Quantitative Analysis for Management 11th Edition by Barry Render - Test Bank

Quantitative Analysis for Management 11th Edition by Barry Render – Test Bank

 

Instant Download – Complete Test Bank With Answers

 

 

Sample Questions Are Posted Below

 

 

Quantitative Analysis for Management, 11e (Render)

Chapter 5   Forecasting

 

1) A medium-term forecast typically covers a two- to four-year time horizon.

Answer:  FALSE

Diff: 2

Topic:  INTRODUCTION

 

2) Regression is always a superior forecasting method to exponential smoothing, so regression should be used whenever the appropriate software is available.

Answer:  FALSE

Diff: 1

Topic:  INTRODUCTION

 

3) The three categories of forecasting models are time series, quantitative, and qualitative.

Answer:  FALSE

Diff: 2

Topic:  TYPES OF FORECASTS

 

4) Time-series models attempt to predict the future by using historical data.

Answer:  TRUE

Diff: 2

Topic:  TYPES OF FORECASTS

 

5) Time-series models rely on judgment in an attempt to incorporate qualitative or subjective factors into the forecasting model.

Answer:  FALSE

Diff: 1

Topic:  TYPES OF FORECASTS

 

6) A moving average forecasting method is a causal forecasting method.

Answer:  FALSE

Diff: 2

Topic:  TYPES OF FORECASTS

 

7) An exponential forecasting method is a time-series forecasting method.

Answer:  TRUE

Diff: 2

Topic:  TYPES OF FORECASTS

 

8) A trend-projection forecasting method is a causal forecasting method.

Answer:  FALSE

Diff: 2

Topic:  TYPES OF FORECASTS

 

 

9) Qualitative models produce forecasts that are little better than simple guesses or coin tosses.

Answer:  FALSE

Diff: 1

Topic:  TYPES OF FORECASTS

10) The most common quantitative causal model is regression analysis.

Answer:  TRUE

Diff: 2

Topic:  TYPES OF FORECASTS

 

11) Qualitative models attempt to incorporate judgmental or subjective factors into the forecasting model.

Answer:  TRUE

Diff: 1

Topic:  TYPES OF FORECASTS

 

12) A scatter diagram is useful to determine if a relationship exists between two variables.

Answer:  TRUE

Diff: 1

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

13) The Delphi method solicits input from customers or potential customers regarding their future purchasing plans.

Answer:  FALSE

Diff: 2

Topic:  TYPES OF FORECASTS

 

14) The naïve forecast for the next period is the actual value observed in the current period.

Answer:  TRUE

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

15) Mean absolute deviation (MAD) is simply the sum of forecast errors.

Answer:  FALSE

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

16) Time-series models enable the forecaster to include specific representations of various qualitative and quantitative factors.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

17) Four components of time series are trend, moving average, exponential smoothing, and seasonality.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

 

18) The fewer the periods over which one takes a moving average, the more accurately the resulting forecast mirrors the actual data of the most recent time periods.

Answer:  TRUE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

19) In a weighted moving average, the weights assigned must sum to 1.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

20) A scatter diagram for a time series may be plotted on a two-dimensional graph with the horizontal axis representing the variable to be forecast (such as sales).

Answer:  FALSE

Diff: 2

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

21) Scatter diagrams can be useful in spotting trends or cycles in data over time.

Answer:  TRUE

Diff: 1

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

22) Exponential smoothing cannot be used for data with a trend.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

23) In a second order exponential smoothing, a low β gives less weight to more recent trends.

Answer:  TRUE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

24) An advantage of exponential smoothing over a simple moving average is that exponential smoothing requires one to retain less data.

Answer:  TRUE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Reflective Thinking

 

25) When the smoothing constant α = 0, the exponential smoothing model is equivalent to the naïve forecasting model.

Answer:  FALSE

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

26) A seasonal index must be between -1 and +1.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

27) A seasonal index of 1 means that the season is average.

Answer:  TRUE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

28) The process of isolating linear trend and seasonal factors to develop a more accurate forecast is called regression.

Answer:  FALSE

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

29) When the smoothing constant α = 1, the exponential smoothing model is equivalent to the naïve forecasting model.

