| 2008 CFA Level 2 － Mock Exam 2 （PM）模考试题 Q3 (part 1 - Part 6)
Question 3 John Rains, CFA, is a professor of finance at a large university located in the Eastern United States.
He is actively involved with his local chapter of the Society of
Financial Analysts. Recently, he was asked to teach one session of a
Society-sponsored CFA review course, specifically teaching the class
addressing the topic of quantitative analysis. Based upon his
familiarity with the CFA exam, he decides that the first part of the
session should be a review of the basic elements of quantitative
analysis, such as hypothesis testing, regression and multiple
regression analysis. He would like to devote the second half of the
review session to the practical application of the topics he covered in
the first half. Rains
decides to construct a sample regression analysis case study for his
students in order to demonstrate a “real-life” application of the
concepts. He begins by compiling financial information on a fictitious
company called Big Rig, Inc. According to the case study, Big Rig is
the primary producer of the equipment used in the exploration for and
drilling of new oil and gas wells in the United States.
Rains has based the information in the problem on an actual equity
holding in his personal portfolio, but has simplified the data for the
purposes of the review course. Rains
constructs a basic regression model for Big Rig in order to estimate
its profitability (in millions), using two independent variables: the
number of new wells drilled in the U.S. (WLS) and the number of new
competitors (COMP) entering the market: Profits = b0 + b1WLS – b2COMP + ε Based on the model, the estimated regression equation is: Profits = 22.5 + 0.98(WLS) − 0.35(COMP) Using the past 5 years of quarterly data, he calculated the following regression estimates for Big Rig, Inc:
| Coefficient
| Standard Error
| Intercept | 22.5 | 2.465 | WLS | 0.98 | 0.683 | COMP | 0.35 | 0.186 |
Part 1) Using the information presented, the t-statistic for the number of new competitors (COMP) coefficient is: A) 1.435. B) 9.128. C) 0.142. D) 1.882. Part 2) Rains
asks his students to test the null hypothesis that states for every new
well drilled, profits will be increased by the given multiple of the
coefficient, all other factors remaining constant. The appropriate
hypotheses for this two-tailed test can best be stated as: A) H0: b1 = 0.35 versus Ha: b1 ≠ 0.35. B) H0: b1 ≤ 0.35 versus Ha: b1 > 0.35. C) H0: b1 ≤ 0.98 versus Ha: b1 > 0.98. D) H0: b1 = 0.98 versus Ha: b1 ≠ 0.98.
Part 3) Continuing
with the analysis of Big Rig, Rains asks his students to calculate the
mean squared error(MSE). Assume that the sum of squared errors (SSE)
for the regression model is 359. A) 17.956. B) 19.927. C) 18.896. D) 21.118.
Part 4) Rains now wants to test the students’ knowledge of the use of the F-test and the interpretation of the F-statistic. Which of the following statements regarding the F-test and the F-statistic is the most correct? A) The
F-statistic is almost always formulated to test each independent
variable separately, in order to identify which variable is the most
statistically significant. B) The F-test is usually formulated as a two-tailed test. C) To
be considered statistically significant, the calculated F-statistic
must be equal to or less than the critical F-value at the appropriate
level of significance. D) The
F-statistic is used to test whether at least one independent variable
in a set of independent variables explains a significant portion of the
variation of the dependent variable.
Part 5) One
of the main assumptions of a multiple regression model is that the
variance of the residuals is constant across all observations in the
sample. A violation of the assumption is known as: A) positive serial correlation. B) robust standard errors. C) heteroskedasticity. D) negative serial correlation.
Part 6) Rains
reminds his students that a common condition that can distort the
results of a regression analysis is referred to as serial correlation.
The presence of serial correlation can be detected through the use of: A) the Breusch-Pagen test. B) the Durbin-Watson statistic. C) a discriminant model. D) the Hansen method.
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发表于 2008-6-6 21:55
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