Question 1

Models such as moving average,exponential smoothing,and linear trend use only:

Accepted Answer

A) future values of Y to forecast previous values of Y 
B) previous values of Y to forecast future values of Y 
C) multiple explanatory variables (not just values of Y)to forecast future values of Y 
D) ratio-to-moving-average methods 
A) future values of Y to forecast previous values of Y 
B) previous values of Y to forecast future values of Y 
C) multiple explanatory variables (not just values of Y)to forecast future values of Y 
D) ratio-to-moving-average methods

Question 2

In a random walk model the

Accepted Answer

A) series itself is random 
B) series itself is not random but its differences are random 
C) series itself and its differences are random 
D) series itself and its differences are not random 
A) series itself is random 
B) series itself is not random but its differences are random 
C) series itself and its differences are random 
D) series itself and its differences are not random

Question 3

A time series is any variable that is measured over time in sequential order.

Accepted Answer

A) True 
 B)False

Question 4

(A)Use Excel to generate a time series of 25 values using this random walk model with a starting value of 200.
(B)Conduct a runs test on the series you generated for (A).Is it random? Explain.
(C)Conduct a runs test on the differences between successive values for the series you generated for (A).Is it random? Explain.
(D)Use the time series you constructed in (A)to forecast the next observation.

Accepted Answer

(A)Following is one random path of such

Question 5

What changes,if any,would you suggest to improve the forecast?

Accepted Answer

Although this deseasonalized moving aver

Question 6

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

Accepted Answer

A) True 
 B)False

Question 7

The runs test uses a series of 0's and 1's.The 0's and 1's represent whether each observation is:

Accepted Answer

A) above or below the predicted value of Y 
B) above or below the mean value of Y 
C) is above or below the mean value of the previous two observations 
D) is positive or negative 
A) above or below the predicted value of Y 
B) above or below the mean value of Y 
C) is above or below the mean value of the previous two observations 
D) is positive or negative

Question 8

Holt's model differs from simple exponential smoothing in that it includes a term for:

Accepted Answer

A) seasonality 
B) trend 
C) residuals 
D) cyclical fluctuations 
A) seasonality 
B) trend 
C) residuals 
D) cyclical fluctuations

Question 9

A regression approach can also be used to deal with seasonality by using_____variables for the seasons.

Accepted Answer

A) smoothing 
B) response 
C) residual 
D) dummy 
A) smoothing 
B) response 
C) residual 
D) dummy

Question 10

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

Accepted Answer

A) True 
 B)False

Question 11

Explain briefly whether the plot of the series visually supports the company's suspicion.

Accepted Answer

@#IMG-DLM& The above plot,in units sold,should cur

Question 12

Examples of non-random patterns that may be evident on a time series graph include:

Accepted Answer

A) trends 
B) increasing variance over time 
C) a meandering pattern 
D) too many zigzags 
E) all of these options 
A) trends 
B) increasing variance over time 
C) a meandering pattern 
D) too many zigzags 
E) all of these options

Question 13

The cyclic component of a time series is more likely to exhibit business cycles that record periods of economic recession and inflation.

Accepted Answer

A) True 
 B)False

Question 14

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Accepted Answer

A) True 
 B)False

Question 15

(A)Develop a time series plot of the data.
(B)Perform a runs test and compute a few autocorrelations to determine whether this time series is random.
(C)Given your answers to (A)and (B),what type of forecast do you recommend? Explain your answer.
(D)Use your answer to (C),to obtain a forecast for the next quarter (4 months).How reliable do you think this forecast is?

Accepted Answer

(A) @#IMG-DLM& (B) @#IMG-DLM& The data are clearly random.Th

Question 16

Related to the runs test,if you use a Z-statistic and you get a Z value greater than 2.0,this means that there is evidence of in the series

Accepted Answer

A) randomness 
B) nonrandomness 
C) nonnormality 
D) heteroscedasticity 
A) randomness 
B) nonrandomness 
C) nonnormality 
D) heteroscedasticity

Question 17

We compare the percent of variation explained R2 for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

Accepted Answer

A) True 
 B)False

Question 18

Which of the following summary measures for forecast errors does not depend on the units of the forecast variable?

