A) should avoid the use of regression analysis. B) attempt to explain a time series' behavior. C) do not use time series data. D) All of the alternatives are true. A) should avoid the use of regression analysis. B) attempt to explain a time series' behavior. C) do not use time series data. D) All of the alternatives are true.

Exam 6: Forecasting

Quarterly revenues (in $1,000,000's)for a national restaurant chain for a five-year period were as follows: a.Determine the appropriate seasonal index values for this time series. b.Determine the appropriate trend value for this time series. c.Forecast the revenues for the next four quarters.

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Question 1

Correct Answer:

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a.S₁ = 1.069,S₂ = 0.973,S₃ = 0.954,S₄ = 1.004
b.b₁ = 3.361
c.F₂₁ = 100.50,F₂₂ = 94.72,F₂₃ = 96.15,F₂₄ = 104.53

The average SAT verbal score for students from one high school over the last ten exams is 508,490,502,505,493,506,492,490,503,501 Do the scores support an increasing or a decreasing trend?

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Question 2

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FORECASTING WITH LINEAR TREND
************************************
THE LINEAR TREND EQUATION:
T = 500.933 - 0.352 t
where T = trend value of the time series in period t
The negative slope for the trend indicates that scores are decreasing.(However,we have not tested for the significance of this coefficient. )

Which of the following is a qualitative forecasting method?

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Question 3

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Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1)Christmas (November-December); (2)Father's Day (late May - mid-June);and (3)all other times.Average weekly sales (in $'s)during each of these three seasons during the past four years has been as follows: Determine a forecast for the average weekly sales in year 5 for each of the three seasons.

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Question 4

Quantitative forecasting methods do not require that patterns from the past will necessarily continue in the future.

(True/False)

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Question 5

A customer comment phone line is staffed from 8:00 a.Use this information to calculate a seasonal index. a.m.to 4:30 p.m.five days a week.Records are available that show the number of calls received every day for the last five weeks. b.Deseasonalize the data. c.Find the trend line. d.Assume there is no cyclical component and forecast the calls for week 6.

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Question 6

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

(True/False)

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Question 7

Use the following to forecast a value for period 14,a second quarter. T = 16.32 - .18(t) C₂ = .91 S₂ = .75

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Question 8

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

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Question 9

An alpha ( $\alpha$ )value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in demand than will an $\alpha$ equal to .4.

(True/False)

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Question 10

If the random variability in a time series is great,a high SYMBOL 97 \f "Symbol" value should be used to exponentially smooth out the fluctuations.

(True/False)

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Question 11

For a multiplicative time series model,the sum of the seasonal indexes should equal the number of seasons.

(True/False)

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Question 12

With fewer periods in a moving average,it will take longer to adjust to a new level of demand.

(True/False)

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Question 13

Sales (in thousands)of the new Thorton Model 506 convection oven over the eight-week period since its introduction have been as follows: $Sales (in thousands)of the new Thorton Model 506 convection oven over the eight-week period since its introduction have been as follows: a.Which exponential smoothing model provides better forecasts,one using SYMBOL 97 \f Symbol = .6 or \alpha = .2? Compare them using mean squared error. b.Using the two forecast models in part (a),what are the forecasts for week 9?$ a.Which exponential smoothing model provides better forecasts,one using SYMBOL 97 \f "Symbol" = .6 or $\alpha$ = .2? Compare them using mean squared error. b.Using the two forecast models in part (a),what are the forecasts for week 9?

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Question 14

Quarterly billing for water usage is shown below. a.Find the seasonal index for each quarter. b.Deseasonalize the data. c.Find the trend line. d.Assume there is no cyclical component and forecast the summer billing for year 5.

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Question 15

Causal models

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Question 16

Time series methods base forecasts only on past values of the variables.

(True/False)

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Question 17

If data for a time series analysis is collected on an annual basis only,which component may be ignored?

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Question 18

Seasonal components with values above 1.00 indicate actual values below the trend line.

