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In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of correlation is


A) -0.7
B) +0.7
C) 0.49
D) -0.49

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In a regression analysis, the coefficient of determination is 0.4225. The coefficient of correlation in this situation is


A) 0.65
B) 0.1785
C) any positive value
D) any value

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In a regression analysis, the variable that is being predicted


A) must have the same units as the variable doing the predicting
B) is the independent variable
C) is the dependent variable
D) usually is denoted by x

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Exhibit 14-2 You are given the following information about y and x. Exhibit 14-2 You are given the following information about y and x.   -Refer to Exhibit 14-2. The point estimate of y when x = 10 is A) -10 B) 10 C) -4 D) 4 -Refer to Exhibit 14-2. The point estimate of y when x = 10 is


A) -10
B) 10
C) -4
D) 4

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The following data represent the number of flash drives sold per day at a local computer shop and their prices.  The following data represent the number of flash drives sold per day at a local computer shop and their prices.    a.Perform an F test and determine if the price and the number of flash drives sold are related. Let  \alpha  = 0.01. b.Perform a t test and determine if the price and the number of flash drives sold are related. Let  \alpha  = 0.01. a.Perform an F test and determine if the price and the number of flash drives sold are related. Let α\alpha = 0.01. b.Perform a t test and determine if the price and the number of flash drives sold are related. Let α\alpha = 0.01.

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a.F = 29.624 > 16.26; p-value < .01; (Ex...

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The interval estimate of an individual value of y for a given value of x is


A) prediction interval estimate
B) confidence interval estimate
C) average regression
D) x versus y correlation interval

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Exhibit 14-2 You are given the following information about y and x. Exhibit 14-2 You are given the following information about y and x.   -Refer to Exhibit 14-2. The least squares estimate of b<sub>0</sub> (intercept) equals A) 1 B) -1 C) 6 D) 5 -Refer to Exhibit 14-2. The least squares estimate of b0 (intercept) equals


A) 1
B) -1
C) 6
D) 5

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If there is a very strong correlation between two variables then the coefficient of determination must be


A) much larger than 1, if the correlation is positive
B) much smaller than -1, if the correlation is negative
C) any value larger than 1
D) None of these alternatives is correct.

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In a regression analysis if SST = 4500 and SSE = 1575, then the coefficient of determination is


A) 0.35
B) 0.65
C) 2.85
D) 0.45

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Exhibit 14-7 You are given the following information about y and x. Exhibit 14-7 You are given the following information about y and x.   -Refer to Exhibit 14-7. The sample correlation coefficient equals A) 0.3162 B) -0.3162 C) 0.10 D) -0.10 -Refer to Exhibit 14-7. The sample correlation coefficient equals


A) 0.3162
B) -0.3162
C) 0.10
D) -0.10

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Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Exhibit 14-5 The following information regarding a dependent variable (Y)  and an independent variable (X)  is provided.   -Refer to Exhibit 14-5. The MSE is A) 0 B) -1 C) 1 D) 0.5 -Refer to Exhibit 14-5. The MSE is


A) 0
B) -1
C) 1
D) 0.5

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If two variables, x and y, have a strong linear relationship, then


A) there may or may not be any causal relationship between x and y
B) x causes y to happen
C) y causes x to happen
D) None of these alternatives is correct.

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A least squares regression line


A) may be used to predict a value of y if the corresponding x value is given
B) implies a cause-effect relationship between x and y
C) can only be determined if a good linear relationship exists between x and y
D) None of these alternatives is correct.

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Exhibit 14-5 The following information regarding a dependent variable (Y) and an independent variable (X) is provided. Exhibit 14-5 The following information regarding a dependent variable (Y)  and an independent variable (X)  is provided.   -Refer to Exhibit 14-5. The coefficient of determination is A) 0 B) -1 C) 0.5 D) 1 -Refer to Exhibit 14-5. The coefficient of determination is


A) 0
B) -1
C) 0.5
D) 1

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Assume you have noted the following prices for books and the number of pages that each book contains.  Assume you have noted the following prices for books and the number of pages that each book contains.    a.Perform an F test and determine if the price and the number of pages of the books are related. Let  \alpha  = 0.01. b.Perform a t test and determine if the price and the number of pages of the books are related. Let  \alpha  = 0.01. c.Develop a 90% confidence interval for estimating the average price of books that contain 800 pages. d.Develop a 90% confidence interval to estimate the price of a specific book that has 800 pages. a.Perform an F test and determine if the price and the number of pages of the books are related. Let α\alpha = 0.01. b.Perform a t test and determine if the price and the number of pages of the books are related. Let α\alpha = 0.01. c.Develop a 90% confidence interval for estimating the average price of books that contain 800 pages. d.Develop a 90% confidence interval to estimate the price of a specific book that has 800 pages.

