Islamic university – Gaza mid exam

Faculty of commerce feasibility study

Accounting department 2008/2009 first term ( 1 hours )

Name :…………………………………………………………………………………………

Id : ………………………………………………. Class : ………………………………...

Multiple choice:

1. If one desires to make a prediction for a point in the distant future, the most appropriate technique is:

a.regression analysis.

b.exponential smoothing.

c.time-series.

d.multiple regression analysis.

e.None of the above

2.If the actual value for the month of January was 120, and the forecast made for January was 112, what would be the forecast for February if we used a simple exponential smoothing with an α value of 0.3?

  1. 114.4
  2. 109.6
  3. 122.4
  4. 112
  5. none of the above

3.The mean absolute deviation error (MAD) will always be:

  1. positive.
  2. between 0 and 1.
  3. negative.
  4. equal to the tracking signal
  5. None of the above

4. In picking an appropriate value for the smoothing constant (α) in a simple smoothing model, the objective is to______.

  1. isolate the seasonal component
  2. identify the regression slope
  3. obtain the most accurate forecast
  4. achieve the highest tracking signal possible
  5. none of the above

5. The process of isolating linear trend and seasonal factors to develop more accurate forecasts is called

  1. multiple regression.
  2. decomposition.
  3. linearization.
  4. multiplicative model
  5. none of the above

6. Given the following data, if MAD = 1.25, the actual demand in period 2 (A2) must have been

a.A2 = 3.

b.A2 = 5.

c.A2 = 4.5.

d.A2 = 3.5.

e.either (a) or (b).

7.Positive tracking signals indicate a tendency of demand to ______.

  1. display a downward trend
  2. be lagging behind the forecast
  3. be exactly the same as the forecast most of the time
  4. be greater than the forecast
  5. none of the above

8.Average starting salaries for students using a placement service at a university have been steadily increasing. A study of the last four graduating classes indicates the following average salaries: $20,000, $22,000, $23,000, and $25,000 (last graduating class).

Predict the starting salary for the next graduating class using an exponential smoothing model with = 0.2. Assume that the initial forecast was $20,000 (so that the forecast and the actual were the same).

a.$21,536

b.$21,736

c.$20,176

d.$21,936

e.None of the above

9.Demand for a particular type of battery fluctuates from one week to the next. A study of the last six weeks provides the following demands (in dozens): 4, 5, 3, 6, 7, 8 (last week).

Forecast demand during the next week using a two-week moving average.

  1. 8
  2. 7
  3. 7.5
  4. 6
  5. None of the above

10. The MAD for the following forecast versus actual sales figures is

  1. 5
  2. 2.5
  3. 3
  4. 4.5
  5. none of the above

11.Decomposition is

  1. a causal forecasting method.
  2. a sales force composite forecast method.
  3. a qualitative forecasting method.
  4. a time-series forecasting model.
  5. none of the above

12.Decomposition implies the disaggregation of a time series into:

  1. trend, cycles, and seasonal variations.
  2. cycles, average, and random variations.
  3. cycles, trend, random variations, and seasonal.
  4. any of the above
  5. none of the above.

1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 9 / 10 / 11 / 12

TRUE OR FALSE:

  1. Causal models are unable to establish a specific cause and effect connection between the dependent and independent variables.
  2. Only time-series models can employ historical data in developing a forecast.
  3. If you are going to try to develop a forecast, one of the best ways to start is by plotting whatever data you have.
  4. A time-series forecast is based upon data taken at regularly spaced intervals.
  5. One assumption when using moving average techniques is that the forecasted variable remains at a reasonably constant value.
  6. The more periods over which one takes a moving average, the less likely one is to miss an actual change in the data.
  7. The tracking signal is computed as the running sum of the forecast errors divided by the mean squared errors.
  8. Multiple regression is a good choice for forecasting when both trend and seasonal components are present in the data series.
  9. Simple smoothing is also referred to as first order smoothing .
  10. In setting limits within which a tracking signal is considered to imply that the forecast is "in control," we should look at both the nature of the data and the purpose of the forecast.
  11. Choosing appropriate weights for moving average forecasts is a somewhat arbitrary process.

1 / 2 / 3 / 4 / 5 / 6 / 7 / 8 / 9 / 10 / 11
QUESTION 3 : FIND UNKNOWN NUMER FROM 1 TO 3
YEAR / QUARTER / SALES / CMA / SEASONAL RATIO
1 / 1 / 218
2 / 247
3 / 243 / 250.875 / 0.968609865
4 / 292 / 252.625 / (2)
2 / 1 / 225 / (1) / 0.882352941
2 / 254 / 257.375 / 0.986886838
3 / 255 / 259.375 / 0.98313253
4 / 299 / 261.875 / 1.14176611
3 / 1 / 234 / 264.375 / 0.885106383
2 / 265 / 269 / 0.985130112
3 / 264 / 274.5 / 0.961748634
4 / 327 / 278.75 / 1.17309417
4 / 1 / 250 / 284.125 / 0.879894413
2 / 283 / 290.875 / 0.972926515
3 / 289
4 / 356
SEASONAL INDIX
QUARTER 1 / 0.88373
QUARTER 2 / (3)
QUARTER 3 / 0.972441
QUARTER 4 / 1.15743

CALCULATIONS:

1