ECU OMGT 3223 Final Exam Study Guide | Business Decision Modeling Review, Forecasting,
Regression & Practice Questions
Quantitative Forecasts - ANS ✔✔Quantitative forecasts are usually the result of conventional
statistical analysis.
Used when situation is 'stable' and historical data exist.
Involve mathematical techniques.
Qualitative Forecasts - ANS ✔✔Qualitative forecasts are usually the result of experts' judgments
and opinions. Senior management, marketing,
purchasing, and engineering are usually sources for internal qualitative forecasts.
Used when the situation is 'vague' and little data exist.
Involve intuition and experience.
Delphi Method (Qualitative Forecasting technique) - ANS ✔✔Procedure for acquiring informed
judgments and opinions from knowledgeable individuals using a series of questionnaires in
order to develop a consensus forecast about what will occur in the future.
Sales-force composite - ANS ✔✔Procedure for developing forecasts where salespersons'
estimates of expected sales are combined or added together.
Time Series (Quantitative Forecasting method) - ANS ✔✔Statistical techniques that use
historical demand data to predict future demand.
Regression Methods - ANS ✔✔Techniques that develop a mathematical relationship between a
variable and the different factors that cause its behavior.
Time Frame - ANS ✔✔planning horizon.. short, mid, long range forecasts
, Short- to mid-range forecasts (Quant) - ANS ✔✔Usually encompass the immediate future
(Planning horizon: Daily up to a year)
Long-range forecasts (qual) - ANS ✔✔Usually encompass a period of time longer than a year.
Trends - ANS ✔✔Gradual long-term up or down movements of demand.
Seasonal Patterns - ANS ✔✔Up-and-down repetitive movements in demand occurring
periodically (Typically occur within 1 year)
Cycles - ANS ✔✔Repeating up and down movements (Typically 2-10 years duration).
Simple Moving average - ANS ✔✔(Sum of the data)/Number of periods
weighted moving average - ANS ✔✔(W*D)+(W2*D2)+...
exponential smoothing - ANS ✔✔Ft +1 = α Dt + (1 - α) Ft
where:
Ft +1 = forecast for next period
Dt = actual demand for present period
Ft = forecast for present period
α = weighting factor or smoothing constant
cumulative error - ANS ✔✔CE =sum(actual -forecaset)
where:
et = error for period t
Regression & Practice Questions
Quantitative Forecasts - ANS ✔✔Quantitative forecasts are usually the result of conventional
statistical analysis.
Used when situation is 'stable' and historical data exist.
Involve mathematical techniques.
Qualitative Forecasts - ANS ✔✔Qualitative forecasts are usually the result of experts' judgments
and opinions. Senior management, marketing,
purchasing, and engineering are usually sources for internal qualitative forecasts.
Used when the situation is 'vague' and little data exist.
Involve intuition and experience.
Delphi Method (Qualitative Forecasting technique) - ANS ✔✔Procedure for acquiring informed
judgments and opinions from knowledgeable individuals using a series of questionnaires in
order to develop a consensus forecast about what will occur in the future.
Sales-force composite - ANS ✔✔Procedure for developing forecasts where salespersons'
estimates of expected sales are combined or added together.
Time Series (Quantitative Forecasting method) - ANS ✔✔Statistical techniques that use
historical demand data to predict future demand.
Regression Methods - ANS ✔✔Techniques that develop a mathematical relationship between a
variable and the different factors that cause its behavior.
Time Frame - ANS ✔✔planning horizon.. short, mid, long range forecasts
, Short- to mid-range forecasts (Quant) - ANS ✔✔Usually encompass the immediate future
(Planning horizon: Daily up to a year)
Long-range forecasts (qual) - ANS ✔✔Usually encompass a period of time longer than a year.
Trends - ANS ✔✔Gradual long-term up or down movements of demand.
Seasonal Patterns - ANS ✔✔Up-and-down repetitive movements in demand occurring
periodically (Typically occur within 1 year)
Cycles - ANS ✔✔Repeating up and down movements (Typically 2-10 years duration).
Simple Moving average - ANS ✔✔(Sum of the data)/Number of periods
weighted moving average - ANS ✔✔(W*D)+(W2*D2)+...
exponential smoothing - ANS ✔✔Ft +1 = α Dt + (1 - α) Ft
where:
Ft +1 = forecast for next period
Dt = actual demand for present period
Ft = forecast for present period
α = weighting factor or smoothing constant
cumulative error - ANS ✔✔CE =sum(actual -forecaset)
where:
et = error for period t
