5. (2 points) One of the cities in the data set had an average temperature of 11

Question

5.               (2 points) One of the cities in the data set had an average temperature of 11 degrees and students did not miss any days of school. You should be able to locate this city on the scatterplot above. This city is an example of which of the following?

                        (A) Extrapolation      (B) Lurking variable      (C) Outlier      (D) Influential observation      (E) Residual

6.   (2 points) The regression line that gives the linear relationship between the average temperature and the number of days of school missed is predicted number of days of school missed =18.16 - 0.42(average temperature). Suppose in Richmond the average temperature during this period was 24 degrees Fahrenheit. Use the regression line to predict the number of days of school missed for a city with an average temperature of 24 degrees. Please circle your final answer.

_____    7.   (2 points) The regression line that gives the linear relationship between the average temperature and the number of days of school missed is predicted number of days of school missed =18.16 - 0.42(average temperature). Which of the following is the correct interpretation of the slope of this regression line?

(A)If the average temperature is 0.42, then the predicted number of days of school missed is 18.16 days.

(B)If the number of days of school missed decreases by 0.42 days, then the predicted average temperature increases by 18.16 degrees.

(C)If the number of days of school missed increases by 1 day, then the predicted average temperature increases by 18.16 degrees.

(D)If the number of days of school missed increases by 1 day, then the predicted average temperature decreases by 0.42 degrees.

(E)If the average temperature increases by 1 degree, then the predicted number of days of school missed increases by 18.16 days.

(F)If the average temperature increases by 1 degree, then the predicted number of days of school missed decreases by 0.42 days.

_____    8.   (2 points) The regression line that gives the linear relationship between the average temperature and the number of days of school missed is predicted number of days of school missed =18.16 - 0.42(average temperature). Which of the following is the correct interpretation of the intercept of this regression line?

(A)If the average temperature is 0.42, then the predicted number of days of school missed is 18.16 days.

(B)If the number of days of school missed decreases by 0.42 days, then the predicted average temperature increases by 18.16 degrees.

(C)If the number of days of school missed is 0 days, then the predicted average temperature is 18.16 degrees.

(D)If the number of days of school missed is 0 days, then the predicted average temperature is 0.42 degrees.

(E)If the average temperature is 0 degrees, then the predicted number of days of school missed is 18.16 days.

(F)If the average temperature is 0 degrees, then the predicted number of days of school missed is 0.42 days.

_____    9.   (2 points) Suppose in Richmond the average temperature during this period was 24 degrees Fahrenheit, and during the period students missed 9 days of school due to the weather. If one computed the difference between the observed 9 days of school missed due to bad weather and the predicted number of days of school missed for a city with an average temperature of 24 degrees using the regression line stated above, this difference is referred to as which of the following? (Note: this is a definition question, not a calculation question).

                  (A) Extrapolation      (B) Lurking variable      (C) Outlier      (D) Influential observation      (E) Residual

_____    10. (2 points) Some cities have more snow removal equipment than others, and hence the number of days of school missed due to bad weather may be different from city to city depending on the amount of snow removal equipment that they own. In this scenario, the amount of snow removal equipment that a city owns is which of the following?

                  (A) Lurking variable      (B) Residual      (C) Outlier      (D) Dependent variable      (E) Simpson’s paradox

Consider the following list of possible correlation coefficients. Use this list to answer questions 11 through 13.

(A) r = -1.05     (B) r = -0.98     (C) r = -0.64     (D) r = -0.36     (E) r = 0.04     (F) r = 0.63     (G) r = 0.93     (H) r = 1.42

_____    11. (2 points) Which of the choices above reveals the strongest correlation between the two variables?

_____    12. (2 points) Which of the choices above reveals the weakest correlation between the two variables?

_____    13. (2 points) Which of the choices above matches the relationship revealed in the scatterplot below?

Explanation / Answer

Ans:

6)number of days of school missed =18.16 - 0.42*24=18.16-10.08=8.08

7)If the average temperature increases by 1 degree, then the predicted number of days of school missed decreases by 0.42 days.(as slope is -0.42)

Option F is correct.

8)If the average temperature is 0 degrees, then the predicted number of days of school missed is 18.16 days.

Option E is correct.

9)Residual

Rsidual error is the difference between thobserved and perdicted values.

Option B is correct.

10)Lurking variable

Option A is correct.

Lurking Variable. A well-designed experiment includes design features that allow researchers to eliminate extraneous variables as an explanation for the observed relationship between the independent variable(s) and the dependent variable.

11)strongest correlation,r=-0.98

Option B is correct.

(r=1.42 and -1.05 are not valid correlation cofficient values,as -1<=r<=1)

12)Weakest correlation,r=0.04

Option E is correct.

13)The above scatterplot will have r approximately,r=0.93(positive correlation)

Option G is correct.

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