Answer:  TRUE

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

30) Adaptive smoothing is analogous to exponential smoothing where the coefficients α and β are periodically updated to improve the forecast.

Answer:  TRUE

Diff: 2

Topic:  MONITORING AND CONTROLLING FORECASTS

 

31) Bias is the average error of a forecast model.

Answer:  TRUE

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

32) Which of the following is not classified as a qualitative forecasting model?

1. A) exponential smoothing
2. B) Delphi method
3. C) jury of executive opinion
4. D) sales force composite
5. E) consumer market survey

Answer:  A

Diff: 1

Topic:  TYPES OF FORECASTS

 

33) A judgmental forecasting technique that uses decision makers, staff personnel, and respondent to determine a forecast is called

1. A) exponential smoothing.
2. B) the Delphi method.
3. C) jury of executive opinion.
4. D) sales force composite.
5. E) consumer market survey.

Answer:  B

Diff: 2

Topic:  TYPES OF FORECASTS

34) Which of the following is considered a causal method of forecasting?

1. A) exponential smoothing
2. B) moving average
3. C) Holt’s method
4. D) Delphi method
5. E) None of the above

Answer:  E

Diff: 2

Topic:  TYPES OF FORECASTS

 

35) A graphical plot with sales on the Y axis and time on the X axis is a

1. A) catter diagram.
2. B) trend projection.
3. C) radar chart.
4. D) line graph.
5. E) bar chart.

Answer:  A

Diff: 2

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

36) Which of the following statements about scatter diagrams is true?

1. A) Time is always plotted on the y-
2. B) It can depict the relationship among three variables simultaneously.
3. C) It is helpful when forecasting with qualitative data.
4. D) The variable to be forecasted is placed on the y-
5. E) It is not a good tool for understanding time-series data.

Answer:  D

Diff: 2

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

37) Which of the following is a technique used to determine forecasting accuracy?

1. A) exponential smoothing
2. B) moving average
3. C) regression
4. D) Delphi method
5. E) mean absolute percent error

Answer:  E

Diff: 1

Topic:  MEASURES OF FORECAST ACCURACY

 

38) A medium-term forecast is considered to cover what length of time?

1. A) 2-4 weeks
2. B) 1 month to 1 year
3. C) 2-4 years
4. D) 5-10 years
5. E) 20 years

Answer:  B

Diff: 2

Topic:  INTRODUCTION

39) When is the exponential smoothing model equivalent to the naïve forecasting model?

1. A) α = 0
2. B) α =5
3. C) α = 1
4. D) during the first period in which it is used
5. E) never

Answer:  C

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

40) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130.  Suppose a one-semester moving average was used to forecast enrollment (this is sometimes referred to as a naïve forecast).  Thus, the forecast for the second semester would be 120, for the third semester it would be 126, and for the last semester it would be 110. What would the MSE be for this situation?

196. A) 196.00
197. B) 230.67
198. C) 100.00
199. D) 42.00
200. E) None of the above

Answer:  B

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Analytic Skills

 

41) Which of the following methods tells whether the forecast tends to be too high or too low?

1. A) MAD
2. B) MSE
3. C) MAPE
4. D) decomposition
5. E) bias

Answer:  E

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

42) Assume that you have tried three different forecasting models.  For the first, the MAD = 2.5, for the second, the MSE = 10.5, and for the third, the MAPE = 2.7.  We can then say:

1. A) the third method is the best.
2. B) the second method is the best.
3. C) methods one and three are preferable to method two.
4. D) method two is least preferred.
5. E) None of the above

Answer:  E

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

43) Which of the following methods gives an indication of the percentage of forecast error?

1. A) MAD
2. B) MSE
3. C) MAPE
4. D) decomposition
5. E) bias

Answer:  C

Diff: 1

Topic:  MEASURES OF FORECAST ACCURACY

 

44) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent).  Forecast sales for the next day using a two-day moving average.

1. A) 14
2. B) 13
3. C) 15
4. D) 28
5. E) 12.5

Answer:  A

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

45) As one increases the number of periods used in the calculation of a moving average,

1. A) greater emphasis is placed on more recent data.
2. B) less emphasis is placed on more recent data.
3. C) the emphasis placed on more recent data remains the same.
4. D) it requires a computer to automate the calculations.
5. E) one is usually looking for a long-term prediction.

