Question 1

Forecast errors

Accepted Answer

A) are the difference in successive values of a time series 
B) are the differences between actual and forecast values 
C) should all be nonnegative 
D) should be summed to judge the goodness of a forecasting model 
A) are the difference in successive values of a time series 
B) are the differences between actual and forecast values 
C) should all be nonnegative 
D) should be summed to judge the goodness of a forecasting model B

Question 2

The number of pizzas ordered on Friday evenings between 5:30 and 6:30 at a pizza delivery location for the last 10 weeks is shown below.Use exponential smoothing with smoothing constants of .2 and .8 to forecast a value for week 11.Compare your forecasts using MSE.Which smoothing constant would you prefer?
58,46,55,39,42,63,54,55,61,52

Accepted Answer

FORECASTING WITH EXPONENTIAL SMOOTHING
************************************************
THE SMOOTHING CONSTANT IS 0.2 $$\begin{array} { c c c r } 
\text { Time Period } & \text { Actual Value } & \text { Forecast } & \text { Forecast Error } \
1 & & & \
2 & 46 & 58.00 & - 12.00 \
3 & 55 & 55.60 & - 0.60 \
4 & 39 & 55.48 & - 16.48 \
5 & 42 & 52.18 & - 10.18 \
6 & 63 & 50.15 & 12.85 \
7 & 54 & 52.72 & 1.28 \
8 & 55 & 52.97 & 2.03 \
9 & 61 & 53.38 & 7.62 \
10 & 52 & 54.90 & - 2.90
\end{array}$$ THE MEAN SQUARE ERROR 84.12
THE FORECAST FOR PERIOD 11 54.32
FORECASTING WITH EXPONENTIAL SMOOTHING
************************************************
THE SMOOTHING CONSTANT IS 0.8 $$\begin{array} { c c c r } 
\text { Time Period } & \text { Actual Value } & \text { Forecast } & \text { Forecast Error } \
1 & & & \
2 & 46 & 58.00 & - 12.00 \
3 & 55 & 48.40 & 6.60 \
4 & 39 & 53.68 & - 14.68 \
5 & 42 & 41.94 & 0.06 \
6 & 63 & 41.99 & 21.01 \
7 & 54 & 58.80 & - 4.80 \
8 & 55 & 54.96 & 0.04 \
9 & 61 & 54.99 & 6.01 \
10 & 52 & 59.80 & - 7.80
\end{array}$$ THE MEAN SQUARE ERROR 107.17
THE FORECAST FOR PERIOD 11 53.56
Based on MSE,smoothing with F1F1F1S1F1F1F10 = .2 provides a better model.

Question 3

Which of the following exponential smoothing constant values puts the same weight on the most recent time series value as does a 5-period moving average?

Accepted Answer

A) F1F1F1S1F1F1F10 = .2 
B) F1F1F1S1F1F1F10 = .25 
C) F1F1F1S1F1F1F10 = .75 
D) F1F1F1S1F1F1F10 = .8 
A) F1F1F1S1F1F1F10 = .2 
B) F1F1F1S1F1F1F10 = .25 
C) F1F1F1S1F1F1F10 = .75 
D) F1F1F1S1F1F1F10 = .8 A

Question 4

A trend line for the attendance at a restaurant's Sunday brunch is given by
Number = 264 + .72(t)
How many guests would you expect in week 20?

Accepted Answer

264 + .72(

Question 5

The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time series.

Accepted Answer

A) True 
 B)False

Question 6

The focus of smoothing methods is to smooth out

Accepted Answer

A) the random fluctuations. 
B) wide seasonal variations. 
C) significant trend effects. 
D) long range forecasts. 
A) the random fluctuations. 
B) wide seasonal variations. 
C) significant trend effects. 
D) long range forecasts.

Question 7

Qualitative forecasting techniques should be applied in situations where time series data exists,but where conditions are expected to change.

