Holding the inflation surprise variable constant, the coefficient on GDP growth of 1.85 is best interpreted as meaning that:
A. a one-unit increase in GDP growth is associated with an increase of 1.85 units in fund excess return, on average
B. GDP growth explains 1.85% of the variation in fund excess return
C. a one-unit increase in GDP growth causes fund excess return to increase by exactly 1.85 units
D. the correlation between GDP growth and fund excess return is 1.85
Source: CFA Program Curriculum, Quantitative Methods, "Multiple Regression" -- Interpreting coefficients in a multiple regression model (https://www.cfainstitute.org/insights/professional-learning/refresher-readings)
Based on the regression output, is the coefficient on the inflation surprise variable statistically significant at the 5% level?
A. Yes, because its t-statistic of -1.90 is more extreme than the critical value of -2.00
B. No, because its t-statistic of -1.90 is less extreme in absolute value than the critical value of 2.00
C. Yes, because its coefficient is negative, which is always significant in a regression
D. No, because its standard error of 0.50 exceeds its coefficient of -0.95
Source: CFA Program Curriculum, Quantitative Methods, "Multiple Regression" -- Hypothesis testing of individual regression coefficients (https://www.cfainstitute.org/insights/professional-learning/refresher-readings)
Which of the following is closest to the F-statistic for testing the null hypothesis that all slope coefficients are jointly equal to zero, and what does it imply?
A. F = 20.64; reject the null hypothesis, so the regression has significant explanatory power overall
B. F = 2.98; fail to reject the null hypothesis
C. F = 0.42; reject the null hypothesis
D. F = 1.85; fail to reject the null hypothesis
Source: CFA Program Curriculum, Quantitative Methods, "Multiple Regression" -- F-test of overall regression significance (https://www.cfainstitute.org/insights/professional-learning/refresher-readings)
Shah is concerned that the regression residuals may exhibit heteroskedasticity. If present but left uncorrected, heteroskedasticity most directly causes which of the following problems?
A. Biased and inconsistent regression coefficients
B. Incorrect standard errors, which can lead to invalid t-statistics and hypothesis tests
C. An R-squared value that is always overstated
D. A violation of the assumption that the independent variables are measured without error
Source: CFA Program Curriculum, Quantitative Methods, "Multiple Regression" -- Violations of regression assumptions: heteroskedasticity (https://www.cfainstitute.org/insights/professional-learning/refresher-readings)
Using the auxiliary regression R-squared of 0.81, the variance inflation factor (VIF) for the debt-to-EBITDA variable is closest to:
A. 0.81
B. 1.23
C. 5.26
D. 8.10
Source: CFA Program Curriculum, Quantitative Methods, "Multiple Regression" -- Detecting multicollinearity using variance inflation factors (https://www.cfainstitute.org/insights/professional-learning/refresher-readings)