Answer:  B

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Reflective Thinking

 

46) Enrollment in a particular class for the last four semesters has been 122, 128, 100, and 155 (listed from oldest to most recent).  The best forecast of enrollment next semester, based on a three-semester moving average, would be

116. A) 116.7.
117. B) 126.3.
118. C) 168.3.
119. D) 135.0.
120. E) 127.7.

Answer:  E

Diff: 1

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

47) Which of the following methods produces a particularly stiff penalty in periods with large forecast errors?

1. A) MAD
2. B) MSE
3. C) MAPE
4. D) decomposition
5. E) bias

Answer:  B

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Reflective Thinking

 

48) Sales for boxes of Girl Scout cookies over a 4-month period were forecasted as follows: 100, 120, 115, and 123. The actual results over the 4-month period were as follows: 110, 114, 119, 115. What was the MAD of the 4-month forecast?

1. A) 0
2. B) 5
3. C) 7
4. D) 108
5. E) None of the above

Answer:  C

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Analytic Skills

 

49) Sales for boxes of Girl Scout cookies over a 4-month period were forecasted as follows: 100, 120, 115, and 123. The actual results over the 4-month period were as follows: 110, 114, 119, 115. What was the MSE of the 4-month forecast?

1. A) 0
2. B) 5
3. C) 7
4. D) 108
5. E) None of the above

Answer:  E

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Analytic Skills

 

50) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent).  Forecast sales for the next day using a three-day weighted moving average where the weights are 3, 1, and 1 (the highest weight is for the most recent number).

12. A) 12.8
13. B) 13.0
14. C) 70.0
15. D) 14.0
16. E) None of the above

Answer:  D

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

51) Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent).  Forecast sales for the next day using a two-day weighted moving average where the weights are 3 and 1 are

14. A) 14.5.
15. B) 13.5.
16. C) 14.
17. D) 12.25.
18. E) 12.75.

Answer:  A

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

52) Which of the following is not considered to be one of the components of a time series?

1. A) trend
2. B) seasonality
3. C) variance
4. D) cycles
5. E) random variations

Answer:  C

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

53) Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130 (listed from oldest to most recent).  Develop a forecast of enrollment next semester using exponential smoothing with an alpha = 0.2.  Assume that an initial forecast for the first semester was 120 (so the forecast and the actual were the same).

118. A) 118.96
119. B) 121.17
120. C) 130
121. D) 120
122. E) None of the above

Answer:  B

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

54) Demand for soccer balls at a new sporting goods store is forecasted using the following regression equation: Y = 98 + 2.2X where X is the number of months that the store has been in existence. Let April be represented by X = 4. April is assumed to have a seasonality index of 1.15. What is the forecast for soccer ball demand for the month of April (rounded to the nearest integer)?

1. A) 123
2. B) 107
3. C) 100
4. D) 115
5. E) None of the above

Answer:  B

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

55) A time-series forecasting model in which the forecast for the next period is the actual value for the current period is the

1. A) Delphi model.
2. B) Holt’s model.
3. C) naïve model.
4. D) exponential smoothing model.
5. E) weighted moving average.

Answer:  C

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

56) In picking the smoothing constant for an exponential smoothing model, we should look for a value that

1. A) produces a nice-looking curve.
2. B) equals the utility level that matches with our degree of risk aversion.
3. C) produces values which compare well with actual values based on a standard measure of error.
4. D) causes the least computational effort.
5. E) None of the above

Answer:  C

Diff: 1

Topic:  TIME-SERIES FORECASTING MODELS

 

57) In the exponential smoothing with trend adjustment forecasting method, is the

1. A) slope of the trend line.
2. B) new forecast.
3. C) Y-axis intercept.
4. D) independent variable.
5. E) trend smoothing constant.

Answer:  E

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

58) The computer monitoring of tracking signals and self-adjustment is referred to as

1. A) exponential smoothing.
2. B) adaptive smoothing.
3. C) trend projections.
4. D) trend smoothing.
5. E) running sum of forecast errors (RFSE).

Answer:  B

Diff: 2

Topic:  MONITORING AND CONTROLLING FORECASTS

 

 

59) Which of the following is not a characteristic of trend projections?