Accepted Answer

A) True 
 B)False

Question 8

Using a naive forecasting method,the forecast for next week's sales volume equals

Accepted Answer

A) the most recent week's sales volume 
B) the most recent week's forecast 
C) the average of the last four weeks' sales volumes 
D) next week's production volume 
A) the most recent week's sales volume 
B) the most recent week's forecast 
C) the average of the last four weeks' sales volumes 
D) next week's production volume

Question 9

Time series data can exhibit seasonal patterns of less than one month in duration.

Accepted Answer

A) True 
 B)False

Question 10

When using a moving average of order k to forecast,a small value for k is preferred if only the most recent values of the time series are considered relevant.

Accepted Answer

A) True 
 B)False

Question 11

Linear trend is calculated as Tt = 28.5 + .75t.The trend projection for period 15 is

Accepted Answer

A) 11.25 
B) 28.50 
C) 39.75 
D) 44.25 
A) 11.25 
B) 28.50 
C) 39.75 
D) 44.25

Question 12

In situations where you need to compare forecasting methods for different time periods,realtive measures such as mean absolute error (MAE)are preferred.

Accepted Answer

A) True 
 B)False

Question 13

All of the following are true about time series methods except

Accepted Answer

A) They discover a pattern in historical data and project it into the future. 
B) They involve the use of expert judgment to develop forecasts. 
C) They assume that the pattern of the past will continue into the future. 
D) Their forecasts are based solely on past values of the variable or past forecast errors. 
A) They discover a pattern in historical data and project it into the future. 
B) They involve the use of expert judgment to develop forecasts. 
C) They assume that the pattern of the past will continue into the future. 
D) Their forecasts are based solely on past values of the variable or past forecast errors.

Question 14

Use a four-period moving average to forecast attendance at baseball games.Historical records show
5346,7812,6513,5783,5982,6519,6283,5577,6712,7345

Accepted Answer

FORECASTING WITH MOVING AVERAG

Question 15

Exponential smoothing with F1F1F1S1F1F1F10 = .2 and a moving average with n = 5 put the same weight on the actual value for the current period.

Accepted Answer

A) True 
 B)False

Question 16

Whenever a categorical variable such as season has k levels,the number of dummy variables required is

Accepted Answer

A) k F1F1F1S1F1F1F10 1 
B) k 
C) k + 1 
D) 2k 
A) k F1F1F1S1F1F1F10 1 
B) k 
C) k + 1 
D) 2k

Question 17

Using exponential smoothing,the demand forecast for time period 10 equals the demand forecast for time period 9 plus

Accepted Answer

A) F1F1F1S1F1F1F10 times (the demand forecast for time period 8) 
B) F1F1F1S1F1F1F10 times (the error in the demand forecast for time period 9) 
C) F1F1F1S1F1F1F10 times (the observed demand in time period 9) 
D) F1F1F1S1F1F1F10 times (the demand forecast for time period 9) 
A) F1F1F1S1F1F1F10 times (the demand forecast for time period 8) 
B) F1F1F1S1F1F1F10 times (the error in the demand forecast for time period 9) 
C) F1F1F1S1F1F1F10 times (the observed demand in time period 9) 
D) F1F1F1S1F1F1F10 times (the demand forecast for time period 9)

Question 18

The mean squared error is influenced much more by large forecast errors than is the mean absolute error.

Accepted Answer

A) True 
 B)False

Question 19

The forecasting method that is appropriate when the time series has no significant trend,cyclical,or seasonal pattern is

Accepted Answer

A) moving average 
B) mean squared error 
C) mean average error 
D) qualitative forecasting 
A) moving average 
B) mean squared error 
C) mean average error 
D) qualitative forecasting

Question 20

Weekly sales of the Weber Dicamatic food processor for the past ten weeks have been: $$\begin{array} { c r | c r } 
\text { Week } & \text { Sales } & \text { Week } & \text { Sales } \
1 & 980 & 6 & 990 \
2 & 1040 & 7 & 1030 \
3 & 1120 & 8 & 1260 \
4 & 1050 & 9 & 1240 \
5 & 960 & 10 & 1100
\end{array}$$ 
a. Determine, on the basis of minimizing the mean square error, whether a three-period or four-period simple moving average model gives a better forecast for this problem.
b. For each model, forecast sales for week 11.

