2020年CFA考试《CFA二级》考试共240题，分为单选题。小编为您整理Quantitative Methods5道练习题，附答案解析，供您备考练习。

1、In response to Question 4, the 95 percent confidence interval for the regression coefficient for the default spread is to:【单选题】

A.0.13 to 5.95.

B.1.72 to 4.36.

C.1.93 to 4.15.

正确答案:B

答案解析:B is correct. The confidence interval is computed as a1 ± s(a1) × t(95%,∞). From Exhibit 1, a1 = 3.04 and t(a1) = 4.52, resulting in a standard error of a1 = s(a1) = 3.04/4.52 = 0.673. The critical value for t from Exhibit 3 is 1.96 for p = 0.025. The confidence interval for a1 is 3.04 ± 0.673 × 1.96 = 3.04 ± 1.31908 or from 1.72092 to 4.35908.?

2、In order to perform the nonstationarity test, Busse should transform the Regression 1 equation by:【单选题】

A.subtracting the independent variable from both sides of the equation.

正确答案:A

答案解析:C is correct. To conduct the Dickey–Fuller test, one must subtract the independent variable, x – 1) but not a change in the independent variable.

3、Based on the regression output in Exhibit 3 and sales data in Exhibit 4, the forecasted value of quarterly sales for March 2016 for PoweredUP is closest to:【单选题】

A.$4.193 billion.

B.$4.231 billion.

正确答案:B

答案解析:C is correct. The quarterly sales for March 2016 is calculated as follows:

4、In response to Gupta’s question about predicting High Tech’s return, Garfield’s prediction (in decimal form) will be closest to:【单选题】

A.0.06118.

B.0.04333.

C.0.06297.

正确答案:C

答案解析:Calculate a predicted value for the dependent variable, given an estimated regression model and a value for the independent variable.

5、Suppose that you deleted several of the observations that had small residual values. If you re-estimated the regression equation using this reduced sample, what would likely happen to the standard error of the estimate and the R-squared?【单选题】

A.Standard Error of the Estimate: Decrease; R-Squared: Decrease

B.Standard Error of the Estimate: Decrease; R-Squared: Increase

C.Standard Error of the Estimate: Increase; R-Squared: Decrease

正确答案:C

答案解析:C is correct. Deleting observations with small residuals will degrade the strength of the regression, resulting in an increase in the standard error and a decrease in R-squared.