The following table represents the number of applicants at a popular private college in the last four years.
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81) The following table represents the
number of applicants at a popular private college in the last four years.
Month
New
members
2007
10,067
2008
10,940
2009
11,116
2010
10,999
Assuming ? = 0.2, ? = 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.
82) 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)
.jpg”>
1
2
3
1
2
A2 = ?
4
–
3
6
5
1
4
4
6
2
83) 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
84) 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.
85) 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.
86) 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?
87) Given the following data and seasonal
index:
.jpg”>
(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.
88) Wick’s Ski Shop is looking to forecast
ski sales on a quarterly basis based on the historical data listed in the table
below:
.jpg”>
Use the steps to develop a forecast using
the decomposition method to answer the following questions:
(a) Using the CMAs, calculate the seasonal
indices for Q1, Q2, Q3, and Q4.
(b) Find the equation for the trend line
using deseasonalized data.
(c) Find the year 5 quarterly forecasts.
89) 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.
90) What is the basic additive
decomposition model (in regression terms)?
91) In general terms, describe what causal
forecasting models are.
92) In general terms, describe what
qualitative forecasting models are.
93) Briefly describe the structure of a
scatter diagram for a time series.
94) Briefly describe the jury of executive
opinion forecasting method.
95) Briefly describe the consumer market
survey forecasting method.
96) Describe the nave forecasting method.
Topic: MEASURES OF FORECAST ACCURACY
97) Briefly describe why the scatter
diagram is helpful.
98) Explain, briefly, why most forecasting
error measures use either the absolute or the square of the error.
99) List four measures of historical
forecasting errors.
100) In general terms, describe what TIME
SERIES forecasting models are.
101) List four components of TIME SERIES
data.
102) 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.
103) State the mathematical expression for
exponential smoothing.
104) Explain, briefly, why, in the
exponential smoothing forecasting method, the larger the value of the smoothing
constant, ?, the better the forecast will be in allowing the user to see rapid
changes in the variable of interest.
105) In exponential smoothing, discuss the
difference between ? and ?.
106) In general terms, describe the difference
between a general linear regression model and a trend projection.
107) In general terms, describe a centered
moving average.
108) 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.
109) List the steps to develop a forecast
using the decomposition method.
110) What is one advantage of using causal
models over TIME SERIES or qualitative models?
111) Discuss the use of a tracking signal.
81) The following table represents the
number of applicants at a popular private college in the last four years.MonthNew
members200710,067200810,940200911,116201010,999Assuming ? = 0.2, ? = 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.82) Given the following data, if MAD =
1.25, determine what the actual demand must have been in period 2 (A2).Time
PeriodActual
(A)Forecast
(F).jpg”>12312A2 = ?4-3651446283) Calculate (a) MAD, (b) MSE, and (c)
MAPE for the following forecast versus actual sales figures. (Please round to
four decimal places for MAPE.)ForecastActual1009511010812012313013084) Use the sales data given below to
determine:YearSales
(units)YearSales
(units)19951301999169199614020001821997152200119419981602002?(a) The least squares trend line.(b) The predicted value for 2002 sales.(c) The MAD.(d) The unadjusted forecasting MSE.85) For the data below:YearAutomobile
SalesYearAutomobile
Sales19901161977119199110519983419922919993419935920004819941082001531995942002651996272003111(a) Determine the least squares regression
line.(b) Determine the predicted value for
2004.(c) Determine the MAD.(d) Determine the unadjusted forecasting
MSE.86) Given the following gasoline data:Quarter
Year
1Year
21150156214014831852014160174(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?87) Given the following data and seasonal
index:.jpg”>(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.88) Wick’s Ski Shop is looking to forecast
ski sales on a quarterly basis based on the historical data listed in the table
below:.jpg”>Use the steps to develop a forecast using
the decomposition method to answer the following questions:(a) Using the CMAs, calculate the seasonal
indices for Q1, Q2, Q3, and Q4.(b) Find the equation for the trend line
using deseasonalized data.(c) Find the year 5 quarterly forecasts.89) The following table represents the
actual vs. forecasted amount of new customers acquired by a major credit card
company:MonthActualForecastJan10241010Feb10571025March10491141April10691053May10651059(a) What is the tracking signal?(b) Based on the answer in part (a),
comment on the accuracy of this forecast.90) What is the basic additive
decomposition model (in regression terms)?91) In general terms, describe what causal
forecasting models are.92) In general terms, describe what
qualitative forecasting models are.93) Briefly describe the structure of a
scatter diagram for a time series.94) Briefly describe the jury of executive
opinion forecasting method.95) Briefly describe the consumer market
survey forecasting method.96) Describe the nave forecasting method.Topic: MEASURES OF FORECAST ACCURACY97) Briefly describe why the scatter
diagram is helpful.98) Explain, briefly, why most forecasting
error measures use either the absolute or the square of the error.99) List four measures of historical
forecasting errors.100) In general terms, describe what TIME
SERIES forecasting models are.101) List four components of TIME SERIES
data.102) 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.103) State the mathematical expression for
exponential smoothing.104) Explain, briefly, why, in the
exponential smoothing forecasting method, the larger the value of the smoothing
constant, ?, the better the forecast will be in allowing the user to see rapid
changes in the variable of interest.105) In exponential smoothing, discuss the
difference between ? and ?.106) In general terms, describe the difference
between a general linear regression model and a trend projection.107) In general terms, describe a centered
moving average.108) 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.109) List the steps to develop a forecast
using the decomposition method.110) What is one advantage of using causal
models over TIME SERIES or qualitative models?111) Discuss the use of a tracking signal.