1. A) The variable being predicted is the Y
2. B) Time is the X
3. C) It is useful for predicting the value of one variable based on time trend.
4. D) A negative intercept term always implies that the dependent variable is decreasing over time.
5. E) They are often developed using linear regression.

Answer:  D

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

60) When both trend and seasonal components are present in time series, which of the following is most appropriate?

1. A) the use of centered moving averages
2. B) the use of moving averages
3. C) the use of simple exponential smoothing
4. D) the use of weighted moving averages
5. E) the use of double smoothing

Answer:  A

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

61) A tracking signal was calculated for a particular set of demand forecasts.  This tracking signal was positive.  This would indicate that

1. A) demand is greater than the forecast.
2. B) demand is less than the forecast.
3. C) demand is equal to the forecast.
4. D) the MAD is negative.
5. E) None of the above

Answer:  A

Diff: 2

Topic:  MONITORING AND CONTROLLING FORECASTS

 

62) A seasonal index of ________ indicates that the season is average.

1. A) 10
2. B) 100
3. C) 0.5
4. D) 0
5. E) 1

Answer:  E

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

 

63) The errors in a particular forecast are as follows: 4, -3, 2, 5, -1. What is the tracking signal of the forecast?

1. A) 0.4286
2. B) 2.3333
3. C) 5
4. D) 1.4
5. E) 2.5

Answer:  B

Diff: 3

Topic:  MONITORING AND CONTROLLING FORECASTS

AACSB:  Analytic Skills

64) 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, 2, 8, 10 (last week).

(a)           Forecast demand for the next week using a two-week moving average.

(b)           Forecast demand for the next week using a three-week moving average.

Answer:

(a)  (8 + 10)/2 = 9

(b)  (2 + 8 + 10)/3 = 6.67

Diff: 1

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

65) Daily high temperatures in the city of Houston for the last week have been:  93, 94, 93, 95, 92, 86, 98 (yesterday).

(a)           Forecast the high temperature today using a three-day moving average.

(b)           Forecast the high temperature today using a two-day moving average.

(c)           Calculate the mean absolute deviation based on a two-day moving average, covering all days in which you can have a forecast and an actual temperature.

Answer:

(a)           (92 + 86 + 98)/3 = 92

(b)           (86 + 98)/2 = 92

(c)           MAD = (  +  +  +  +  ) / 5 = 20.5 / 5 = 4.1

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

 

66) For the data below:

 

Month	Automobile Battery Sales	Month	Automobile Battery Sales
January	20	July	17
February	21	August	18
March	15	September	20
April	14	October	20
May	13	November	21
June	16	December	23

 

(a)           Develop a scatter diagram.

(b)           Develop a three-month moving average.

(c)           Compute MAD.

Answer:

(a)           scatter diagram

 

 

 

 

(b)

Month	Automobile Battery Sales	3-Month Moving Avg.	Absolute Deviation
January	20	–	–
February	21	–	–
March	15	–	–
April	14	18.67	4.67
May	13	16.67	3.67
June	16	14	2
July	17	14.33	2.67
August	18	15.33	3.67
September	20	17	3
October	20	18.33	1.67
November	21	19.33	1.67
December	23	20.33	2.67
January	–	21.33	–

 

(c)           MAD = 2.85

Diff: 3

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

67) For the data below:

 

Month	Automobile Tire Sales	Month	Automobile Tire Sales
January	80	July	68
February	84	August	100
March	60	September	80
April	56	October	80
May	52	November	84
June	64	December	92

 

(a)           Develop a scatter diagram.

(b)           Compute a three-month moving average.

(c)           Compute the MSE.

 

Answer:  (a)        scatter diagram

 

 

 

(b)

Month	Automobile Tire Sales	3-Month Tire Average	Squared Error
January	80	–	–
February	84	–	–
March	60	–	–
April	56	74.7	349.69
May	52	66.7	216.09
June	64	56.0	64
July	68	57.3	114.49
August	100	61.3	1497.69
September	80	77.3	7.29
October	80	82.7	7.29
November	84	86.7	7.29
December	92	81.3	114.49
January	–	85.33

 

(c)           MSE = 264.26

Diff: 3

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

 

68) For the data below:

 

Year	Automobile Sales	Year	Automobile Sales
1990	116	1977	119
1991	105	1998	34
1992	29	1999	34
1993	59	2000	48
1994	108	2001	53
1995	94	2002	65
1996	27	2003	111

 

(a)           Develop a scatter diagram.

