BUS5112 Quantitative Research Methods Unit 7 Graded Quiz — 25
Q&A Verified Answers 2025
Course:
BUS5112 Quantitative Research Methods — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Graded Quiz Solutions — 25 Q&A with Verified Answers
BUS5112 Unit 7 Graded Quiz — Regression and Multivariate
Analysis
25 Questions with Verified Answers | Score: 96/100
Question 1:
What is the primary difference between simple linear regression and multiple regression?
A) Simple regression uses one independent variable, while multiple regression uses two or more.
B) Simple regression predicts a categorical variable, while multiple predicts continuous.
C) Simple regression handles non-linear relationships, while multiple handles linear.
D) Simple regression requires normally distributed independent variables, while multiple does not.
Answer: A
Explanation:
Simple linear regression uses only one independent variable to predict the dependent variable (A).
Multiple regression expands on this by using two or more independent variables. Options B, C,
and D are incorrect because both types predict continuous outcomes, assume linearity, and
require normally distributed residuals rather than independent variables.
Question 2:
In the context of multiple regression, what does the coefficient of determination (R²) represent?
, A) The correlation between the independent variables.
B) The average error in predicting the dependent variable.
C) The proportion of variance in the dependent variable explained by the independent variables.
D) The statistical significance of the overall regression model.
Answer: C
Explanation:
R-squared (the coefficient of determination) measures the goodness of fit by representing the
proportion of variance in the dependent variable explained by the independent variables (C).
Option A describes correlation, B describes the standard error of the estimate, and D refers to the
F-test.
Question 3:
Why is the Adjusted R-squared preferred over the standard R-squared when evaluating multiple
regression models with many predictors?
A) It standardizes the coefficients to allow for direct comparison between variables.
B) It penalizes the model for adding independent variables that do not significantly improve the
prediction.
C) It automatically removes variables with high multicollinearity.
D) It ensures that the residuals of the model are normally distributed.
Answer: B
Explanation:
As you add more variables to a model, standard R-squared will mechanically increase even if the
variables are useless. Adjusted R-squared accounts for this by penalizing the model for adding
independent variables that do not contribute significantly to predictive power (B). Standardizing
coefficients (A) is for betas, and it doesn't automatically remove variables (C) or fix normality (D).
Question 4:
A marketing manager runs a regression to predict sales based on advertising spend (measured in
thousands of dollars) and store size (measured in square feet). To determine which variable has a
stronger relative impact on sales, which metric should they examine?
A) Unstandardized coefficients (B)
B) The intercept
C) Variance Inflation Factor (VIF)
Q&A Verified Answers 2025
Course:
BUS5112 Quantitative Research Methods — University of the People (UoPeople)
Level:
MBA
Year:
2025/2026
Format:
Graded Quiz Solutions — 25 Q&A with Verified Answers
BUS5112 Unit 7 Graded Quiz — Regression and Multivariate
Analysis
25 Questions with Verified Answers | Score: 96/100
Question 1:
What is the primary difference between simple linear regression and multiple regression?
A) Simple regression uses one independent variable, while multiple regression uses two or more.
B) Simple regression predicts a categorical variable, while multiple predicts continuous.
C) Simple regression handles non-linear relationships, while multiple handles linear.
D) Simple regression requires normally distributed independent variables, while multiple does not.
Answer: A
Explanation:
Simple linear regression uses only one independent variable to predict the dependent variable (A).
Multiple regression expands on this by using two or more independent variables. Options B, C,
and D are incorrect because both types predict continuous outcomes, assume linearity, and
require normally distributed residuals rather than independent variables.
Question 2:
In the context of multiple regression, what does the coefficient of determination (R²) represent?
, A) The correlation between the independent variables.
B) The average error in predicting the dependent variable.
C) The proportion of variance in the dependent variable explained by the independent variables.
D) The statistical significance of the overall regression model.
Answer: C
Explanation:
R-squared (the coefficient of determination) measures the goodness of fit by representing the
proportion of variance in the dependent variable explained by the independent variables (C).
Option A describes correlation, B describes the standard error of the estimate, and D refers to the
F-test.
Question 3:
Why is the Adjusted R-squared preferred over the standard R-squared when evaluating multiple
regression models with many predictors?
A) It standardizes the coefficients to allow for direct comparison between variables.
B) It penalizes the model for adding independent variables that do not significantly improve the
prediction.
C) It automatically removes variables with high multicollinearity.
D) It ensures that the residuals of the model are normally distributed.
Answer: B
Explanation:
As you add more variables to a model, standard R-squared will mechanically increase even if the
variables are useless. Adjusted R-squared accounts for this by penalizing the model for adding
independent variables that do not contribute significantly to predictive power (B). Standardizing
coefficients (A) is for betas, and it doesn't automatically remove variables (C) or fix normality (D).
Question 4:
A marketing manager runs a regression to predict sales based on advertising spend (measured in
thousands of dollars) and store size (measured in square feet). To determine which variable has a
stronger relative impact on sales, which metric should they examine?
A) Unstandardized coefficients (B)
B) The intercept
C) Variance Inflation Factor (VIF)
