MKTG 372 EXAM 2 OLE MISS QUESTIONS AND
ANSWERS 2026
Forecasting - Answers -Predicting future events
One of the most important business functions as decisions are based on a forecast of
the future
Goal: Generate good forecasts on the average over time and keep errors low
Forecasting is an ongoing process
You can use forecasts to make planning decisions about: - Answers -Customer orders.
Inventory.
Delivery of goods.
Work load.
Capacity requirements.
Warehouse space.
Labor.
Equipment.
Budgets.
Development of new products.
Work force requirements.
Principles of Forecasting: - Answers -Common features include:
1. Forecasts are rarely perfect
2. Forecasts are more accurate for grouped data than for individual items
3. Forecast are more accurate for shorter than longer time periods
Quantitative Methods: - Answers -Time Series Models:
Assumes information needed to generate a forecast is contained in a time series of
data
Assumes the future will follow same patterns as the past
Causal Models or Associative Models:
Explores cause-and- effect relationships
Uses leading indicators to predict the future
Housing starts and appliance sales
Time Series Models: - Answers -Forecaster looks for data patterns as
Data = historic pattern + random variation
Historic pattern to be forecasted:
Level (long-term average) - data fluctuates around a
constant mean
, Trend - data exhibits an increasing or decreasing pattern
Seasonality - any pattern that regularly repeats itself and is of a constant length
Cycle - patterns created by economic fluctuations
Random Variation cannot be predicted
Time Series Models: General Form - Answers -Data=
level+trend+seasonality+cycles+random variation
or: (pattern)+ random variation
Observation of Moving Average: - Answers -A smaller n makes the forecast more
responsive; however, the forecast is also more subject to the random changes in the
data. A larger n makes the forecast more stable.
Forecasting Seasonality: - Answers -Remember it is a regularly repeating pattern
Examples:
University enrollment varies between quarters or semesters; higher in the fall than in
the summer
Seasonal Index:
Percentage amount by which data for each season
are above or below the mean.
Forecasting Seasonality Steps: - Answers -1.Calculate the average demand per
season
E.g.: average quarterly demand
2.Calculate a seasonal index for each season
of each year:
Divide the actual demand of each season by the average demand per season for that
year
3.Average the indexes by season
E.g.: take the average of all Spring indexes, then of all Summer indexes, ...
4.Forecast total demand for the next year using any of the forecasting models & divide
by the number of seasons
Use regular forecasting method & divide by 4 for average quarterly demand
ANSWERS 2026
Forecasting - Answers -Predicting future events
One of the most important business functions as decisions are based on a forecast of
the future
Goal: Generate good forecasts on the average over time and keep errors low
Forecasting is an ongoing process
You can use forecasts to make planning decisions about: - Answers -Customer orders.
Inventory.
Delivery of goods.
Work load.
Capacity requirements.
Warehouse space.
Labor.
Equipment.
Budgets.
Development of new products.
Work force requirements.
Principles of Forecasting: - Answers -Common features include:
1. Forecasts are rarely perfect
2. Forecasts are more accurate for grouped data than for individual items
3. Forecast are more accurate for shorter than longer time periods
Quantitative Methods: - Answers -Time Series Models:
Assumes information needed to generate a forecast is contained in a time series of
data
Assumes the future will follow same patterns as the past
Causal Models or Associative Models:
Explores cause-and- effect relationships
Uses leading indicators to predict the future
Housing starts and appliance sales
Time Series Models: - Answers -Forecaster looks for data patterns as
Data = historic pattern + random variation
Historic pattern to be forecasted:
Level (long-term average) - data fluctuates around a
constant mean
, Trend - data exhibits an increasing or decreasing pattern
Seasonality - any pattern that regularly repeats itself and is of a constant length
Cycle - patterns created by economic fluctuations
Random Variation cannot be predicted
Time Series Models: General Form - Answers -Data=
level+trend+seasonality+cycles+random variation
or: (pattern)+ random variation
Observation of Moving Average: - Answers -A smaller n makes the forecast more
responsive; however, the forecast is also more subject to the random changes in the
data. A larger n makes the forecast more stable.
Forecasting Seasonality: - Answers -Remember it is a regularly repeating pattern
Examples:
University enrollment varies between quarters or semesters; higher in the fall than in
the summer
Seasonal Index:
Percentage amount by which data for each season
are above or below the mean.
Forecasting Seasonality Steps: - Answers -1.Calculate the average demand per
season
E.g.: average quarterly demand
2.Calculate a seasonal index for each season
of each year:
Divide the actual demand of each season by the average demand per season for that
year
3.Average the indexes by season
E.g.: take the average of all Spring indexes, then of all Summer indexes, ...
4.Forecast total demand for the next year using any of the forecasting models & divide
by the number of seasons
Use regular forecasting method & divide by 4 for average quarterly demand
