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Exhibit 12-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Exhibit 12-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. -Refer to Exhibit 12-5. The least squares estimate of the Y intercept is A) 1 B) 0 C) -1 D) 3 -Refer to Exhibit 12-5. The least squares estimate of the Y intercept is


A) 1
B) 0
C) -1
D) 3

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Exhibit 12-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x) . Exhibit 12-9 A regression and correlation analysis resulted in the following information regarding a dependent variable (y) and an independent variable (x) . -Refer to Exhibit 12-9. The sample correlation coefficient equals A) 0.8045 B) -0.8045 C) 0 D) 1 -Refer to Exhibit 12-9. The sample correlation coefficient equals


A) 0.8045
B) -0.8045
C) 0
D) 1

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In regression analysis, if the independent variable is measured in pounds, the dependent variable


A) must also be in pounds
B) must be in some unit of weight
C) cannot be in pounds
D) can be any units

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Regression analysis was applied between demand for a product (Y) and the price of the product (X) , and the following estimated regression equation was obtained.Regression analysis was applied between demand for a product (Y) and the price of the product (X) , and the following estimated regression equation was obtained. = 120 - 10 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to A) increase by 120 units B) increase by 100 units C) increase by 20 units D) decease by 20 units = 120 - 10 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to


A) increase by 120 units
B) increase by 100 units
C) increase by 20 units
D) decease by 20 units

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Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. -Refer to Exhibit 12-10. The coefficient of correlation is A) 0.1905 B) -0.1905 C) 0.4364 D) -0.4364 -Refer to Exhibit 12-10. The coefficient of correlation is


A) 0.1905
B) -0.1905
C) 0.4364
D) -0.4364

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Exhibit 12-6 For the following data the value of SSE = 0.4130. Exhibit 12-6 For the following data the value of SSE = 0.4130. -Refer to Exhibit 12-6. The slope of the regression equation is A) 18 B) 24 C) 0.707 D) -1.5 -Refer to Exhibit 12-6. The slope of the regression equation is


A) 18
B) 24
C) 0.707
D) -1.5

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Exhibit 12-3 You are given the following information about y and x. Exhibit 12-3 You are given the following information about y and x. -Refer to Exhibit 12-3. The least squares estimate of b<sub>1</sub> equals A) 1 B) -1 C) -11 D) 11 -Refer to Exhibit 12-3. The least squares estimate of b1 equals


A) 1
B) -1
C) -11
D) 11

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Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. Exhibit 12-10 The following information regarding a dependent variable Y and an independent variable X is provided. -Refer to Exhibit 12-10. The MSE is A) 17 B) 8 C) 34 D) 42 -Refer to Exhibit 12-10. The MSE is


A) 17
B) 8
C) 34
D) 42

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Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of determination is A) 0.7096 B) - 0.7906 C) 0.625 D) 0.375 SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of determination is


A) 0.7096
B) - 0.7906
C) 0.625
D) 0.375

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A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation = 30,000 + 4 X The above equation implies that an A) increase of $4 in advertising is associated with an increase of $4,000 in sales B) increase of $1 in advertising is associated with an increase of $4 in sales C) increase of $1 in advertising is associated with an increase of $34,000 in sales D) increase of $1 in advertising is associated with an increase of $4,000 in sales = 30,000 + 4 X The above equation implies that an


A) increase of $4 in advertising is associated with an increase of $4,000 in sales
B) increase of $1 in advertising is associated with an increase of $4 in sales
C) increase of $1 in advertising is associated with an increase of $34,000 in sales
D) increase of $1 in advertising is associated with an increase of $4,000 in sales

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Below you are given a partial computer output based on a sample of 14 observations, relating an independent variable (x) and a dependent variable (y). Below you are given a partial computer output based on a sample of 14 observations, relating an independent variable (x) and a dependent variable (y). a.Develop the estimated regression line. b.At \alpha = 0.05, test for the significance of the slope. c.At \alpha = 0.05, perform an F test. d.Determine the coefficient of determination. e. Determine the coefficient of correlation. Below you are given a partial computer output based on a sample of 14 observations, relating an independent variable (x) and a dependent variable (y). a.Develop the estimated regression line. b.At \alpha = 0.05, test for the significance of the slope. c.At \alpha = 0.05, perform an F test. d.Determine the coefficient of determination. e. Determine the coefficient of correlation. a.Develop the estimated regression line. b.At α\alpha = 0.05, test for the significance of the slope. c.At α\alpha = 0.05, perform an F test. d.Determine the coefficient of determination. e. Determine the coefficient of correlation.

