Question 1

For a month following a presidential illness, very few homes were sold.Afterwards, the realty business returned to normal levels.This is an example of:

Accepted Answer

A) long term trend. 
B) seasonal variation. 
C) cyclical variation. 
D) random effects. 
A) long term trend. 
B) seasonal variation. 
C) cyclical variation. 
D) random effects. C

Question 2

What value of $\alpha$ makes exponential smoothing equivalent to a moving average based on 4 periods of data?

Accepted Answer

($\alpha$= 2/(k + 1) = 0.4)

Question 3

In a stationary forecasting model, the value of the time series for a specific period equals the unchanging mean value of the time series plus a random error term for that period.

Accepted Answer

A) True 
 B)False  True

Question 4

How do you use the p-value and the significance (or confidence) level to check for trend in a time series?

Accepted Answer

(If the p-value is g

Question 5

If positive autocorrelation exists, the exponential smoothing constant should be close to zero to track changes in the time series mean.

Accepted Answer

A) True 
 B)False

Question 6

If it is suspected that the major influence in a stationary time series is random variation, the preferable forecasting technique would be the:

Accepted Answer

A) classical decomposition. 
B) moving average method. 
C) Holt's linear exponential smoothing technique. 
D) linear regression. 
A) classical decomposition. 
B) moving average method. 
C) Holt's linear exponential smoothing technique. 
D) linear regression.

Question 7

Autocorrelation measures only how the value in one time period
affects the value in the next time period.

Accepted Answer

A) True 
 B)False

Question 8

What are the advantages of the Last Period technique for a stationary time series?

Accepted Answer

(It is easy to calcu

Question 9

In exponential smoothing, if the smoothing constant, alpha, is 0, the forecast will never change.

Accepted Answer

A) True 
 B)False

Question 10

If a time series is believed to exhibit non-linear trend, one should use Holt's exponential smoothing technique on the original time series data.

Accepted Answer

A) True 
 B)False

Question 11

If one uses a stationary linear forecasting model, the forecast for period t + 1 will not necessarily be the same as the forecast for period t + 2.

Accepted Answer

A) True 
 B)False

Question 12

A business experiencing stationary demand does not need forecasting.

Accepted Answer

A) True 
 B)False

Question 13

In exponential smoothing, if the smoothing constant, alpha, is 1, you will get the same forecast as obtainable using the last period technique.

Accepted Answer

A) True 
 B)False

Question 14

In the exponential smoothing (ES) technique, the value of alpha, the smoothing constant:

Accepted Answer

A) may assume any non-negative value. 
B) determines the forecasting model's responsiveness to abrupt changes. 
C) typically is at the higher end in the range of possible values. 
D) is preset by the analyst and not subject to validity testing. 
A) may assume any non-negative value. 
B) determines the forecasting model's responsiveness to abrupt changes. 
C) typically is at the higher end in the range of possible values. 
D) is preset by the analyst and not subject to validity testing.

Question 15

Selecting a forecasting technique for which the largest absolute deviation is minimized is similar to which decision analysis approach?

Accepted Answer

A) Maximin. 
B) Minimax. 
C) Minimax regret. 
D) Maximax. 
A) Maximin. 
B) Minimax. 
C) Minimax regret. 
D) Maximax.

Question 16

One of the measures for evaluating forecast errors is the Mean Squared Error (MSE), in which differences between forecasted and actual values are squared.Why is this a desirable trait?

Accepted Answer

(Squaring would be desirable

Question 17

How can cyclical components of a time series be identified?

Accepted Answer

A) Autocorrelation test. 
B) Graphically. 
C) Linear regression. 
D) Cyclical effects cannot be detected. 
A) Autocorrelation test. 
B) Graphically. 
C) Linear regression. 
D) Cyclical effects cannot be detected.

Question 18

Below is a chart of potholes repaired in Sunnyside Township.$\begin{array}{ccc}
\text{Year}&\text{Potholes}&\text{Year}&\text{Potholes}\
1990 & 27 & 1996 & 35 \
1991 & 29 & 1997 & 37 \
1992 & 35 & 1998 & 39 \
1993 & 28 & 1999 & 38 \
1994 & 32 & 2000 & 41 \
1995 & 37 & 2001 & 44
\end{array}$
 A.Using a three period weighted moving average with weights of .5, .3, and .2, compute what would have been forecast for 1993-2002.
B.Compute MSE, MAD, MAPE, and LAD.

