Question

a)Listed below are annual data for various years. The data are weights (metric tons) of lemons imported from Mexico and U.S. car crash fatality rates per 100,000 population.

Lemon imports |
230 | 265 | 358 | 480 | 530 |

Crash fatality rate |
15.9 | 15.7 | 15.4 | 15.3 | 14.9 |

Draw a scatterplot of these two variables and comment on the following:

- There appears to be [ Select ] ["a strong positive", "a strong negative", "a weak", "no"] correlation.
- Correlation coefficient, r = [ Select ] ["0.959", "-0.959", "0.258", "0.043"] (choose the appropriate value based on the scatterplot)
- Conclusion: [ Select ] ["Greater amounts of lemon imports are associated with lower crash fatality rates, but common sense suggests this is a coincidence", "Greater amounts of lemon imports has no association with lower crash fatality rates"]

**b)It has been found that when gas prices increase, the
distances that vehicles are driven tend to get
shorter.**

Select the correct option to describe correlation between gas price and distance driven

Group of answer choices

There is a negative correlation that is probably due to coincidence

There is a positive correlation that is probably due to a direct cause.

There is a negative correlation that is probably due to a direct cause.

There is a positive correlation that is probably due to a common under-lying cause

Answer #1

using minitab>stat>basic stat>correlation

we have

Correlation: Lemon imports, Crash fatality rate

Pearson correlation of Lemon imports and Crash fatality rate =
-0.959

P-Value = 0.010

scatterplot is

Draw a scatterplot of these two variables and comment on the following:

- There appears to be a strong negative correlation.
- Correlation coefficient, r =-0.959
- Conclusion Greater amounts of lemon imports are associated with lower crash fatality rates, but common sense suggests this is a coincidence

**b)**

There is a negative correlation that is probably due to a direct cause.

Listed below are annual data for various years. The data are
weights (metric tons) of imported lemons and car crash fatality
rates per 100,000 population. Construct a scatterplot, find the
value of the linear correlation coefficient r, and find the
P-value using
alphaαequals=0.050.05.
Is there sufficient evidence to conclude that there is a linear
correlation between lemon imports and crash fatality rates? Do the
results suggest that imported lemons cause car fatalities?
Lemon Imports
228
264
359
480
533
Crash...

7. Listed below are annual data for various years. The data are
weights (metric tons) of imported lemons and car crash fatality
rates per 100,000 population. Construct a scatterplot, find the
value of the linear correlation coefficient r, and find the
P-value using alphaequals0.05. Is there sufficient evidence to
conclude that there is a linear correlation between lemon imports
and crash fatality rates? Do the results suggest that imported
lemons cause car fatalities?
Lemon_Imports_(x)
Crash_Fatality_Rate_(y)
231 15.9
264 15.7
358 ...

Listed below are annual data for various years. The data are
weights (metric tons) of imported lemons and car crash fatality
rates per 100,000 population. Construct a scatterplot, find the
value of the linear correlation coefficient r, and find the
P-value using α=0.05 Is there sufficient evidence to conclude that
there is a linear correlation between lemon imports and crash
fatality rates? Do the results suggest that imported lemons cause
car fatalities?
Lemon Imports
228
266
357
483
534
Crash...

Listed below are annual data for various years. The data are
weights (metric tons) of imported lemons and car crash fatality
rates per 100,000 population. Construct a scatterplot, find the
value of the linear correlation coefficient r, and find the
P-value using alpha = 0.05. Is there sufficient evidence to
conclude that there is a linear correlation between lemon imports
and crash fatality rates? Do the results suggest that imported
lemons cause car fatalities?
Lemon_Imports_(x) Crash_Fatality_Rate_(y)
230
15.8
266 ...

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