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a.blured image = 6.428 + 0.47x
b.t = 13.529; p-value...

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In a regression analysis if SSE = 200 and SSR = 300, then the coefficient of determination is


A) 0.6667
B) 0.6000
C) 0.4000
D) 1.5000

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Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided Exhibit 12-8 The following information regarding a dependent variable Y and an independent variable X is provided -Refer to Exhibit 12-8. The mean square error (MSE) is A) 1870 B) 13 C) 1974 D) 935 -Refer to Exhibit 12-8. The mean square error (MSE) is


A) 1870
B) 13
C) 1974
D) 935

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Correlation analysis is used to determine


A) the equation of the regression line
B) the strength of the relationship between the dependent and the independent variables
C) a specific value of the dependent variable for a given value of the independent variable
D) None of these alternatives is correct.

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Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Exhibit 12-1 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of correlation is A) 0.7906 B) - 0.7906 C) 0.625 D) 0.375 SSE = 6 SST = 16 -Refer to Exhibit 12-1. The coefficient of correlation is


A) 0.7906
B) - 0.7906
C) 0.625
D) 0.375

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The owner of a bakery wants to analyze the relationship between the expenditure of a customer and the customer's income. A sample of 5 customers is taken and the following information was obtained. The owner of a bakery wants to analyze the relationship between the expenditure of a customer and the customer's income. A sample of 5 customers is taken and the following information was obtained. The least squares estimated line is = 4.348 + 0.0826 X. a.Obtain a measure of how well the estimated regression line fits the data. b.You want to test to see if there is a significant relationship between expenditure and income at the 5% level of significance. Be sure to state the null and alternative hypotheses. c.Construct a 95% confidence interval estimate for the average expenditure for all customers with an income of $20,000. d.Construct a 95% confidence interval estimate for the expenditure of one customer whose income is $20,000. The least squares estimated line is The owner of a bakery wants to analyze the relationship between the expenditure of a customer and the customer's income. A sample of 5 customers is taken and the following information was obtained. The least squares estimated line is = 4.348 + 0.0826 X. a.Obtain a measure of how well the estimated regression line fits the data. b.You want to test to see if there is a significant relationship between expenditure and income at the 5% level of significance. Be sure to state the null and alternative hypotheses. c.Construct a 95% confidence interval estimate for the average expenditure for all customers with an income of $20,000. d.Construct a 95% confidence interval estimate for the expenditure of one customer whose income is $20,000. = 4.348 + 0.0826 X. a.Obtain a measure of how well the estimated regression line fits the data. b.You want to test to see if there is a significant relationship between expenditure and income at the 5% level of significance. Be sure to state the null and alternative hypotheses. c.Construct a 95% confidence interval estimate for the average expenditure for all customers with an income of $20,000. d.Construct a 95% confidence interval estimate for the expenditure of one customer whose income is $20,000.

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a.R2 = 0.0079
b.H0...

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Exhibit 12-3 You are given the following information about y and x. Exhibit 12-3 You are given the following information about y and x. -Refer to Exhibit 12-3. The coefficient of determination equals A) -0.4364 B) 0.4364 C) -0.1905 D) 0.1905 -Refer to Exhibit 12-3. The coefficient of determination equals


A) -0.4364
B) 0.4364
C) -0.1905
D) 0.1905

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A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation = 60 - 8X The above equation implies that an A) increase of $1 in price is associated with a decrease of $8 in sales B) increase of $8 in price is associated with an decrease of $52,000 in sales C) increase of $1 in price is associated with a decrease of $52 in sales D) increase of $1 in price is associated with a decrease of $8000 in sales = 60 - 8X The above equation implies that an


A) increase of $1 in price is associated with a decrease of $8 in sales
B) increase of $8 in price is associated with an decrease of $52,000 in sales
C) increase of $1 in price is associated with a decrease of $52 in sales
D) increase of $1 in price is associated with a decrease of $8000 in sales

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Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with "?". Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with ? . Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with ? . Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with ? .

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In regression analysis, the variable that is being predicted is the


A) dependent variable
B) independent variable
C) intervening variable
D) is usually x

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