Question 1

A trend component of a time series is a long-term,relatively smooth pattern or direction exhibited by a series,and its duration is more than one year.

Accepted Answer

A) True 
 B)False

Question 2

When using exponential smoothing,a smoothing constant   must be used.The value for   :

Accepted Answer

A) ranges between 0 and 1 
B) ranges between -1 and +1 
C) equals the largest observed value in the series 
D) represents the strength of the association between the forecasted and observed values 
A) ranges between 0 and 1 
B) ranges between -1 and +1 
C) equals the largest observed value in the series 
D) represents the strength of the association between the forecasted and observed values

Question 3

(A)Is this time series random? Perform a runs test and compute a few autocorrelations to support your answer.
(B)Does a linear trend appear to fit these data well? If so,estimate the linear-trend model for this time series,and interpret the   value.
(C)Is there evidence of some seasonal pattern in these sales data? If so,characterize the seasonal pattern,and explain how to forecast future values.

Accepted Answer

(A) @#IMG-DLM& The runs test indicates the data is

Question 4

When using exponential smoothing,if you want the forecast to react quickly to movements in the series,you should choose:

Accepted Answer

A) values of   near 1 
B) values of   near 0 
C) values of   midway between 0 and 1 
D) it depends on the data set 
A) values of   near 1 
B) values of   near 0 
C) values of   midway between 0 and 1 
D) it depends on the data set

Question 5

Regression models with seasonal dummy variables produce coefficients for each quarter,which represent the additive or multiplicative factors relative to the annual average.

Accepted Answer

A) True 
 B)False

Question 6

A linear trend means that the time series variable changes by a:

Accepted Answer

A) constant amount each time period 
B) constant percentage each time period 
C) positive amount each time period 
D) negative amount each time period 
A) constant amount each time period 
B) constant percentage each time period 
C) positive amount each time period 
D) negative amount each time period

Question 7

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

Accepted Answer

A) True 
 B)False

Question 8

Which of the following is not a method for dealing with seasonality in data

Accepted Answer

A) Winter's exponential smoothing model 
B) deseasonalizing the data,using any forecasting model,then reseasonalizing the data 
C) multiple regression with lags for the seasons 
D) multiple regression with dummy variables for the seasons 
A) Winter's exponential smoothing model 
B) deseasonalizing the data,using any forecasting model,then reseasonalizing the data 
C) multiple regression with lags for the seasons 
D) multiple regression with dummy variables for the seasons

Question 9

To deseasonalize an observation (assuming a multiplicative model of seasonality),multiply it by the appropriate seasonal index.

Accepted Answer

A) True 
 B)False

Question 10

The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods.

Accepted Answer

A) True 
 B)False

Question 11

Extrapolation methods attempt to:

Accepted Answer

A) use non-quantitative methods to predict future values 
B) search for patterns in the data and then use those to predict future values 
C) find variables that are correlated with the data being predicted 
D) predict the next period's value by using the latest period's value 
A) use non-quantitative methods to predict future values 
B) search for patterns in the data and then use those to predict future values 
C) find variables that are correlated with the data being predicted 
D) predict the next period's value by using the latest period's value

Question 12

If we use a value close to 1 for the level smoothing constant   and a value close to 0 for the trend smoothing constant   in Holt's exponential smoothing model,then we expect the model to respond very quickly to changes in the level,but very slowly to changes in the trend.

Accepted Answer

A) True 
 B)False

Question 13

Run the moving average fit again,this time holding out the last 6 observations to validate the fit.What do you find?

Accepted Answer

@#IMG-DLM& The fit statistics in the holdouts peri

Question 14

The null hypothesis in a runs test is   the data series is random

Accepted Answer

A) True 
 B)False

Question 15

Suppose that a simple exponential smoothing model is used (with a = 0.30)to forecast monthly sandwich sales at a local sandwich shop.After June's demand is observed at 1520 sandwiches,the forecasted demand for July is 1600 sandwiches.At the beginning of July,what would be the forecasted demand for August?

