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In correlation analysis, the independent variable is


A) the variable that is scaled on the vertical axes.
B) the variable that is being predicted or estimated.
C) a variable that provides the basis for estimation. It is the predictor variable.

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A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: This model predicts that with 25 sales contacts, sales will be: A) $49,576. B) $42,022. C) $302,831. D) $67,067. E) $42,665. A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: This model predicts that with 25 sales contacts, sales will be: A) $49,576. B) $42,022. C) $302,831. D) $67,067. E) $42,665. A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: This model predicts that with 25 sales contacts, sales will be: A) $49,576. B) $42,022. C) $302,831. D) $67,067. E) $42,665. This model predicts that with 25 sales contacts, sales will be:


A) $49,576.
B) $42,022.
C) $302,831.
D) $67,067.
E) $42,665.

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We have collected price per share and dividend information from a sample of 30 companies. We have collected price per share and dividend information from a sample of 30 companies. The y-intercept in this instance suggests: A) for each additional dollar in stock price, we can anticipate an additional $2.73 in dividend. B) for each additional dollar in stock price, we can anticipate a drop of $2.41 in dividend. C) when the stock price is zero, we can anticipate a dividend of $0.27. This value, however, makes no sense. D) when the stock price is zero, we can anticipate a dividend of $-3.68. This value, however, makes no sense. E) when the dividends are zero, we can anticipate a negative share price. We have collected price per share and dividend information from a sample of 30 companies. The y-intercept in this instance suggests: A) for each additional dollar in stock price, we can anticipate an additional $2.73 in dividend. B) for each additional dollar in stock price, we can anticipate a drop of $2.41 in dividend. C) when the stock price is zero, we can anticipate a dividend of $0.27. This value, however, makes no sense. D) when the stock price is zero, we can anticipate a dividend of $-3.68. This value, however, makes no sense. E) when the dividends are zero, we can anticipate a negative share price. The y-intercept in this instance suggests:


A) for each additional dollar in stock price, we can anticipate an additional $2.73 in dividend.
B) for each additional dollar in stock price, we can anticipate a drop of $2.41 in dividend.
C) when the stock price is zero, we can anticipate a dividend of $0.27. This value, however, makes no sense.
D) when the stock price is zero, we can anticipate a dividend of $-3.68. This value, however, makes no sense.
E) when the dividends are zero, we can anticipate a negative share price.

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The following table shows the number of workdays absent based on the length of employment in years. The following table shows the number of workdays absent based on the length of employment in years. i. The Y intercept of the linear equation is 7.7407. ii. The dependent variable (Y) is the number of work days absent. iii. The slope of the linear equation is -0.6852 A) (i) , (ii) , and (iii) are all correct statements. B) (i) and (ii) are correct statements but not (iii) . C) (i) and (iii) are correct statements but not (ii) . D) (ii) and (iii) are correct statements but not (i) . E) (i) , (ii) , and (iii) are all false statements. i. The Y intercept of the linear equation is 7.7407. ii. The dependent variable (Y) is the number of work days absent. iii. The slope of the linear equation is -0.6852


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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What does the coefficient of determination equal if r = 0.89?


A) 0.94
B) 0.89
C) 0.79
D) 0.06

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A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this believe, the following data was collected: A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this believe, the following data was collected: The 95% prediction interval for a particular person making 30 calls is: A) 55.8, 51.5 B) 51.4, 55.9 C) 46.7, 60.6 D) 31.1, 76.2 A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this believe, the following data was collected: The 95% prediction interval for a particular person making 30 calls is: A) 55.8, 51.5 B) 51.4, 55.9 C) 46.7, 60.6 D) 31.1, 76.2 A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this believe, the following data was collected: The 95% prediction interval for a particular person making 30 calls is: A) 55.8, 51.5 B) 51.4, 55.9 C) 46.7, 60.6 D) 31.1, 76.2 The 95% prediction interval for a particular person making 30 calls is:


A) 55.8, 51.5
B) 51.4, 55.9
C) 46.7, 60.6
D) 31.1, 76.2

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The partial Mega Stat output below is regression analysis of the relationship between attendance and number of wins in a season for a sample of 11 teams in professional sports. The purpose of the analysis is to predict annual attendance (000) when given the number of wins. The partial Mega Stat output below is regression analysis of the relationship between attendance and number of wins in a season for a sample of 11 teams in professional sports. The purpose of the analysis is to predict annual attendance (000) when given the number of wins. Refer to the printout above. The Sum of Squares Regression is: A) 1,866,821 B) 736,467 C) 1,130,353 D) 82.5157 E) 28.2049 Refer to the printout above. The Sum of Squares Regression is:


A) 1,866,821
B) 736,467
C) 1,130,353
D) 82.5157
E) 28.2049

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i. The purpose of correlation analysis is to find how strong the relationship is between two variables. ii. A correlation coefficient of -1 or + 1 indicates perfect correlation. iii. The standard error of estimate measures the accuracy of our prediction.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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The following table shows the number of workdays absent based on the length of employment in years. The following table shows the number of workdays absent based on the length of employment in years. i. The least squares equation for the data: Y' = 7.7407 - 0.6852X. ii. The dependent variable (Y) is the number of work days absent. iii. The negative slope indicates an inverse relationship between the variables. A) (i) , (ii) , and (iii) are all correct statements. B) (i) and (ii) are correct statements but not (iii) . C) (i) and (iii) are correct statements but not (ii) . D) (ii) and (iii) are correct statements but not (i) . E) (i) , (ii) , and (iii) are all false statements. i. The least squares equation for the data: Y' = 7.7407 - 0.6852X. ii. The dependent variable (Y) is the number of work days absent. iii. The negative slope indicates an inverse relationship between the variables.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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Given the following five points: (-2,0) , (-1,0) , (0,1) , (1,1) , and (2,3) . What is the critical value necessary to determine a confidence interval for a 90% level of confidence?


