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For RRJha (or anyone else...

For RRJha (or anyone else available),

Here's a few statistics questions I'm looking to get some answers to and I need your insight on how to solve them.

TABLE A

A bank tests the null hypothesis that the mean age of the bank's mortgage holders is less than or equal to 45, versus an alternative that the mean age is greater than 45. They take a sample and calculate a p value of 0.0202.

3. Referring to Table A, if the same sample was used to test the opposite one-tailed test, what would be this test's p value?

a. 0.0202

b. 0.0404

c. 0.9596

d. 0.9798

TABLE B

A student claims that he can correctly identify whether a person is a business major or an agriculture major by the way the person dresses. Suppose in actuality that he can correctly identify a business major 87% of the time, while 16% of the time he mistakenly identifies an agriculture major as a business major. Presented with one person and asked to identify the major of this person (who is either a business or agriculture major), he considers this to be a hypothesis test with the null hypothesis being that the person is a business major and the alternative that the person is an agriculture major.

4. Referring to Table B, what would be a Type II error?

a. Saying that the person is a business major when in fact the person is a business major.

b. Saying that the person is a business major when in fact the person is an agriculture major.

c. Saying that the person is an agriculture major when in fact the person is a business major.

d. Saying that the person is an agriculture major when in fact the person is an agriculture major.

TABLE E

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 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) = Bo + 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)

__________________________________________________________________________

11. Referring to Table E, interpret the estimate of Bo, the Y-intercept of the line.

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

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

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.

TABLE G

The following EXCEL tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance"

(X) for 22 students in a Statistics for Business and Economics course.

________________________________________

Regression Statistics

________________________________________

Multiple R 0.142620229

R Square 0.02034053

Adjusted R Square -0.028642444

Standard Error 20.25979924

Observations 22

________________________________________

__________________________________________________________________________

Coefficients Standard Error T Stat P-value

__________________________________________________________________________

Intercept 39.39027309 37.24347659 1.057642216 0.302826622

Attendance 0.340583573 0.52852452 0.644404489 0.526635689

__________________________________________________________________________

14. Referring to Table G, which of the following statements is true?

a. -2.86% of the total variability in score received can be explained by percentage attendance.

b. -2.86% of the total variability in percentage attendance can be explained by score received.

c. 2% of the total variability in score received can be explained by percentage attendance.

d. 2% of the total variability in percentage attendance can be explained by score received.

Here's a few statistics questions I'm looking to get some answers to and I need your insight on how to solve them.

TABLE A

A bank tests the null hypothesis that the mean age of the bank's mortgage holders is less than or equal to 45, versus an alternative that the mean age is greater than 45. They take a sample and calculate a p value of 0.0202.

3. Referring to Table A, if the same sample was used to test the opposite one-tailed test, what would be this test's p value?

a. 0.0202

b. 0.0404

c. 0.9596

d. 0.9798

TABLE B

A student claims that he can correctly identify whether a person is a business major or an agriculture major by the way the person dresses. Suppose in actuality that he can correctly identify a business major 87% of the time, while 16% of the time he mistakenly identifies an agriculture major as a business major. Presented with one person and asked to identify the major of this person (who is either a business or agriculture major), he considers this to be a hypothesis test with the null hypothesis being that the person is a business major and the alternative that the person is an agriculture major.

4. Referring to Table B, what would be a Type II error?

a. Saying that the person is a business major when in fact the person is a business major.

b. Saying that the person is a business major when in fact the person is an agriculture major.

c. Saying that the person is an agriculture major when in fact the person is a business major.

d. Saying that the person is an agriculture major when in fact the person is an agriculture major.

TABLE E

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 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) = Bo + 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)

__________________________________________________________________________

11. Referring to Table E, interpret the estimate of Bo, the Y-intercept of the line.

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

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

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.

TABLE G

The following EXCEL tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance"

(X) for 22 students in a Statistics for Business and Economics course.

________________________________________

Regression Statistics

________________________________________

Multiple R 0.142620229

R Square 0.02034053

Adjusted R Square -0.028642444

Standard Error 20.25979924

Observations 22

________________________________________

__________________________________________________________________________

Coefficients Standard Error T Stat P-value

__________________________________________________________________________

Intercept 39.39027309 37.24347659 1.057642216 0.302826622

Attendance 0.340583573 0.52852452 0.644404489 0.526635689

__________________________________________________________________________

14. Referring to Table G, which of the following statements is true?

a. -2.86% of the total variability in score received can be explained by percentage attendance.

b. -2.86% of the total variability in percentage attendance can be explained by score received.

c. 2% of the total variability in score received can be explained by percentage attendance.

d. 2% of the total variability in percentage attendance can be explained by score received.

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