# ISDS 3115

A naive forecast for September sales of a product would be equal to the forecast for August.
FALSE
The forecasting time horizon and the forecasting techniques used to vary over the life cycle of a product.
TRUE
Demand (sales) forecasts serve as inputs to financial, marketing, and personnel planning.
TRUE
Forecasts of individual products tend to be more accurate than forecasts of product families.
FALSE
Most forecasting techniques assume that there is some underlying stability in the system.
TRUE
The sales force composite forecasting method relies on salespersons’ estimates of expected sales.
TRUE
A time-series model uses a series of past data points to make the forecast
TRUE
The quarterly “make meeting” of Lexus dealers is an example of a sales force composite forecast.
TRUE
Cycles and random variation are both components of time series.
TRUE
A naive forecast for September sales of a product would be equal to the sales in August.
TRUE
One advantage of exponential smoothing is the limited amount of record keeping involved.
TRUE
The larger the number of periods in the simple moving average forecasting method, the greater the method’s responsiveness to changes in demand.
FALSE
Forecast including trend is an exponential smoothing technique that utilizes two smoothing constants: one for the average level of the forecast and one for its trend.
TRUE
Mean squared error and Coefficient of Correlation are two measures of the overall error of a forecasting model.
FALSE
In trend projection, the trend component is the slope of the regression equation.
TRUE
In trend projection, a negative regression slope is mathematically impossible.
FALSE
Seasonal indexes adjust raw data for patterns that repeat at regular time intervals.
TRUE
If a quarterly seasonal index has been calculated at 1.55 for the October-December quarter, then raw data for that quarter must be multiplied by 1.55 so that the quarter can be fairly compare to other quarters.
FALSE
The best way to forecast a business cycle is by finding a leading variable.
TRUE
Linear regression analysis is a straight-line mathematical model to describe the functional relationships between independent and dependent variables.
TRUE
The larger the standard error of the estimate, the more accurate the forecasting model.
FALSE
A trend projection equation with a slope of 0.78 means that there is a 0.78 unit rise in Y for every unit of time that passes.
TRUE
In a regression equation where Y is demand and X is advertising, a coefficient of determination (r squared) of .70 means that 70% of the variance in advertising is explained by demand.
FALSE
Demand cycles for individual products can be driven by product life cycles.
TRUE
If a forecast is consistently greater than (or less than) actual values, the forecast is said to be biased.
TRUE
Focus forecasting tries a variety of computer models and selects the best one for a particular application.
TRUE
Many service firms use point-of-sale computers to collect detailed records needed for accurate short-term forecasts.
TRUE
What two numbers are contained in the daily report to the CEO of Walt Disney Parks & Resorts regarding the six Orlando parks?
yesterday’s forecasted attendance and yesterday’s actual attendance
Using an exponential smoothing modeal with smoothing constant alpha= .20, how much weight would be assigned to the 2nd most recent period?
.16
Forecasts:
are rarely perfect
One use of short-range forecasts is to determine
job assignments
Forecasts are usually classified by time horizon into three categories:
short-range, medium-range, and long-range
A forecast with a time horizon of about 3 months to 3 years is typically called a
medium-range forecast
Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a…
long-range time horizon
The three major types of forecasts used by business organizations are:
economic, technological, and demand
Which of the following is not a step in the forecasting process?
eliminate any assumptions
The two general approaches to forecasting are:
qualitative and quantitative
Which of the following uses three types of participants: decision makers, staff personnel, and respondents?
the delphi method
The forecasting model that pools the opinions of a group of experts or managers is known as the:
jury of executive opinion modeal
Which of the following is NOT a type of qualitative forecasting?
moving average
Which of the following techniques uses variables such as price and promotional expenditures, which are related to product demand, to predict demand?
Associative models
Which of the following statements about time series forecasting is true?
The analysis of past demand helps predict future demand.
Time series data may exhibit which of the following behaviors?
They may exhibit all of the above.
Gradual, long-term movement in time series data is called
trends
Which of the following is not present in a time series?
operational variations
The fundamental difference between cycles and seasonality is the:
duration of the repeating patterns
In time series, which of the following cannot be predicted?
random fluctuation
What is the approximate forecast for May using a four-month moving average?
Nov=39, Dec=36, Jan=40, Feb=42, Mar=48, April=46?
44
What time series model below assumes that demand in the next period will be equal to the most recent period’s demand?
naive approach
John’s House of Pancakes uses a weighted moving average method to forecast pancake sales. It assigns a weight of 5 to the previous month’s demand, 3 to demand two months ago, and 1 to demand three months ago. If sales amounted to 1000 pancakes in May, 2200 pancakes in June, and 3000 pancakes in July, what should be the forecast for August?
2511
A six-month moving average forecast is better than a three-month moving average forecast if demand:
is rather stable
Increasing the number of periods in a moving average will accomplish greater smoothing, but at the expense of:
responsiveness to changes
Which of the following statements comparing the weighted moving average technique and exponential smoothing is true:
Exponential smoothing typically requires less record keeping of past data.
Which time series model uses past forecasts and past demand data to generate a new forecast?
exponential smoothing
Which is NOT a characteristic of exponential smoothing?
weights each historical value equally
Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?
1.0
Given an actual demand of 103, a previous forecast value of 99, and an alpha of 0.4, the exponential smoothing forecast for the next period would be:
100.6
A forecast based on the previous forecast plus a percentage of the forecast error is an:
exponentially smoothed forecast
Given an actual demand of 61, a previous forecast of an alpha=0.3, what would the forecast for the next period be using simple exponential smoothing?
58.9
Which of the following values of alpha would cause exponential smoothing to respond the most slowly to forecast errors?
0.10
(the lowest number)
The primary purpose of the mean absolute deviation (MAD) in forecasting is to:
measure forecast accuracy
Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
4
The last four months of sales were 8, 10, 15, and 9 units. The last four forecast 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is:
3.5
A time series trend equation is 25.3 + 2.1 X. what is your forecast for period 7?
40.0
For a given product demand, the time series trend equation is 53 – 4X. The negative sign on the slope of the equation:
is an indication that product demand is declining
Yamaha manufacturers which set of products with complementary demands to address seasonal fluctuations:
jet skis and snowmobiles
Which of the following is TRUE regarding two smoothing constants of the Forecast Including Trend (FIT) model?
Their values are determined independently
Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?
1000 units
A seasonal index for a monthly series is about to be calculated on the basis of three years’ accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is:
0.684
A fundamental distinction between trend projection and linear regression is that :
In trend projection the independent variable is time; in linear regression the independent variable need not be time, but can be any variable with explanatory power.
The percent of variation in the dependent variable that is explained by the regression equation is measured by the:
coefficient of determination
The degree or strength of a linear relationship is shown by the
correlation coefficient
If two variables were perfectly correlated, the correlation coefficient r would equal:
b or c
(-1 and 1)
The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate:
bias
The tracking signal is the