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a.F = 6.439 < 16.26; p-value is between ...

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Exhibit 14-6 For the following data the value of SSE = 0.4130. Exhibit 14-6 For the following data the value of SSE = 0.4130.   -Refer to Exhibit 14-6. The total sum of squares (SST)  equals A) 36 B) 18 C) 9 D) 1296 -Refer to Exhibit 14-6. The total sum of squares (SST) equals


A) 36
B) 18
C) 9
D) 1296

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An automobile dealer wants to see if there is a relationship between monthly sales and the interest rate. A random sample of 4 months was taken. The results of the sample are presented below. The estimated least squares regression equation is An automobile dealer wants to see if there is a relationship between monthly sales and the interest rate. A random sample of 4 months was taken. The results of the sample are presented below. The estimated least squares regression equation is   = 75.061 - 6.254X    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 the interest rate and monthly sales at the 1% level of significance. State the null and alternative hypotheses. c.At 99% confidence, test the hypotheses. d.Construct a 99% confidence interval for the average monthly sales for all months with a 10% interest rate. e.Construct a 99% confidence interval for the monthly sales of one month with a 10% interest rate. = 75.061 - 6.254X An automobile dealer wants to see if there is a relationship between monthly sales and the interest rate. A random sample of 4 months was taken. The results of the sample are presented below. The estimated least squares regression equation is   = 75.061 - 6.254X    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 the interest rate and monthly sales at the 1% level of significance. State the null and alternative hypotheses. c.At 99% confidence, test the hypotheses. d.Construct a 99% confidence interval for the average monthly sales for all months with a 10% interest rate. e.Construct a 99% confidence interval for the monthly sales of one month with a 10% interest rate. 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 the interest rate and monthly sales at the 1% level of significance. State the null and alternative hypotheses. c.At 99% confidence, test the hypotheses. d.Construct a 99% confidence interval for the average monthly sales for all months with a 10% interest rate. e.Construct a 99% confidence interval for the monthly sales of one month with a 10% interest rate.

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Exhibit 14-3 You are given the following information about y and x. Exhibit 14-3 You are given the following information about y and x.   -Refer to Exhibit 14-3. The least squares estimate of b<sub>1</sub> equals A) 1 B) -1 C) -11 D) 11 -Refer to Exhibit 14-3. The least squares estimate of b1 equals


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

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A regression analysis between demand (Y in 1000 units) and price (X in dollars) resulted in the following equation A regression analysis between demand (Y in 1000 units)  and price (X in dollars)  resulted in the following equation   = 9 - 3X The above equation implies that if the price is increased by $1, the demand is expected to A) increase by 6 units B) decrease by 3 units C) decrease by 6,000 units D) decrease by 3,000 units = 9 - 3X The above equation implies that if the price is increased by $1, the demand is expected to


A) increase by 6 units
B) decrease by 3 units
C) decrease by 6,000 units
D) decrease by 3,000 units

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Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s) and advertising data (x in $100s) and the following information was obtained. Exhibit 14-4 Regression analysis was applied between sales data (Y in $1,000s)  and advertising data (x in $100s)  and the following information was obtained.   = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 S<sub>b1</sub> = 0.2683 -Refer to Exhibit 14-4. The t statistic for testing the significance of the slope is A) 1.80 B) 1.96 C) 6.708 D) 0.555 = 12 + 1.8 x n = 17 SSR = 225 SSE = 75 Sb1 = 0.2683 -Refer to Exhibit 14-4. The t statistic for testing the significance of the slope is


A) 1.80
B) 1.96
C) 6.708
D) 0.555

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