A) 1.533
B) 1.638
C) 2.132
D) 2.353

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i. The strength of the correlation between two variables depends on the sign of the coefficient of correlation. ii. A coefficient of correlation close to 0 (say, 0.08) shows that the relationship between two variables is quite weak. iii. Coefficients of -0.91 and + 0.91 have equal strength.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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i. If the coefficient of correlation is 0.70, what is the coefficient of determination? ii. If the value of r is -0.88, what does this indicate about the dependent variable as the independent variable increases? iii. If the dependent variable is measured in hours, in what units is the standard error of estimate measured?


A) 0.49; it decreases; hours.
B) 0.49; it decreases; hours squared.
C) 0.49; it increases; hours.
D) 0.8367; it decreases; hours.
E) 0.8367; it increases; hours squared.

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i. The coefficient of determination is the proportion of the total variation in the dependent variable Y that is explained or accounted for by its relationship with the independent variable X. ii. The coefficient of determination is found by taking the square root of the coefficient of correlation. iii. The standard error of estimate measures the accuracy of our prediction.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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The following table shows the number of workdays absent based on the length of employment in years. The following table shows the number of workdays absent based on the length of employment in years. Determine the linear regression equation. A) Y' = 7.7107 - 0.6852X B) Y' = 7.7407 - 0.6852X C) Y' = 7.7407 + 0.6852X D) Y' = -7.7407 - 0.6852X The following table shows the number of workdays absent based on the length of employment in years. Determine the linear regression equation. A) Y' = 7.7107 - 0.6852X B) Y' = 7.7407 - 0.6852X C) Y' = 7.7407 + 0.6852X D) Y' = -7.7407 - 0.6852X Determine the linear regression equation.


A) Y' = 7.7107 - 0.6852X
B) Y' = 7.7407 - 0.6852X
C) Y' = 7.7407 + 0.6852X
D) Y' = -7.7407 - 0.6852X

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The following table shows the number of workdays absent based on the length of employment in years. The following table shows the number of workdays absent based on the length of employment in years. Predict the number of days absent when an employee has 6 years of employment. A) 3.63 B) 6.33 C) 3.36 D) 6.36 The following table shows the number of workdays absent based on the length of employment in years. Predict the number of days absent when an employee has 6 years of employment. A) 3.63 B) 6.33 C) 3.36 D) 6.36 Predict the number of days absent when an employee has 6 years of employment.


A) 3.63
B) 6.33
C) 3.36
D) 6.36

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The slope of the regression line:


A) represents the average change in Y' for each change of one unit in the independent variable, X.
B) represents the average change in X for each change of one unit in the dependent variable, Y.
C) represents the change in Y' for each change of one unit in the independent variable, X.

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Information was collected from employee records to determine whether there is an association between an employee's age and the number or workdays they miss. Partial excel results are summarized below: Information was collected from employee records to determine whether there is an association between an employee's age and the number or workdays they miss. Partial excel results are summarized below: Given this information alone, would you decide to continue with the regression analysis? A) Yes, because the information is based on a significant size sample. B) Yes, because the multiple r value is large. C) No, because the multiple r value is small. D) No, because the standard error value is more than 5. E) Yes, because the standard error value is more than 5. Given this information alone, would you decide to continue with the regression analysis?


A) Yes, because the information is based on a significant size sample.
B) Yes, because the multiple r value is large.
C) No, because the multiple r value is small.
D) No, because the standard error value is more than 5.
E) Yes, because the standard error value is more than 5.

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In correlation analysis, the dependent variable is


A) the variable that is scaled on the horizontal axes.
B) the variable that is being predicted or estimated.
C) a variable that provides the basis for estimation. It is the predictor variable.

Correct Answer

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A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: What is the value of the coefficient of determination? A) 9.3104 B) 0.9754 C) 0.6319 D) 0.9513 A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: What is the value of the coefficient of determination? A) 9.3104 B) 0.9754 C) 0.6319 D) 0.9513 A sales manager for an advertising agency believes there is a relationship between the number of contacts and the amount of the sales. To verify this belief, the following data was collected: What is the value of the coefficient of determination? A) 9.3104 B) 0.9754 C) 0.6319 D) 0.9513 What is the value of the coefficient of determination?


A) 9.3104
B) 0.9754
C) 0.6319
D) 0.9513

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The following table shows the number of workdays absent based on the length of employment in years. The following table shows the number of workdays absent based on the length of employment in years. i. The Y intercept of the linear equation is 4.407. ii. The dependent variable (Y) is the number of work days absent. iii. The slope of the linear equation is -0.6852. A) (i) , (ii) , and (iii) are all correct statements. B) (i) and (ii) are correct statements but not (iii) . C) (i) and (iii) are correct statements but not (ii) . D) (ii) and (iii) are correct statements but not (i) . E) (i) , (ii) , and (iii) are all false statements. i. The Y intercept of the linear equation is 4.407. ii. The dependent variable (Y) is the number of work days absent. iii. The slope of the linear equation is -0.6852.


A) (i) , (ii) , and (iii) are all correct statements.
B) (i) and (ii) are correct statements but not (iii) .
C) (i) and (iii) are correct statements but not (ii) .
D) (ii) and (iii) are correct statements but not (i) .
E) (i) , (ii) , and (iii) are all false statements.

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