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When two items are heavily correlated, causality is the proper conclusion.
(1 pt)Test of duplicating

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True

False

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Dave decided to lift weights during bowling season. The more time he spent lifting weights, the lower his bowling average dropped. Which correlation number below could characterize Dave's situation with respect to time spent weight lifting and average bowling scores? (1 pt)

Select one:

a. -1.10

b. -.50

c. 0

d. .50

e. 1.10

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Outliers have little effect on regression coefficients. (1 pt)

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True

False

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Assume we want to test significance of the correlation coefficient (r) using the t-test method. Assume your sample is 27 observations and we want a 99% level of confidence. Provide the critical value for r from the Table-F (to three decimals).

(1 pt)

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Assume we want to test significance of the correlation coefficient (r) using the t-test method. Assume your sample is 27 observations. Assume the computed r is .8 .

Compute the test value (to three decimals).

(Hint: Text example problem 10-3 may be used as a model problem.)

(2 pts)

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Assume you want to test the significant of the r at the 95% level. The F-Table gives you a critical t-value of 2.5. You compute an actual statistic of 3.

How do you interpret this result? (Note: There is no math here, just theory.)

Select one:

a. No evidence of linear relationship between the variables. (You accept the possibility of being wrong 5% of the time.)

b. No evidence of linear relationship between the variables. (You accept the possibility of being wrong 10% of the time.)

c. You conclude there is a linear relationship between the variables. (You accept the possibility of being wrong 5% of the time.)

d. You conclude there is a linear relationship between the variables. (You accept the possibility of being wrong 10% of the time.)

e. You cannot answer due to not knowing the sample size (n) and the original correlation statistic.

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Why would we run a regression of pre-cancerous lung cells versus total lifetime number of cigarettes smoked? (1 pt)

Select one:

a. To predict smoking levels (cigarettes smoked per lifetime) as a function of observed pre-cancerous cells.

b. To predict the number of pre-cancerous cells as a function of cigarette smoking levels.

c. To predict average cigarettes smoked in future years (measure of addiction).

d. To predict cigarette expenditures.

e. To predict health care expenditures.

f. None of the above.

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Hypothetical disease XYZ is a rare condition associated with taking statistics classes. A regression model was developed with the probability of getting the disease as the dependent variable (Y), and hours sitting in statistics class lectures as the independent variable (H). Consider the following hypothetical model of the probability of getting disease XYZ:

P(XYZ) = .10 (H) - 5

What is likely about the regression results?

(2 pts)

Select one:

a. The constant term (-5) is insignificant (not different from zero).

b. The constant term (-5) is significant (different from zero).

c. The H coefficient (.10) is insignificant (not different from zero).

d. The H coefficient (.10) carries the wrong sign (sign should be negative).

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Student scores on statistics exams (Y) are sometimes a function of the number of hours a student spends drinking and carousing the night before the exam (X). Assume the following relationship is determined through regression analysis:

Y = 75 - 5X

On average, a student drinking and carousing for three hours the night before an exam would earn what score on the test?

Provide your answer to one decimal place.

(2 pts.)

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An established car dealership ran regressions on winter month sales. They obtained the following results.

Count of Car Sales = 150 + 10(X) - 3(Y)

Where X was $10,000 of advertising, and Y was snowy days.

Both explanatory variables and the constant term were significantly different than zero.

How do you interpret these results.

(2 pts)

Select one:

a. Results make sense: Dealership will sell 150 cars if they advertise as they always do, and whether or not they advertise due to past advertising, established customer base, and walk-up business. Spending on advertising will increase sales, so advertising has a positive coefficient sign. Snowy weather will keep customers away, thus the negative coefficient sign.

b. The results do not make sense. The constant term (150) makes no sense. The advertising and Why would the delearship expect to sell cars without advertising

Explanation / Answer

Dave decided to lift weights during bowling season. The more time he spent lifting weights, the lower his bowling average dropped. Which correlation number below could characterize Dave's situation with respect to time spent weight lifting and average bowling scores?

This hints a positive correlation. However, correlation cannot exceed 1.

Hence,

OPTION D: 0.50 [ANSWER]

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Outliers have little effect on regression coefficients.

FALSE. [ANSWER]

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Assume we want to test significance of the correlation coefficient (r) using the t-test method. Assume your sample is 27 observations and we want a 99% level of confidence. Provide the critical value for r from the Table-F (to three decimals).

Here, df = n - 2 = 25. Hence, at 99% confidence, using the table,

rcrit = 0.487 [ANSWER]

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Assume we want to test significance of the correlation coefficient (r) using the t-test method. Assume your sample is 27 observations. Assume the computed r is .8 .

t = r sqrt((n-2)/(1-r^2)) = 0.8*sqrt(25/(1-0.8^2)) = 6.667 [ANSWER]

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