Accepted Answer

a.The 3 week moving average gives the be

Forecast errors

Which of the following exponential smoothing constant values puts the same weight on the most recent time series value as does a 5-period moving average?

A trend line for the attendance at a restaurant's Sunday brunch is given by Number = 264 + .72(t) How many guests would you expect in week 20?

The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time series.

The focus of smoothing methods is to smooth out

Qualitative forecasting techniques should be applied in situations where time series data exists,but where conditions are expected to change.

Using a naive forecasting method,the forecast for next week's sales volume equals

Time series data can exhibit seasonal patterns of less than one month in duration.

When using a moving average of order k to forecast,a small value for k is preferred if only the most recent values of the time series are considered relevant.

Linear trend is calculated as T_t = 28.5 + .75t.The trend projection for period 15 is

In situations where you need to compare forecasting methods for different time periods,realtive measures such as mean absolute error (MAE)are preferred.

All of the following are true about time series methods except

Use a four-period moving average to forecast attendance at baseball games.Historical records show 5346,7812,6513,5783,5982,6519,6283,5577,6712,7345

Exponential smoothing with  = .2 and a moving average with n = 5 put the same weight on the actual value for the current period.

Whenever a categorical variable such as season has k levels,the number of dummy variables required is

Using exponential smoothing,the demand forecast for time period 10 equals the demand forecast for time period 9 plus

The mean squared error is influenced much more by large forecast errors than is the mean absolute error.

The forecasting method that is appropriate when the time series has no significant trend,cyclical,or seasonal pattern is

Introduction

Introduction to Probability

Probability Distributions

Decision Analysis

Utility and Game Theory

Introduction to Linear Programming

Linear Programming: Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications in Marketing, Finance, and Operations Management

Distribution and Network Models

Integer Linear Programming

Advanced Optimization Applications

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Markov Processes

Filters

Exam 6: Times Series Analysis and Forecasting

Forecast errors

Which of the following exponential smoothing constant values puts the same weight on the most recent time series value as does a 5-period moving average?

A trend line for the attendance at a restaurant's Sunday brunch is given by Number = 264 + .72(t) How many guests would you expect in week 20?

The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time series.

The focus of smoothing methods is to smooth out

Qualitative forecasting techniques should be applied in situations where time series data exists,but where conditions are expected to change.

Using a naive forecasting method,the forecast for next week's sales volume equals

Time series data can exhibit seasonal patterns of less than one month in duration.

When using a moving average of order k to forecast,a small value for k is preferred if only the most recent values of the time series are considered relevant.

Linear trend is calculated as Tt = 28.5 + .75t.The trend projection for period 15 is

In situations where you need to compare forecasting methods for different time periods,realtive measures such as mean absolute error (MAE)are preferred.

All of the following are true about time series methods except

Use a four-period moving average to forecast attendance at baseball games.Historical records show 5346,7812,6513,5783,5982,6519,6283,5577,6712,7345

Exponential smoothing with  = .2 and a moving average with n = 5 put the same weight on the actual value for the current period.

Whenever a categorical variable such as season has k levels,the number of dummy variables required is

Using exponential smoothing,the demand forecast for time period 10 equals the demand forecast for time period 9 plus

The mean squared error is influenced much more by large forecast errors than is the mean absolute error.

The forecasting method that is appropriate when the time series has no significant trend,cyclical,or seasonal pattern is

Introduction

Introduction to Probability

Probability Distributions

Decision Analysis

Utility and Game Theory

Introduction to Linear Programming

Linear Programming: Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications in Marketing, Finance, and Operations Management

Distribution and Network Models

Integer Linear Programming

Advanced Optimization Applications

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Markov Processes

Filters

Linear trend is calculated as T_t = 28.5 + .75t.The trend projection for period 15 is