Q5
Quiz by Dmitry Kislitsyn
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- Q1
In analyzing the relationship between two variables, a scatter plot cannot be used to detect which of the following?
A curvilinear relationship
A negative linear relationship
A causal relationship
A positive linear relationship
30s - Q2
If the population correlation between two variables is determined to be -0.70, which of the following is known to be true?
There is a fairly strong negative linear relationship between the two variables.
There is a positive linear relationship between the two variables.
The scatter diagram for the two variables will be upward sloping from left to right.
An increase in one of the variables will cause the other variable to decline by 70 percent.
30s - Q3
If a pair of variables have a strong curvilinear relationship, which of the following is true?
A scatter plot will not be needed to indicate that curvature is present
The correlation coefficient will be able to indicate that curvature is present.
The correlation coefficient will not be able to indicate the relationship is curved.
The correlation coefficient will be equal to zero.
30s - Q4
Which of the following statements is correct?
Two variables that are uncorrelated with one another may still be related in a nonlinear manner.
Correlation implies causation.
The stronger the linear relationship between two variables, the closer the correlation coefficient will be to 1.0.
A scatter plot showing two variables with a positive linear relationship will have all points on a straight line.
30s - Q5
Assume that a medical research study found a correlation of -0.73 between consumption of vitamin A and the cancer rate of a particular type of cancer. This could be interpreted to mean:
The more vitamin A consumed, the lower a person's chances are of getting this type of cancer.
The less vitamin A consumed, the lower a person's chances are of getting this type of cancer.
The more vitamin A consumed, the higher a person's chances are of getting this type of cancer.
Vitamin A causes this type of cancer
30s - Q6
The term that is given when two variables are correlated but there is no apparent connection between them is:
Spurious correlation
Random correlation
Spontaneous correlation
Linear correlation
30s - Q7
What is the range of possible values for the correlation coefficient?
-1 to 1
-∞ to ∞
1 to ∞
0 to 1
30s - Q8
If the correlation coefficient is zero, what does it mean?
There is a perfect positive relationship between the two variables
None of the above
There is no relationship between the two variables
There is a perfect negative relationship between the two variables
30s - Q9
Which of the following statements is true about covariance?
It is always negative
It can be positive or negative
It is always zero
It is always positive
30s - Q10
What is the difference between correlation and covariance?
Correlation measures the strength of the linear relationship between two variables, while covariance measures the strength of the non-linear relationship between two variables.
Correlation can be interpreted as a measure of both the strength and direction of the linear relationship between two variables, while covariance is usually used only to indicate the direction of the relationship.
Correlation measures the strength of the association between two variables, while covariance measures the direction of the relationship between two variables.
Correlation and covariance are interchangeable terms for measuring the same thing.
30s - Q11
What is a residual in regression analysis?
The difference between the mean value and the actual value of the dependent variable
The difference between the predicted value and the actual value of the dependent variable
The difference between the mean value and the actual value of the independent variable
The difference between the predicted value and the actual value of the independent variable
30s - Q12
How is covariance calculated?
By multiplying the standard deviation of one variable by the standard deviation of another variable
By subtracting the mean of one variable from the mean of another variable
By adding the variances of two variables together
By dividing the sum of the products of deviations from the means by the sample size minus one
30s - Q13
Can covariance be used to infer causation?
Sometimes, depending on the context and other factors.
It is unclear whether covariance can be used to infer causation without additional information.
Yes, a high covariance indicates that one variable causes another variable.
No, covariance only measures the strength of a linear relationship, not causation.
30s - Q14
Which of the following is an example of spurious correlation?
The correlation between height and weight in a sample of adults
The correlation between age and income in a sample of workers
The correlation between ice cream sales and crime rates
The correlation between GPA and SAT scores
30s - Q15
How can confounding variables affect the correlation between two variables?
By creating a false correlation
By reversing the direction of the correlation
All of the above
By reducing the strength of the correlation
30s