A) Regression is a measure of how well correlation explains the relationship between two variables
B) The correlation coefficient always lies on the regression line
C) Correlation can indicate how well the regression line 'fits' the data
D) The regression line can be calculated using values for the mean and the correlation coefficient
Correct Answer
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Multiple Choice
A) Causality normally implies correlation
B) Correlation normally implies causality
C) There is never a relationship between causality and correlation
D) Correlation only occurs when there is causality
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Multiple Choice
A) 62.0%
B) 61.6%
C) 45.3%
D) 38.4%
Correct Answer
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Multiple Choice
A) As x increases, y will increase
B) As x increases, y will decrease
C) As x increases, y will remain unchanged
D) We cannot say what happens as x increases
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Multiple Choice
A) Marketing budget and Sales revenue
B) Years of experience and Salary
C) Volume of raw materials purchased and Unit cost
D) Car speed and Stopping distance
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Multiple Choice
A) 85.0%
B) 72.3%
C) 56.3%
D) 27.8%
Correct Answer
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Multiple Choice
A) Rank correlation is sometimes used to approximate product moment correlation
B) If one variable is non-numeric the rank correlation is most appropriate
C) Rank correlation can be used if variables can be placed in order (e.g. of size)
D) Rank correlation is usually considered to be more accurate than product moment correlation
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Multiple Choice
A) -0.94
B) 0
C) 0.36
D) 1.2
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Multiple Choice
A) Correlated differences
B) Explained differences
C) Unexplained differences
D) Regression differences
Correct Answer
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Multiple Choice
A) An estimate of strength of a relationship between two variables
B) An estimate of the spread of values in a set of data
C) An estimate of skewness in a set of data
D) An estimate of causality between two variables
Correct Answer
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