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NEW QUESTION: 1
Omen Company는 남성용 셔츠 제조업체입니다. 주간 영업 보고서를 각 영업 관리자에게 배포합니다. 수량 2R5는 영업 관리자 중 하나의 주간 판매 보고서에서 품목 중 하나에 대한 판매 수량 열에 표시되었습니다. 발생한 일에 대한 가장 가능성있는 설명은 다음과 같습니다.
A. 실행 중에 컴퓨터가 오작동했습니다.
B. 프로그램에 입력 데이터에 대한 데이터 검사 루틴이 없습니다.
C. 출력 수량이 16 진수로 표시되었습니다.
D. 프린터가 고장 났으며 "R"은 소수점이어야 합니다.
Answer: B
Explanation:
The probable explanation for reporting a quantity using a character other than a digit is that the data were incorrectly encoded and the computer program did not perform a field check, which would have detected the error. A field check tests whether a field consists of the proper characters, whether JI alphabetic, numeric, special, or combinations thereof.

NEW QUESTION: 2
Which IPS feature models worm behavior and correlates the specific time between events, network behavior and multiple exploit behavior to more accurately identify and stop worms?
A. Traffic normalization
B. Risk Rating
C. Meta Event Generator
D. Security Device Event Exchange support
Answer: C

NEW QUESTION: 3
George Armor, CFA, is a new stock analyst for Pedad Investments. One tool that Pedad uses to compare stock valuations is the dividend discount model (DOM). In particular, the firm evaluates stocks in terms of
"justified" multiples of sales and book value. These multiples are based on algebraic manipulation of the DDM. Over time, these multiples seem to provide a good check on the market valuation of a stock relative to the company's fundamentals. Any stock which is currently priced below its value based on a justified multiple of sales or book value is considered attractive for purchase by Pedad portfolio managers. Exhibit 1 contains financial information from the year just ended for three stable companies in the meat-packing industry: Able Corp, Baker, Inc., and Charles Company, from which Armor will derive his valuation estimates.

One of Pedad's other equity analysts, Marie Swift, CFA, recently held a meeting with Armor to discuss a relatively new model the firm is implementing to determine the P/E ratios of companies that Pedad researches. Swift explains that the model utilizes a cross-sectional regression using the previous year-end data of a group of comparable companies' P/E ratios against their dividend payout ratios (r), sustainable growth rates (g), and returns on equity (ROE). The resulting regression equation is used to determine a predicted P/E ratio for the subject company using the subject company's most recent year-end data. Swift has developed the following model, which has an R-squared of 81%, for the meat packing industry (16 companies):
Predicted P/E = 2.74 + 8.21(r) + 14.21(g) + 2.81(ROE)
(STD error) (2.11) (6.52) (9.24) (2.10)
After Swift presents the model to Armor, she points out that models of this nature are subject to limitations.
In particular, multicollinearity, which appears to be present in the meat packing industry model, can create great difficulty in interpreting the effects of the individual coefficients of the model. Swift continues by stating that in spite of this limitation, models of this nature generally have known and significant predictive power across different time periods although not across different stocks.
Evaluate Swift's comments regarding multicollinearity and predictive power. Which of the following comments is correct?
A. Only the comment about multicollinearity is correct.
B. Only the comment about predictive power is correct.
C. Both comments are correct.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Swift has correctly stated that if multicollinearity is present in a model, the interpretation of the individual regression coefficients becomes problematic. The existence of multicollinearity is generally signaled by a high R-squared value and low f-statistics on the regression coefficients. The r-stat for the coefficients for r, g, and ROE can be calculated as (8.21 / 6.52) = 1.26, (14.21 / 9.24) = 1.54, and (2.81 / 2.10) = 1.34, respectively. Note that all of these f-stats are well below the approximate critical value of 2, indicating they are statistically insignificant. With the high R-squared of 81% and insignificant f-stats, it appears that multicollinearity is indeed present in this model. Swift's comment regarding predictive power is incorrect.
Cross-sectional regressions have unknown predictive power outside the specific sample and time period used to generate the regression. (Study Session 12, LOS 42.k)

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