Accepted Answer

A.(1993-31.6, 1994-30.3, 1995

Question 19

A stationary forecasting model is appropriate for a time series which exhibits primarily:

Accepted Answer

A) trend. 
B) cyclical influences. 
C) seasonal components. 
D) random variation. 
A) trend. 
B) cyclical influences. 
C) seasonal components. 
D) random variation.

Question 20

The "weights" in the weighted moving average method are unequal and typically decrease with the age of the observation.

Accepted Answer

A) True 
 B)False

For a month following a presidential illness, very few homes were sold.Afterwards, the realty business returned to normal levels.This is an example of:

What value of $\alpha$ makes exponential smoothing equivalent to a moving average based on 4 periods of data?

In a stationary forecasting model, the value of the time series for a specific period equals the unchanging mean value of the time series plus a random error term for that period.

How do you use the p-value and the significance (or confidence) level to check for trend in a time series?

If positive autocorrelation exists, the exponential smoothing constant should be close to zero to track changes in the time series mean.

If it is suspected that the major influence in a stationary time series is random variation, the preferable forecasting technique would be the:

Autocorrelation measures only how the value in one time period affects the value in the next time period.

What are the advantages of the Last Period technique for a stationary time series?

In exponential smoothing, if the smoothing constant, alpha, is 0, the forecast will never change.

If a time series is believed to exhibit non-linear trend, one should use Holt's exponential smoothing technique on the original time series data.

If one uses a stationary linear forecasting model, the forecast for period t + 1 will not necessarily be the same as the forecast for period t + 2.

A business experiencing stationary demand does not need forecasting.

In exponential smoothing, if the smoothing constant, alpha, is 1, you will get the same forecast as obtainable using the last period technique.

In the exponential smoothing (ES) technique, the value of alpha, the smoothing constant:

Selecting a forecasting technique for which the largest absolute deviation is minimized is similar to which decision analysis approach?

One of the measures for evaluating forecast errors is the Mean Squared Error (MSE), in which differences between forecasted and actual values are squared.Why is this a desirable trait?

How can cyclical components of a time series be identified?

A stationary forecasting model is appropriate for a time series which exhibits primarily:

The "weights" in the weighted moving average method are unequal and typically decrease with the age of the observation.

Introduction to Management Science Models

Introduction to Management Science Models

Applications of Linear and Integer Programming Models

Network Models

Project Scheduling Models

Decision Models

Inventory Models

Queuing Models

Simulation Models

Quality Management Models

Markov Process Models

Nonlinear Models:dynamic, Goal, and Nonlinear Programming

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Exam 7: Forecasting

For a month following a presidential illness, very few homes were sold.Afterwards, the realty business returned to normal levels.This is an example of:

What value of α\alphaα makes exponential smoothing equivalent to a moving average based on 4 periods of data?

In a stationary forecasting model, the value of the time series for a specific period equals the unchanging mean value of the time series plus a random error term for that period.

How do you use the p-value and the significance (or confidence) level to check for trend in a time series?

If positive autocorrelation exists, the exponential smoothing constant should be close to zero to track changes in the time series mean.

If it is suspected that the major influence in a stationary time series is random variation, the preferable forecasting technique would be the:

Autocorrelation measures only how the value in one time period affects the value in the next time period.

What are the advantages of the Last Period technique for a stationary time series?

In exponential smoothing, if the smoothing constant, alpha, is 0, the forecast will never change.

If a time series is believed to exhibit non-linear trend, one should use Holt's exponential smoothing technique on the original time series data.

If one uses a stationary linear forecasting model, the forecast for period t + 1 will not necessarily be the same as the forecast for period t + 2.

A business experiencing stationary demand does not need forecasting.

In exponential smoothing, if the smoothing constant, alpha, is 1, you will get the same forecast as obtainable using the last period technique.

In the exponential smoothing (ES) technique, the value of alpha, the smoothing constant:

Selecting a forecasting technique for which the largest absolute deviation is minimized is similar to which decision analysis approach?

One of the measures for evaluating forecast errors is the Mean Squared Error (MSE), in which differences between forecasted and actual values are squared.Why is this a desirable trait?

How can cyclical components of a time series be identified?

A stationary forecasting model is appropriate for a time series which exhibits primarily:

The "weights" in the weighted moving average method are unequal and typically decrease with the age of the observation.

Introduction to Management Science Models

Introduction to Management Science Models

Applications of Linear and Integer Programming Models

Network Models

Project Scheduling Models

Decision Models

Inventory Models

Queuing Models

Simulation Models

Quality Management Models

Markov Process Models

Nonlinear Models:dynamic, Goal, and Nonlinear Programming

Filters

What value of $\alpha$ makes exponential smoothing equivalent to a moving average based on 4 periods of data?