(True/False)

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Question 19

To make period-to-period comparisons more meaningful and identify trend,the time series should be deseasonalized.

(True/False)

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Question 20

Showing 1 - 20 of 60

Quarterly revenues (in $1,000,000's)for a national restaurant chain for a five-year period were as follows: a.Determine the appropriate seasonal index values for this time series. b.Determine the appropriate trend value for this time series. c.Forecast the revenues for the next four quarters.

The average SAT verbal score for students from one high school over the last ten exams is 508,490,502,505,493,506,492,490,503,501 Do the scores support an increasing or a decreasing trend?

Which of the following is a qualitative forecasting method?

Quantitative forecasting methods do not require that patterns from the past will necessarily continue in the future.

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

Use the following to forecast a value for period 14,a second quarter. T = 16.32 - .18(t) C₂ = .91 S₂ = .75

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

An alpha ( $\alpha$ )value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in demand than will an $\alpha$ equal to .4.

If the random variability in a time series is great,a high SYMBOL 97 \f "Symbol" value should be used to exponentially smooth out the fluctuations.

For a multiplicative time series model,the sum of the seasonal indexes should equal the number of seasons.

With fewer periods in a moving average,it will take longer to adjust to a new level of demand.

Quarterly billing for water usage is shown below. a.Find the seasonal index for each quarter. b.Deseasonalize the data. c.Find the trend line. d.Assume there is no cyclical component and forecast the summer billing for year 5.

Causal models

Time series methods base forecasts only on past values of the variables.

If data for a time series analysis is collected on an annual basis only,which component may be ignored?

Seasonal components with values above 1.00 indicate actual values below the trend line.

To make period-to-period comparisons more meaningful and identify trend,the time series should be deseasonalized.

Introduction

Introduction to Probability

Probability Distributions

Decision Analysis

Utility and Game Theory

Introduction to Linear Programming

Lp Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications

Distribution and Network Problems

Integer Linear Programming

Advanced Optimization Application

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Markov Processes

Filters

Exam 6: Forecasting

Quarterly revenues (in $1,000,000's)for a national restaurant chain for a five-year period were as follows: a.Determine the appropriate seasonal index values for this time series. b.Determine the appropriate trend value for this time series. c.Forecast the revenues for the next four quarters.

The average SAT verbal score for students from one high school over the last ten exams is 508,490,502,505,493,506,492,490,503,501 Do the scores support an increasing or a decreasing trend?

Which of the following is a qualitative forecasting method?

Quantitative forecasting methods do not require that patterns from the past will necessarily continue in the future.

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

Use the following to forecast a value for period 14,a second quarter. T = 16.32 - .18(t) C2 = .91 S2 = .75

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

An alpha ( α\alphaα )value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in demand than will an α\alphaα equal to .4.

If the random variability in a time series is great,a high SYMBOL 97 \f "Symbol" value should be used to exponentially smooth out the fluctuations.

For a multiplicative time series model,the sum of the seasonal indexes should equal the number of seasons.

With fewer periods in a moving average,it will take longer to adjust to a new level of demand.

Quarterly billing for water usage is shown below. a.Find the seasonal index for each quarter. b.Deseasonalize the data. c.Find the trend line. d.Assume there is no cyclical component and forecast the summer billing for year 5.

Causal models

Time series methods base forecasts only on past values of the variables.

If data for a time series analysis is collected on an annual basis only,which component may be ignored?

Seasonal components with values above 1.00 indicate actual values below the trend line.

To make period-to-period comparisons more meaningful and identify trend,the time series should be deseasonalized.

Introduction

Introduction to Probability

Probability Distributions

Decision Analysis

Utility and Game Theory

Introduction to Linear Programming

Lp Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications

Distribution and Network Problems

Integer Linear Programming

Advanced Optimization Application

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Markov Processes

Filters

Use the following to forecast a value for period 14,a second quarter. T = 16.32 - .18(t) C₂ = .91 S₂ = .75

An alpha ( $\alpha$ )value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in demand than will an $\alpha$ equal to .4.