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CMA- GLEIM MCQ - Part 1 (2nd Batch)

Mark Anthony Pusing 100 preguntas 1.399 visitas

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Preguntas del test (100)

  1. 1[101] Gleim #: 1.3.101 -- Source: CMA 0408 1-147
    Martin Fabricating uses a cumulative average-time learning curve model to monitor labor costs.
    Data regarding two recently completed batches of a part that is used in tractor-trailer rigs is as
    follows:
    Batch Number Cumulative Average
    Number of Units Hours Per Unit
    1 50 20
    2 50 16
    If the same rate of learning continues for the next several batches produced, which of the
    following best describes (1) the type (i.e., degree) of learning curve that the firm is
    experiencing and (2) the average hours per unit for units included in the 201-400 range of units
    produced (i.e., the last 200 units)?
    Type (Degree) of Average Hours Per
    Learning Curve Unit for Units 201-400
    A. 20% 10.24
    B. 80% 10.24
    C. 80% 7.68
    D. 20% 3.84
    1. A20% | 10.24
    2. B80% 10.24
    3. C80% 7.68
    4. D20% 3.84
  2. 2[102] Gleim #: 1.3.102 -- Source: CMA 0408 1-150
    A manufacturing company required 800 direct labor hours to produce the first lot of four units
    of a new motor. Management believes that a 90% learning curve will be experienced over four
    lots of production. How many direct labor hours will be required to manufacture the next 12
    units?
    1. A1,792
    2. B1,944
    3. C2,016
    4. D2,160
  3. 3[103] Gleim #: 1.4.103 -- Source: CMA 1294 3-12
    Which one of the following is a sales forecasting technique that can be utilized in preparing the
    annual profit plan?
    1. ALinear programming.
    2. BExponential smoothing
    3. CQueuing theory
    4. DProgram Evaluation and Review Technique (PERT).
  4. 4[104] Gleim #: 1.4.104 -- Source: CMA 1293 4-25
    The four components of time series data are secular trend, cyclical variation, seasonality, and
    random variation. The seasonality in the data can be removed by
    1. AMultiplying the data by a seasonality factor.
    2. BIgnoring it.
    3. CTaking the weighted average over four time periods
    4. DSubtracting a seasonality factor from the data
  5. 5105] Gleim #: 1.4.105 -- Source: CMA 0205
    A forecasting technique that is a combination of the last forecast and the last observed value is
    called
    1. ADelphi
    2. BLeast squares.
    3. CRegression
    4. DExponential smoothing
  6. 6[106] Gleim #: 1.4.106 -- Source: CIA 594 II-38
    As part of a risk analysis, an auditor wishes to forecast the percentage growth in next month’s
    sales for a particular plant using the past 30 months’ sales results. Significant changes in the
    organization affecting sales volumes were made within the last 9 months. The most effective
    analysis technique to use would be
    1. AUnweighted moving average
    2. BExponential smoothing.
    3. CQueuing theory.
    4. DLinear regression analysis
  7. 7[107] Gleim #: 1.4.107 -- Source: CIA 1189 III-50
    What are the four components of a time series?
    1. ATrend, cyclical, seasonal, and irregular
    2. BAlpha, cyclical, seasonal, and irregular
    3. CAlpha, cyclical, seasonal, and repetitive
    4. DTrend, cyclical, seasonal, and repetitive
  8. 8[108] Gleim #: 1.4.108 -- Source: CIA 589 III-50
    The moving-average method of forecasting
    1. AIs a cross-sectional forecasting method
    2. BRegresses the variable of interest on a related variable to develop a forecast
    3. CDerives final forecasts by adjusting the initial forecast based on the smoothing constant
    4. DIncludes each new observation in the average as it becomes available and discards the
      oldest observation.
  9. 9[109] Gleim #: 1.4.109 -- Source: Publisher
    Violation of which assumption underlying regression analysis is prevalent in time series
    analysis?
    1. AVariance of error term is constant.
    2. BError terms are independent.
    3. CDistribution of error terms is usually normal
    4. DExpected value of error term equals zero
  10. 10[110] Gleim #: 1.4.110 -- Source: CMA 0408 1-152
    Sales of big-screen televisions have grown steadily during the past five years. A dealer
    predicted that the demand for February would be 148 televisions. Actual demand in February
    was 158 televisions. If the smoothing constant is α = 0.3, the demand forecast for March, using
    the exponential smoothing model, will be
    1. A148 televisions
    2. B151 televisions
    3. C155 televisions
    4. D158 televisions

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Gleim CMA Test Prep: Part 1: Financial Planning, Performance, and Control (1337 questions)
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