1
Which statement about correlation is FALSE?
The correlation of a data set can be positive, negative, or 0.
Correlation between the variables of the data set can be measured.
Correlation is the degree to which the two variables of a data set resemble each other.
Correlation is used to define the variables of only non-linearly related data sets.
RATIONALE
Recall that correlation is used for linear association between 2 quantitative variables,
NOT for non-linearly related variables.
CONCEPT
Correlation
2
Which of the following statements is TRUE?
Only a correlation of 0 implies causation.
High correlation always implies causation.
High correlation does not always imply causation.
Only a correlation of 1 implies causation.
RATIONALE
Recall that correlation doesn't imply causation. Causation is a direct change in one
variable causing a change in some outcome. Correlation is simply a measure of
association. It is required for causation, but alone does not mean something is causal.
CONCEPT
Correlation and Causation
3
Shawna reads a scatterplot that displays the relationship between the
number of cars owned per household and the average number of citizens
, who have health insurance in neighborhoods across the country. The plot
shows a strong positive correlation.
Shawna recalls that correlation does not imply causation. In this example,
Shawna sees that increasing the number of cars per household would not
cause members of her community to purchase health insurance.
Identify the lurking variable that is causing an increase in both the number of
cars owned and the average number of citizens with health insurance.
The number of cars on the road
Average mileage per vehicle
Average income per household
The number of citizens in the United States
RATIONALE
Recall that a lurking variable is something that must be related to the outcome and
explanatory variable that when considered can help explain a relationship between 2
variables. Since higher income is positively related to owning more cars and having
health insurance, this variable would help explain why we see this association.
CONCEPT
Correlation and Causation
4
Which of the following scatterplots shows an outlier in both the x-
and y-direction?
,
RATIONALE
Which statement about correlation is FALSE?
The correlation of a data set can be positive, negative, or 0.
Correlation between the variables of the data set can be measured.
Correlation is the degree to which the two variables of a data set resemble each other.
Correlation is used to define the variables of only non-linearly related data sets.
RATIONALE
Recall that correlation is used for linear association between 2 quantitative variables,
NOT for non-linearly related variables.
CONCEPT
Correlation
2
Which of the following statements is TRUE?
Only a correlation of 0 implies causation.
High correlation always implies causation.
High correlation does not always imply causation.
Only a correlation of 1 implies causation.
RATIONALE
Recall that correlation doesn't imply causation. Causation is a direct change in one
variable causing a change in some outcome. Correlation is simply a measure of
association. It is required for causation, but alone does not mean something is causal.
CONCEPT
Correlation and Causation
3
Shawna reads a scatterplot that displays the relationship between the
number of cars owned per household and the average number of citizens
, who have health insurance in neighborhoods across the country. The plot
shows a strong positive correlation.
Shawna recalls that correlation does not imply causation. In this example,
Shawna sees that increasing the number of cars per household would not
cause members of her community to purchase health insurance.
Identify the lurking variable that is causing an increase in both the number of
cars owned and the average number of citizens with health insurance.
The number of cars on the road
Average mileage per vehicle
Average income per household
The number of citizens in the United States
RATIONALE
Recall that a lurking variable is something that must be related to the outcome and
explanatory variable that when considered can help explain a relationship between 2
variables. Since higher income is positively related to owning more cars and having
health insurance, this variable would help explain why we see this association.
CONCEPT
Correlation and Causation
4
Which of the following scatterplots shows an outlier in both the x-
and y-direction?
,
RATIONALE
