two variables was =y+−0.1650.046x , and the standard error of the slope of this least-squares regression line was approximately 0.028 . Using her information, test for a significant linear relationship between these two variables by doing a hypothesis test regarding the population slope β1 . (Assume that the variable y follows a normal distribution for each value of x and that the other regression assumptions are satisfied.) Use the…

We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: regress sales salary . Source Model Residual Total sales salary _cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 t Number of obs F(1, 207) Prob> F R-squared Adj R-squared Root MSE P>|t| 1.74 0.084 5.72 0.000 209 3.02 -.1252934 3757.543 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Suppose a CEO of a company…

We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…

Answer 2

Question

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Expert Solution & Answer

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Answer 3

Question

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Expert Solution & Answer

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Answer 4

Question

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Expert Solution & Answer

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Answer 5

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Answer 6

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

Answer 7

Chapter 12, Problem 12.160ACI

a.

To determine

To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

Answer 8

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

Answer 9

b.

To determine

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

Answer 10

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

Answer 11

c.

To determine

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

Answer 12

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Answer 13

d.

To determine

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

Answer 14

Expert Solution & Answer

Answer 15

Expert Solution & Answer

Answer 16

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Answer 17

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To Construct: The least square prediction equation. To Describe: The nature of the curvature in the estimated model.

Solution Summary: The author explains that the curve is decreasing in nature. The least square prediction equation is plotted using the points.

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To Construct: The least square prediction equation.

To Describe: The nature of the curvature in the estimated model.

To Estimate: The weight change(y) for a rat the dosage of 500 mg/kg of aconiazide.

To Estimate: The weight change(y) for a rat the dosage of 0 mg/kg of aconiazide.

To Find: The largest dosage level x that yields an estimated weight change which is closest to but below the estimated weight change for the control group.

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Chapter 12 Solutions