# Stats chapter 7

## Quiz by Corey Klein

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8 questions
• Q1
The correlation r for the data in this scatterplot is?
r = .876
r = -.954
r = .995
r = . 657
30s
• Q2
The correlation r for the data in this scatterplot is?
r = -.852
r = - .647
r = -.452
r = .943
30s
• Q3
What is the correlation coeficient, r, for the scatterplot?
r = .365
r = .764
r = .046
r = .518
30s
• Q4
A local community college announces the correlation between college entrance exam grades and scholastic achievement was found to be -1.08. On the basis of this, you would tell the college that
students who do best on this exam will be poor students.
the exam is a poor predictor of success.
the entrance exam is a good predictor of success.
the college should hire a new statistician.
60s
• Q5
A study found correlation r = 0.61 between the sex of a worker and his or her income. You conclude that
women earn more than men on average.
this is nonsense because r makes no sense here.
there is a moderately strong, positive, linear relationship between sex and income.
women earn less than men on average.
60s
• Q6
If Data Set A of (x , y) data has correlation coefficient r = 0.65, and a second Data Set B has correlation r = -0.65, then
neither A nor B has a strong linear association.
A and B are actually the same data set.
the points in A exhibit a stronger linear association than B.
you can’t tell which data set has a stronger linear association without seeing the data or seeing the scatterplots.
60s
• Q7
Which of the following statements are correct? I. Two variables that are strongly associated will have a correlation near 1. II. Regression requires an explanatory-response relationship, while correlation does not. III. Even though the correlation between two variables may be high, in order to use the LSRL to predict, there needs to be an explanatory-response relationship between x and y.
I, II, and III
I and II only
none of these gives the complete set of true responses
I and III only
60s
• Q8
Suppose the correlation between two variables x and y is due to the fact that both are responding to changes in some unobserved third variable. What is this due to?
cause and effect between x and y
the effect of the Illuminati
extrapolation
the effect of a lurking variable
60s

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