Introduction to Correlation and Regression MCQs
Introduction to Correlation and Regression MCQs
The following Introduction to Correlation and Regression MCQs have been compiled by our experts through research, in order to test your knowledge of the subject of Introduction to Correlation and Regression. We encourage you to answer these 20 multiple-choice questions to assess your proficiency.
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1: All are true about archival data except_____
A. Data that have already been collected
B. Experimenter only has statistical power
C. Experimenter has both statistical and experimental power
D. Experimenter can no longer change the original experimental design
2: Causal relationships should never be inferred from a correlational design or a correlational test statistic, although correlational relationships may suggest causal relationships.
A. True
B. False
3: Chi-square distribution is a theoretical sampling distribution that can be used to_____
A. Test the significance of a phi correlation
B. Test nonparametric frequency data
C. Test difference of variance
D. Both a and b
4: Coefficient of determination is obtained by squaring the _____ coefficient.
A. Phi
B. Pearson’s
C. Spearman
D. None of these
5: A constant is a _____ value in a statistical formula.
A. Common
B. Fixed
C. Nonspecified
D. All of these
6: Correlation is the _____ between two variables.
A. Degree of freedom
B. Strength of a relationship
C. Dependance
D. None of these
7: An assumption that a curved line best fits the graphic representation between two variables is known as _____
A. Linear relationship
B. Curved relationship
C. Curvilinear relationship
D. None of these
8: Degrees of freedom is a parameter that is equal to the _____ of observations or groups in a study minus some value(s) that limits the observations’ or groups’ freedom to vary.
A. Number
B. Sum of numbers
C. Square of numbers
D. Sum of square of numbers
9: All are true about heteroscedasticity except_____
A. Violation of the homoscedasticity assumption
B. The bivariate distribution has greater variance for some values of the y scores
C. Linear regression distributions have similar variances
D. Linear regression distributions have different variances
10: Heteroscedasticity is a violation of homoscedasticity.
A. True
B. False
11: Least squares method is a method in regression that produces the single best-fitting line that predicts a _____
A. Y score given an X score
B. X score given a Y score
C. Both X and Y scores
D. None of these
12: Linear relationship is a relationship in which a straight line best fits the bivariate distribution of two _____
A. Confounding variables
B. Dependent variables
C. Discrete variables
D. All of these
13: Multiple regression analysis is a statistical procedure that measures the strength of a relationship between multiple independent variables and _____
A. Single dependent variable
B. Single independent variable
C. Multiple dependent variables
D. Multiple independent variables
14: A measure of the strength of a relationship between two continuous variables is known as_____
A. Pearson’s product–moment correlation
B. Phi correlation
C. Point-biserial correlation
D. None of these
15: Phi Correlation is a measure of the strength of a relationship between_____
A. A continuous and a dichotomous variable
B. Two continuous variables
C. Two dichotomous variables
D. None of these
16: Point-Biserial Correlation is a measure of the strength of a relationship between_____
A. A continuous and a dichotomous variable
B. Two continuous variables
C. Two dichotomous variables
D. None of these
17: Regression analysis is a statistical procedure that measures the strength of a relationship between _____
A. An independent variable and a dependent variable
B. Two dependent variables
C. Two independent variables
D. Two continuous variables
18: Scatterplot is a graphic representation of the relationship of two _____ in correlational designs.
A. Dependent Variables
B. Confounding Variables
C. Continuous Variables
D. Independent Variables
19: A statistical procedure where the focus is on the way one variable (y) varies based on how one other variable (x) varies is known as simple regression.
A. True
B. False
20: A measure of the relationship between two ordinal rankings of the same set of data is known as_____
A. Pearson’s product–moment correlation
B. Phi correlation
C. Point-biserial correlation
D. Spearman’s Correlation
21: Spearman’s Correlation is represented by the coefficient _____
A. R
B. Rs
C. Φ
D. Rpb
22: A relationship in which a high score on variable x will be associated with a high score on variable y is known as Strong Negative Relationship.
A. True
B. False
23: A relationship in which a low score on variable x will be associated with a low score on variable y is known as Strong Positive Relationship.
A. True
B. False
24: T distribution is a table of critical values that can be used to test the significance of_____
A. Correlation coefficient
B. Independent t test
C. Dependent t test
D. All of these
25: The correlation in which variable x may be related to y or the variable y may be related to x is known as_____
A. Pearson’s product–moment correlation
B. Zero-order correlation
C. Point-biserial correlation
D. Spearman’s Correlation
26: A relationship in which one factor can be said to be the cause of another is called as “chance is lumpy”
A. True
B. False
27: A ______ shows two variables combined as individual points on a graph.
A. Positive correlation
B. A positive correlation between amount of sunlight and plant growth.
C. A positive correlation between studying and grades
D. Negative correlation
28: Based on the regression equation, we can _______________.
A. Predict the value of the dependent variable given a value of the independent variable
B. Predict the value of the independent variable given a value of the dependent variable
C. Predict the value slope of the line
D. Measure the association between two variables
29: Just because there is a correlation between a and b __________.
A. Is the same as the correlation between B and A.
B. At least one output and one input, but the output obviously is insufficient to generate the input shown
C. At least one input and one output, but the input obviously is insufficient to generate the output shown
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Introduction to Correlation and Regression MCQs