(b)           Develop a six-year moving average forecast.

(c)           Find MAPE.

Answer:

(a)           scatter diagram

 

 

 

 

(b)

Year	Number of Automobiles	Forecast	Error	Error Actual
1990	116	X
1991	105	X
1992	29	X
1993	59	X
1994	108	X
1995	94	X
1996	27	85.2	-58.2	2.15
1977	119	70.3	48.7	0.41
1998	34	72.7	-38.7	1.14
1999	34	73.5	-39.5	1.16
2000	48	69.3	-21.3	0.44
2001	53	59.3	-6.3	0.12
2002	65	52.5	12.5	0.19
2003	111	58.8	52.2	0.47

 

(c)           MAPE = .76 ∗ 100% = 76%

Diff: 3

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

69) Use simple exponential smoothing with α = 0.3 to forecast battery sales for February through May.  Assume that the forecast for January was for 22 batteries.

 

 

Month	Automobile Battery Sales
January	42
February	33
March	28
April	59

 

Answer:  Forecasts for February through May are: 28, 29.5, 29.05, and 38.035.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

70) 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: $30,000, $32,000, $34,500, and $36,000 (last graduating class).  Predict the starting salary for the next graduating class using a simple exponential smoothing model with α = 0.25.  Assume that the initial forecast was $30,000 (so that the forecast and the actual were the same).

Answer:  Forecast for next period = $32,625

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

71) Use simple exponential smoothing with α = 0.33 to forecast the tire sales for February through May.  Assume that the forecast for January was for 22 sets of tires.

 

Month	Automobile Battery Sales
January	28
February	21
March	39
April	34

 

Answer:  Forecast for Feb. through May = 23.98, 22.9966, 28.2777, and 30.1661

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

72) The following table represents the new members that have been acquired by a fitness center.

 

Month	New members
Jan	45
Feb	60
March	57
April	65

 

Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of 0 for January, use exponential smoothing with trend adjustment to come up with a forecast for May on new members.

Answer:

Month	New members	Ft	Tt	FITt
Jan	45	40	0	40
Feb	60	41.5	0.6	42.1
March	57	47.47	2.748	50.218
April	65	52.2526	3.56184	55.81444
May		58.57011	4.664107	63.23422

 

May forecast = 58.57

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

73) The following table represents the number of applicants at popular private college in the last four years.

 

Month	New members
2007	10,067
2008	10,940
2009	11,116
2010	10,999

 

Assuming α = 0.2, β  = 0.3, an initial forecast of 10,000 for 2007, and an initial trend adjustment of 0 for 2007, use exponential smoothing with trend adjustment to come up with a forecast for 2011 on the number of applicants.

Answer:

Month	# of applicants	Ft	Tt	FITt
2007	10,067	10,000	0	10000
2008	10,940	10013.4	4.02	10017.42
2009	11,116	10201.94	59.3748	10261.31
2010	10,999	10432.25	110.6562	10542.9
2011		10634.12	138.0219	10772.15

 

2011 Forecast = 10,634

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

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

 

Time Period	Actual (A)	Forecast (F)
1	2	3	1
2	A2 = ?	4	–
3	6	5	1
4	4	6	2

 

Answer:  A2 = 3 or A2 = 5

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Analytic Skills

 

75) Calculate (a) MAD, (b) MSE, and (c) MAPE for the following forecast versus actual sales figures.  (Please round to four decimal places for MAPE.)

 

Forecast	Actual
100	95
110	108
120	123
130	130

 

Answer:

(a)           MAD = 10/4 = 2.5

(b)           MSE = 38/4 = 9.5

(c)           MAPE = (0.0956/4)100 = 2.39%

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

AACSB:  Analytic Skills

 

 

76) Use the sales data given below to determine:

 

Year	Sales (units)	Year	Sales (units)
1995	130	1999	169
1996	140	2000	182
1997	152	2001	194
1998	160	2002	?

