# 20 Business Statistics Questions

Please see attached file to view the tables.

1. The Y-intercept (b0) represents the

A) variation around the sample regression line.

B) predicted value of Y when X = 0.

C) predicted value of Y.

D) change in estimated average Y per unit change in X.

2. The slope (b1) represents the

A) predicted value of Y when X = 0.

B) estimated average change in Y per unit change in X.

C) variation around the line of regression.

D) predicted value of Y.

3. The least squares method minimizes which of the following?

A) SST

B) SSE

C) SSR

D) all of the above

4. The standard error of the estimate is a measure of the

A) explained variation.

B) variation of the X variable.

C) total variation of the Y variable.

D) variation around the sample regression line.

5.

TABLE 13-1

A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (Y)-measured in dollars per month-for services rendered to local companies. One independent variable used to predict the service charge to a company is the company's sales revenue (X)-measured in millions of dollars. Data for 21 companies who use the bank's services were used to fit the model:

E(Y) = b0 + b1X

The results of the simple linear regression are provided below.

Y= -2,700 + 20X, SYX = 65, two-tailed p value = 0.034 (for testing b1)

Referring to Table 13-1, interpret the estimate of b0, the Y-intercept of the line.

A) There is no practical interpretation since a sales revenue of $0 is a nonsensical value.

B) All companies will be charged at least $2,700 by the bank.

C) About 95% of the observed service charges fall within $2,700 of the least squares line.

D) For every $1 million increase in sales revenue, we expect a service charge to decrease $2,700.

6.

TABLE 13-1

A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (Y)-measured in dollars per month-for services rendered to local companies. One independent variable used to predict the service charge to a company is the company's sales revenue (X)-measured in millions of dollars. Data for 21 companies who use the bank's services were used to fit the model:

E(Y) = b0 + b1X

The results of the simple linear regression are provided below.

Y = -2,700 + 20X, SYX = 65, two-tailed p value = 0.034 (for testing b1)

Referring to Table 13-1, interpret the estimate of the standard deviation of the random error term (standard error of the estimate) in the model.

A) About 95% of the observed service charges equal their corresponding predicted values.

B) About 95% of the observed service charges fall within $65 of the least squares line.

C) For every $1 million increase in sales revenue, we expect a service charge to increase $65.

D) About 95% of the observed service charges fall within $130 of the least squares line.

7.

TABLE 13-1

A large national bank charges local companies for using their services. A bank official reported the results of a regression analysis designed to predict the bank's charges (Y)-measured in dollars per month-for services rendered to local companies. One independent variable used to predict the service charge to a company is the company's sales revenue (X)-measured in millions of dollars. Data for 21 companies who use the bank's services were used to fit the model:

E(Y) = b0 + b1X

The results of the simple linear regression are provided below.

Y = -2,700 + 20X, SYX = 65, two-tailed p value = 0.034 (for testing b1)

Referring to Table 13-1, interpret the p value for testing whether b1 exceeds 0.

A) For every $1 million increase in sales revenue, we expect a service charge to increase $0.034.

B) Sales revenue (X) is a poor predictor of service charge (Y).

C) There is sufficient evidence (at the alpha = 0.05) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y).

D) There is insufficient evidence (at the alpha = 0.10) to conclude that sales revenue (X) is a useful linear predictor of service charge (Y).

8.

TABLE 13-1

E(Y) = b0 + b1X

The results of the simple linear regression are provided below.

Y= -2,700 + 20X, SYX = 65, two-tailed p value = 0.034 (for testing b1)

Referring to Table 13-1, a 95% confidence interval for b1 is (15, 30). Interpret the interval.

A) We are 95% confident that the sales revenue (X) will increase between $15 and $30 million for every $1 increase in service charge (Y).

B) At the alpha = 0.05 level, there is no evidence of a linear relationship between service charge (Y) and sales revenue (X).

C) We are 95% confident that the mean service charge will fall between $15 and $30 per month.

D) We are 95% confident that average service charge (Y) will increase between $15 and $30 for every $1 million increase in sales revenue (X).

9.

TABLE 13-3

The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.

Referring to Table 13-3, the least squares estimate of the slope is ________.

10.

TABLE 13-3

The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.

Referring to Table 13-3, the least squares estimate of the Y-intercept is ________.

11.

TABLE 13-3

The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of 4 students. For these 4, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.

Referring to Table 13-3, the prediction for the number of job offers for a person with 2 co-op jobs is ________.

12.

TABLE 13-3

Referring to Table 13-3, the total sum of squares (SST) is ________.

13.

TABLE 13-3

Referring to Table 13-3, the regression sum of squares (SSR) is ________.

14.

TABLE 13-3

Referring to Table 13-3, the error or residual sum of squares (SSE) is ________.

15.

TABLE 13-3

Referring to Table 13-3, the coefficient of determination is ________.

16.

TABLE 13-3

Referring to Table 13-3, the standard error of estimate is ________.

17.

TABLE 13-3

Referring to Table 13-3, the coefficient of correlation is ________.

18.

TABLE 13-3

Referring to Table 13-3, suppose the director of cooperative education wants to obtain a 95% confidence-interval estimate for the mean number of job offers received by people who have had exactly one cooperative education job. The t critical value she would use is ________.

19.

TABLE 13-3

Referring to Table 13-3, suppose the director of cooperative education wants to obtain a 95% confidence interval estimate for the mean number of job offers received by people who have had exactly one cooperative education job. The confidence interval is from ________ to ________.

20.

TABLE 13-3

Referring to Table 13-3, suppose the director of cooperative education wants to obtain a 95% prediction interval estimate for the mean number of job offers received by people who have had exactly one cooperative education job. The prediction interval is from ________ to ________.

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