Linear Regression and Multiple Regression MCQs
Linear Regression and Multiple Regression MCQs
These Linear Regression and Multiple Regression multiple-choice questions and their answers will help you strengthen your grip on the subject of Linear Regression and Multiple Regression. You can prepare for an upcoming exam or job interview with these 20+ Linear Regression and Multiple Regression MCQs.
So scroll down and start answering.
1: A statistical procedure used to test hypotheses for one or more predictor variables to determine whether the equation for a sample of data points can be used to predict values of the criterion variable (Y) given values of the predictor variable (X) in the population is known as_____
A. Analysis of Regression
B. Analysis of Prediction
C. Analysis of description
D. None of these
2: Criterion Variable is also known as _____
A. Predictor Variable
B. Known Variable
C. To-be-Predicted Variable
D. All of these
3: _____ can be used to predict values of another variable.
A. Known Variable
B. Predictor Variable
C. To-be-predicted Variable
D. Both a and b
4: Linear Regression is used _____
A. To determine the equation of a regression line
B. To determine the extent to which the regression equation can be used
C. Both
D. None
5: Method of Least Squares is a statistical procedure used to compute the slope (b) and_____ of the best-fitting straight line to a set of data points.
A. Y intercept
B. X intercept
C. Both
D. Mid point
6: Multiple Regression is a statistical procedure that includes two or more _____variables.
A. Known Variables
B. Predictor Variables
C. Criterion Variables
D. Both a and b
7: _____ can be used to predict values of another variable.
A. Known Variable
B. Predictor Variable
C. To-be-predicted Variable
D. Both a and b
8: Regression is a statistical procedure used to determine the equation of a regression line to a set of data points and the extent to which the regression equation can be used to predict values of one factor, given unknown values of a second factor in a population.
A. True
B. False
9: The closer the data points fall to the regression line, the _____ the value of regression variation.
A. Smaller
B. Larger
C. Unchanged
D. All of these
10: The variance in Y that is related to changes in X is known as Residual Variation.
A. True
B. False
11: When X and Y change in the same direction, the slope is _____
A. Positive
B. Negative
C. Unchanged
D. Any of these
12: The Standard Error of Estimate equals the _____ of the mean square residual.
A. Square
B. Mean
C. Sum
D. Square root
13: The variable with unknown values that can be predicted or estimated, given known values of the predictor variable is called the to-be-predicted variable.
A. True
B. False
14: Y-Intercept is the value of the criterion variable (Y) when the predictor variable (X) equals _____
A. 0.1
B. 1
C. 10
D. 0
15: A researcher measures the extent to which mother’s age predicts their first baby’s gestational term. Which factor is the criterion variable in this example?
A. Mother’s age
B. Baby’s gestational term
C. Both mother’s age and baby’s gestational term
D. None of these
16: A researcher measures the extent to which the amount of caffeine intake predicts speed on cognitive tasks. Which factor is the predictor variable in this example?
A. Caffeine intake
B. Speed on cognitive tasks
C. Both caffeine intake and speed on cognitive tasks
D. All of these
17: The regression equation uses the method of ________ to determine the best-fitting straight line to a set of data points.
A. Graphing
B. Guessing
C. Approximation
D. Least squares
18: If b = −0.57, MX= 5.25, and MY = 2.75 for a set of data points, then what is the value of the y-intercept for the best-fitting linear equation?
A. 6.82
B. 11.68
C. −0.24
D. −5.74
19: If the coefficient of determination is 0.45 and the sum of squares regression for an analysis of regression is 180, then what is the value of SSY?
A. 180
B. 300
C. 400
D. 550
20: A researcher computes an analysis of regression in which r = .49. What is the value of the coefficient of determination in this example?
A. 49
B. 24
C. 76
D. There is not enough information to answer this question
21: A statistical method that uses two or more predictor variable(s) in the equation of the regression line is called:
A. Linear correlation.
B. Linear analysis of variance.
C. Multiple correlation.
D. Multiple regression.
22: A researcher is interested in predicting the effect of restrained eating and ruminative coping on depression—which statistical analysis would be appropriate for their study?
A. Multiple regression
B. Plotting
C. Correlation
D. Coefficient testin
23: Which of the following is not a step to evaluate the significance of the relative contribution of each factor:
A. Find r2 for the “other” predictor variable
B. Complete the F table and make a decision
C. Identify SS accounted for by the predictor variable of interest
D. Compute the chi-square statistic
24: If t = 1.43 for the relative contribution of one factor, then what is this value when converted to an F statistic?
A. 2.04
B. 1.43
C. 4.16
D. The conversion is not possible.
25: The regression line is the line that makes the value of SS the smallest.
A. True
B. False
26: The y-intercept of a straight line is the value of the criterion variable (Y) when the predictor variable (X) = 1.
A. True
B. False
27: In an analysis of regression there are two sources of variation: regression, and residual or error.
A. True
B. False
28: Multiple regression can be used to measure predictive variability for only two predictor variables.
A. True
B. False
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Linear Regression and Multiple Regression MCQs