 

(a)           the least squares trend line.

(b)           the predicted value for 2002 sales.

(c)           the MAD.

(d)           the unadjusted forecasting MSE.

Answer:

(a)           = 119.14 + 10.46X

(b)           119.14 + 10.46(8) = 202.82

(c)           MAD = 1.01

(d)           MSE = 1.71

Diff: 3

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

77) For the data below:

 

Year	Automobile Sales	Year	Automobile Sales
1990	116	1977	119
1991	105	1998	34
1992	29	1999	34
1993	59	2000	48
1994	108	2001	53
1995	94	2002	65
1996	27	2003	111

 

(a)           Determine the least squares regression line.

(b)           Determine the predicted value for 2004.

(c)           Determine the MAD.

(d)           Determine the unadjusted forecasting MSE.

Answer:

(a)           = 85.15 – 1.8X

(b)           85.15 – 1.8 (15) = 58.15

(c)           MAD = 30.09

(d)           MSE = 1,121.66

Diff: 3

Topic:  MEASURES OF FORECAST ACCURACY and TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

78) Given the following gasoline data:

 

Quarter	Year 1	Year 2
1	150	156
2	140	148
3	185	201
4	160	174

 

(a)           Compute the seasonal index for each quarter.

(b)           Suppose we expect year 3 to have annual demand of 800.  What is the forecast value for each quarter in year 3?

Answer:

(a)

 

Quarter	Year 1	Year 2	Average two- year demand	Quarterly demand	Average seasonal index
1	150	156	153	164.25	.932
2	140	148	144	164.25	.877
3	185	201	193	164.25	1.175
4	160	174	167	164.25	1.017

 

(b)

 

Quarter	Forecast
1	200 ∗ .932 = 186.00
2	200 ∗ .877 = 175.34
3	200 ∗ 1.175 = 235.01
4	200 ∗ 1.017 = 203.35

 

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

 

79) Given the following data and seasonal index:

 

 

(a)           Compute the seasonal index using only year 1 data.

(b)           Determine the deseasonalized demand values using year 2 data and year 1’s seasonal indices.

(c)           Determine the trend line on year 2’s deseasonalized data.

(d)           Forecast the sales for the first 3 months of year 3, adjusting for seasonality.

Answer:  (a) and (b)

 

 

 

(c)           y = 11.96 + .29X

(d)           Jan = [11.96 + .29 (13)] *.87 = 13.69

Feb = [11.96 + .29 (14)] *.67 = 12.18

Mar = [11.96 + .29 (15)] *.55 = 8.97

Diff: 3

Topic:  TIME-SERIES FORECASTING MODELS

AACSB:  Analytic Skills

 

80) The following table represents the actual vs. forecasted amount of new customers acquired by a major credit card company:

 

Month	Actual	Forecast
Jan	1024	1010
Feb	1057	1025
March	1049	1141
April	1069	1053
May	1065	1059

 

(a)           What is the tracking signal?

(b)           Based on the answer in part (a), comment on the accuracy of this forecast.

Answer:

Month	Actual	Forecast	Error	RSFE
Jan	1024	1010	14	14	14
Feb	1057	1025	32	46	32
March	1049	1141	-92	-46	92
April	1069	1053	16	-30	16
May	1065	1059	6	-24	6

 

(a)           RSFE/MAD = -24/32 = -0.75 MAD

(b)           The answer in part (a) indicates an accurate forecast, one where overall, the actual amount of new customers was slightly less than the forecast.

Diff: 3

Topic:  MONITORING AND CONTROLLING FORECASTS

AACSB:  Analytic Skills

 

81) What are the eight steps to forecasting?

Answer:  (1) Determine the use of the forecast–what objective are we trying to obtain? (2) Select the items or quantities that are to be forecasted. (3) Determine the time horizon of the forecast–is it 1 to 30 days (short term), 1 month to 1 year (medium term), or more than 1 year (long term)? (4) Select the forecasting model or models. (5) Gather the data needed to make the forecast, (6) Validate the forecasting model, (7) Make the forecast, and (8) Implement the results.

Diff: 3

Topic:  INTRODUCTION

 

82) In general terms, describe what causal forecasting models are.

