Exam 12: Time Series Analysis and Forecasting

The seasonal component of a time series is more likely to exhibit the relatively steady growth of a variable,such as the population of Egypt from 35 million in 1960 to 93 million in 2016.

(True/False)

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Question 41

The forecast error is the difference between

(Multiple Choice)

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Question 42

Which term refers to a consecutive series of observations that remain on one side of the base level?

(Multiple Choice)

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Question 43

Perhaps the simplest and one of the most frequently used extrapolation methods is the

(Multiple Choice)

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Question 44

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

(True/False)

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Question 45

In contrast to linear trend,an exponential trend is appropriate when the time series changes by a _____ each time period.

(Multiple Choice)

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Question 46

The runs test is a formal test of the null hypothesis of randomness.If there are too many or too few runs in the series,then we conclude that the series is not random.

(True/False)

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Question 47

The following are the values of a time series for the first four time periods: t 1 2 3 4 24 25 26 27 Using a four-period moving average,the forecasted value for time period 5 is

(Multiple Choice)

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Question 48

Suppose that a simple exponential smoothing model is used (with α = 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?

(Multiple Choice)

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Question 49

In a random walk model,the series itself

(Multiple Choice)

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Question 50

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

(Multiple Choice)

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Question 51

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

(True/False)

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Question 52

An exponential trend is appropriate when the time series changes by a constant percentage each period.

(True/False)

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Question 53

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.

(True/False)

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Question 54

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

(Essay)

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Question 55

We compute the five-period moving averages for all time periods except the first two.

(True/False)

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Question 56

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

(Multiple Choice)

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Question 57

Correlogram is a bar chart of autocorrelation at different lags.

(True/False)

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Question 58

Use the method of moving average with an appropriate span to forecast retail sales for the first half of 2009.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

(Essay)

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Question 59

Every form of exponential smoothing model has at least one smoothing constant,which is always between 0 and 1.

(True/False)

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Question 60

Showing 41 - 60 of 95

The seasonal component of a time series is more likely to exhibit the relatively steady growth of a variable,such as the population of Egypt from 35 million in 1960 to 93 million in 2016.

The forecast error is the difference between

Which term refers to a consecutive series of observations that remain on one side of the base level?

Perhaps the simplest and one of the most frequently used extrapolation methods is the

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

In contrast to linear trend,an exponential trend is appropriate when the time series changes by a _____ each time period.

The runs test is a formal test of the null hypothesis of randomness.If there are too many or too few runs in the series,then we conclude that the series is not random.

The following are the values of a time series for the first four time periods: t 1 2 3 4 24 25 26 27 Using a four-period moving average,the forecasted value for time period 5 is

In a random walk model,the series itself

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

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

An exponential trend is appropriate when the time series changes by a constant percentage each period.

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.

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

We compute the five-period moving averages for all time periods except the first two.

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

Correlogram is a bar chart of autocorrelation at different lags.

Use the method of moving average with an appropriate span to forecast retail sales for the first half of 2009.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Every form of exponential smoothing model has at least one smoothing constant,which is always between 0 and 1.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability 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 Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 12: Time Series Analysis and Forecasting

The seasonal component of a time series is more likely to exhibit the relatively steady growth of a variable,such as the population of Egypt from 35 million in 1960 to 93 million in 2016.

The forecast error is the difference between

Which term refers to a consecutive series of observations that remain on one side of the base level?

Perhaps the simplest and one of the most frequently used extrapolation methods is the

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

In contrast to linear trend,an exponential trend is appropriate when the time series changes by a _____ each time period.

The runs test is a formal test of the null hypothesis of randomness.If there are too many or too few runs in the series,then we conclude that the series is not random.

The following are the values of a time series for the first four time periods: t 1 2 3 4 24 25 26 27 Using a four-period moving average,the forecasted value for time period 5 is

In a random walk model,the series itself

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

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

An exponential trend is appropriate when the time series changes by a constant percentage each period.

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.

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

We compute the five-period moving averages for all time periods except the first two.

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

Correlogram is a bar chart of autocorrelation at different lags.

Use the method of moving average with an appropriate span to forecast retail sales for the first half of 2009.Do you obtain a good fit? Do you have confidence in your forecast? Explain your answers.

Every form of exponential smoothing model has at least one smoothing constant,which is always between 0 and 1.

Introduction to Business Analytics

Describing the Distribution of a Variable

Finding Relationships Among Variables

Business Intelligence Bifor Data Analysis

Probability and Probability 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 Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Analysis of Variance and Experimental Design

Statistical Process Control

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.