Accepted Answer

A) MAE (mean absolute error) 
B) MFE (mean forecast error) 
C) RMSE (root mean square error) 
D) MAPE (mean absolute percentage error) 
A) MAE (mean absolute error) 
B) MFE (mean forecast error) 
C) RMSE (root mean square error) 
D) MAPE (mean absolute percentage error)

Question 19

Forecasting models can be divided into three groups.They are:

Accepted Answer

A) time series,optimization,and simulation methods 
B) judgmental,extrapolation,and econometric methods 
C) judgmental,random,and linear methods 
D) linear,non-linear,and extrapolation methods 
A) time series,optimization,and simulation methods 
B) judgmental,extrapolation,and econometric methods 
C) judgmental,random,and linear methods 
D) linear,non-linear,and extrapolation methods

Question 20

Use the method of moving averages with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Accepted Answer

@#IMG-DLM& @#IMG-DLM& The fit statistics show a MA

Models such as moving average,exponential smoothing,and linear trend use only:

In a random walk model the

A time series is any variable that is measured over time in sequential order.

What changes,if any,would you suggest to improve the forecast?

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

The runs test uses a series of 0's and 1's.The 0's and 1's represent whether each observation is:

Holt's model differs from simple exponential smoothing in that it includes a term for:

A regression approach can also be used to deal with seasonality by using_____variables for the seasons.

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

Explain briefly whether the plot of the series visually supports the company's suspicion.

Examples of non-random patterns that may be evident on a time series graph include:

The cyclic component of a time series is more likely to exhibit business cycles that record periods of economic recession and inflation.

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Related to the runs test,if you use a Z-statistic and you get a Z value greater than 2.0,this means that there is evidence of in the series

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

Which of the following summary measures for forecast errors does not depend on the units of the forecast variable?

Forecasting models can be divided into three groups.They are:

Use the method of moving averages with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, binomial, poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters

Exam 12: Time Series Analysis and Forecasting

Models such as moving average,exponential smoothing,and linear trend use only:

In a random walk model the

A time series is any variable that is measured over time in sequential order.

What changes,if any,would you suggest to improve the forecast?

In an additive seasonal model,we add an appropriate seasonal index to a "base" forecast.These indexes,one for each season,typically average to 0.

The runs test uses a series of 0's and 1's.The 0's and 1's represent whether each observation is:

Holt's model differs from simple exponential smoothing in that it includes a term for:

A regression approach can also be used to deal with seasonality by using_____variables for the seasons.

A time series can consist of four different components: trend,seasonal,cyclical,and random (or noise).

Explain briefly whether the plot of the series visually supports the company's suspicion.

Examples of non-random patterns that may be evident on a time series graph include:

The cyclic component of a time series is more likely to exhibit business cycles that record periods of economic recession and inflation.

A moving average is the average of the observations in the past few periods,where the number of terms in the average is the span.

Related to the runs test,if you use a Z-statistic and you get a Z value greater than 2.0,this means that there is evidence of in the series

We compare the percent of variation explained R2 for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.

Which of the following summary measures for forecast errors does not depend on the units of the forecast variable?

Forecasting models can be divided into three groups.They are:

Use the method of moving averages with an appropriate span to forecast retail sales for 2010.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Introduction to Data Analysis and Decision Making

Describing the Distribution of a Single Variable

Finding Relationships Among Variables

Probability and Probability Distributions

Normal, binomial, poisson, and Exponential Distributions

Decision Making Under Uncertainty

Sampling and Sampling Distributions

Confidence Interval Estimation

Hypothesis Testing

Regression Analysis: Estimating Relationships

Regression Analysis: Statistical Inference

Introduction to Optimization Modeling

Optimization Modeling: Applications

Introduction to Simulation Modeling

Simulation Models

Filters

We compare the percent of variation explained R² for a regression model with seasonal dummy variables to the MAPE for the smoothing model with seasonality to see which model is more accurate.