Accepted Answer

A) 1520 
B) 1544 
C) 1550 
D) 1600 
A) 1520 
B) 1544 
C) 1550 
D) 1600

Question 16

A meandering pattern is an example of a random time series.

Accepted Answer

A) True 
 B)False

Question 17

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

Accepted Answer

A) True 
 B)False

Question 18

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

Accepted Answer

A) True 
 B)False

Question 19

The number of reported accidents at a manufacturing plant located in Flint,Michigan,was recorded at the start of each month.These data are provided in the table below:   Is this time series random? Perform a runs test and compute a few autocorrelations to support your answer.

Accepted Answer

@#IMG-DLM& The runs test indicates the data may be

Question 20

Obtain a time series graph of the data.If you will be using a moving average model of the data,what information does this graph provide to help specify such a model?

Accepted Answer

@#IMG-DLM& The graph shows a definite seasonal pat

A trend component of a time series is a long-term,relatively smooth pattern or direction exhibited by a series,and its duration is more than one year.

When using exponential smoothing,a smoothing constant must be used.The value for :

When using exponential smoothing,if you want the forecast to react quickly to movements in the series,you should choose:

Regression models with seasonal dummy variables produce coefficients for each quarter,which represent the additive or multiplicative factors relative to the annual average.

A linear trend means that the time series variable changes by a:

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

Which of the following is not a method for dealing with seasonality in data

To deseasonalize an observation (assuming a multiplicative model of seasonality),multiply it by the appropriate seasonal index.

The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods.

Extrapolation methods attempt to:

If we use a value close to 1 for the level smoothing constant and a value close to 0 for the trend smoothing constant in Holt's exponential smoothing model,then we expect the model to respond very quickly to changes in the level,but very slowly to changes in the trend.

Run the moving average fit again,this time holding out the last 6 observations to validate the fit.What do you find?

The null hypothesis in a runs test is the data series is random

A meandering pattern is an example of a random time series.

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

The number of reported accidents at a manufacturing plant located in Flint,Michigan,was recorded at the start of each month.These data are provided in the table below: Is this time series random? Perform a runs test and compute a few autocorrelations to support your answer.

Obtain a time series graph of the data.If you will be using a moving average model of the data,what information does this graph provide to help specify such a model?

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

A trend component of a time series is a long-term,relatively smooth pattern or direction exhibited by a series,and its duration is more than one year.

When using exponential smoothing,a smoothing constant must be used.The value for :

When using exponential smoothing,if you want the forecast to react quickly to movements in the series,you should choose:

Regression models with seasonal dummy variables produce coefficients for each quarter,which represent the additive or multiplicative factors relative to the annual average.

A linear trend means that the time series variable changes by a:

Holt's method is an exponential smoothing method,which is appropriate for a series with seasonality and possibly a trend.

Which of the following is not a method for dealing with seasonality in data

To deseasonalize an observation (assuming a multiplicative model of seasonality),multiply it by the appropriate seasonal index.

The moving average method is perhaps the simplest and one of the most frequently-used extrapolation methods.

Extrapolation methods attempt to:

If we use a value close to 1 for the level smoothing constant and a value close to 0 for the trend smoothing constant in Holt's exponential smoothing model,then we expect the model to respond very quickly to changes in the level,but very slowly to changes in the trend.

Run the moving average fit again,this time holding out the last 6 observations to validate the fit.What do you find?

The null hypothesis in a runs test is the data series is random

A meandering pattern is an example of a random time series.

Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.

Forecasting software packages typically report several summary measures of the forecasting error.The most important of these are MAE (mean absolute error),RMSE (root mean square error),and MAPE (mean absolute percentage error).

The number of reported accidents at a manufacturing plant located in Flint,Michigan,was recorded at the start of each month.These data are provided in the table below: Is this time series random? Perform a runs test and compute a few autocorrelations to support your answer.

Obtain a time series graph of the data.If you will be using a moving average model of the data,what information does this graph provide to help specify such a model?

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