Answer:  Causal forecasting models incorporate variables or factors that might influence the quantity being forecasted.

Diff: 2

Topic:  TYPES OF FORECASTS

 

83) In general terms, describe what qualitative forecasting models are.

Answer:  Qualitative forecasting models attempt to incorporate judgmental or subjective factors into the model.

Diff: 2

Topic:  TYPES OF FORECASTS

 

84) Briefly describe the structure of a scatter diagram for a time series.

Answer:  A scatter diagram for a time series may be plotted on a two-dimensional graph with the horizontal axis representing the time period, while the variable to be forecast (such as sales) is placed on the vertical axis.

Diff: 2

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

85) Briefly describe the jury of executive opinion forecasting method.

Answer:  The jury of executive opinion forecasting model uses the opinions of a small group of high-level managers, often in combination with statistical models, and results in a group estimate of demand.

Diff: 2

Topic:  TYPES OF FORECASTS

 

86) Briefly describe the consumer market survey forecasting method.

Answer:  It is a forecasting method that solicits input from customers or potential customers regarding their future purchasing plans.

Diff: 2

Topic:  TYPES OF FORECASTS

 

87) Describe the naïve forecasting method.

Answer:  The forecast for the next period is the actual value observed in the current period.

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

88) Briefly describe why the scatter diagram is helpful.

Answer:  Scatter diagrams show the relationships between model variables.

Diff: 1

Topic:  SCATTER DIAGRAMS AND TIME SERIES

 

89) Explain, briefly, why most forecasting error measures use either the absolute or the square of the error.

Answer:  A deviation is equally important whether it is above or below the actual. This also prevents negative errors from canceling positive errors that would understate the true size of the errors.

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

90) List four measures of historical forecasting errors.

Answer:  MAD, MSE, MAPE, and Bias

Diff: 2

Topic:  MEASURES OF FORECAST ACCURACY

 

91) In general terms, describe what time-series forecasting models are.

Answer:  forecasting models that make use of historical data

Diff: 1

Topic:  TYPES OF FORECASTS

 

92) List four components of time-series data.

Answer:  trend, seasonality, cycles, and random variations

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

93) Explain, briefly, why the larger number of periods included in a moving average forecast, the less well the forecast identifies rapid changes in the variable of interest.

Answer:  The larger the number of periods included in the moving average forecast, the less the average is changed by the addition or deletion of a single number.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

94) State the mathematical expression for exponential smoothing.

Answer:  Ft+1 = Ft + α(Yt – Ft)

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

95) Explain, briefly, why, in the exponential smoothing forecasting method, the larger the value of the smoothing constant, α, the better the forecast will be in allowing the user to see rapid changes in the variable of interest.

Answer:  The larger the value of α, the greater is the weight placed on the most recent values.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

96) In exponential smoothing, discuss the difference between α and β.

Answer:  α is a weight applied to adjust for the difference between last period actual and forecasted value. β is a trend smoothing constant.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

97) In general terms, describe the difference between a general linear regression model and a trend projection.

Answer:  A trend projection is a regression model where the independent variable is always time.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

98) In general terms, describe a centered moving average.

Answer:  An average of the values centered at a particular point in time.  This is used to compute seasonal indices when trend is present.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

99) The decomposition approach to forecasting (using trend and seasonal components) may be helpful when attempting to forecast a time-series.  Could an analogous approach be used in multiple regression analysis?  Explain briefly.

Answer:  An analogous approach would be possible using time as one independent variable and using a set of dummy variables to represent the seasons.

Diff: 2

Topic:  TIME-SERIES FORECASTING MODELS

 

100) What is one advantage of using causal models over time-series or qualitative models?

Answer:  Use of the causal model requires that the forecaster gain an understanding of the relationships, not merely the frequency of variation; i.e., the forecaster gains a greater understanding of the problem than the other methods.

Diff: 2

Topic:  TYPES OF FORECASTS

AACSB:  Reflective Thinking

 

101) Discuss the use of a tracking signal.

Answer:  A tracking signal measures how well predictions fit actual data. By setting tracking limits, a manager is signaled to reevaluate the forecasting method.

Diff: 2

Topic:  MONITORING AND CONTROLLING FORECASTS