Question 1

Which of the following is not one of the techniques that can be used to identify whether a time series is truly random?

Accepted Answer

A)  a graph (plot the data) 
B)  the runs test 
C)  a control chart 
D)  the autocorrelations (or a correlogram) 
A)  a graph (plot the data) 
B)  the runs test 
C)  a control chart 
D)  the autocorrelations (or a correlogram)

Question 2

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 3

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 4

The moving average method can also be referred to as a(n) ____ method.

Accepted Answer

A)  causal 
B)  smoothing 
C)  exponential 
D)  econometric 
A)  causal 
B)  smoothing 
C)  exponential 
D)  econometric

Question 5

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 6

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else, except possible time itself - to forecast values of the variable.

Accepted Answer

A) True 
 B)False

Question 7

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 8

To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.

Accepted Answer

A) True 
 B)False

Question 9

As is the case with residuals from regression, the forecast errors for nonregression methods will always average to zero.

Accepted Answer

A) True 
 B)False

Question 10

In exponential smoothing models, the forecast is based on the level at time t, Lt, which is not observable and can only be estimated.

Accepted Answer

A) True 
 B)False

Question 11

The idea behind the runs test is that a random number series should have a number of runs that is:

Accepted Answer

A)  large 
B)  small 
C)  not large or small 
D)  constant 
A)  large 
B)  small 
C)  not large or small 
D)  constant

Question 12

The random walk model is written as:   . In this model,   represents the:

Accepted Answer

A)  average of the Y's 
B)  average of the X's 
C)  forecasted value 
D)  random series with mean 0 and some constant standard deviation 
A)  average of the Y's 
B)  average of the X's 
C)  forecasted value 
D)  random series with mean 0 and some constant standard deviation

Question 13

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

Accepted Answer

A) True 
 B)False

Question 14

(A) Suppose that actual demands during April and May are as follows: May, 5500 cans; June 4500 cans. After observing May's demand, what is the forecast for June's demand?
(B) Based on the data from (A), the demands during April and May average (5500+4500)/2 = 5000 cans per month. This is the same as the forecast for monthly sales before we observed the April and May data. Yet after we observed the April and May demands for Pepsi, our forecast for June demand has decreased from what it was at the end of March. Why?

Accepted Answer

(A) @#IMG-DLM& The forecast for June demand is 495

Question 15

(A) Fit the appropriate regression model to the data. Report the resulting equation and state explicitly what it says about the percentage growth per month.
(B) What is the MAPE for the forecast model in (A)? What does it measure? Considering its magnitudes, does the model seem to be doing a good job?

Accepted Answer

(A) @#IMG-DLM& The regression model should use log

Question 16

In contrast to linear trend, an exponential trend is appropriate when the time series 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 17

The seasonal component of a time series is more difficult to predict than the cyclic component because cyclic variation is much more regular.

Accepted Answer

A) True 
 B)False

Question 18

The number of employees on the payroll at a computer software company is recorded at the start of each month from January 2007 to December 2009. These data are shown below.  
-Develop a time series plot of the data. Does the data appear random on the plot?

Accepted Answer

@#IMG-DLM& The data definitely

Question 19

There are a variety of deseasonalizing methods, but they are typically variations of:

Accepted Answer

A)  ratio-to-seasonality methods 
B)  ratio-to-exponential-smoothing methods 
C)  ratio-to-moving-average methods 
D)  linear trend 
A)  ratio-to-seasonality methods 
B)  ratio-to-exponential-smoothing methods 
C)  ratio-to-moving-average methods 
D)  linear trend

Question 20

A small p-value in the rune test provides evidence of:

Accepted Answer

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

Which of the following is not one of the techniques that can be used to identify whether a time series is truly random?

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

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

The moving average method can also be referred to as a(n) ____ method.

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.

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else, except possible time itself - to forecast values of the variable.

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

To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.

As is the case with residuals from regression, the forecast errors for nonregression methods will always average to zero.

In exponential smoothing models, the forecast is based on the level at time t, L_t, which is not observable and can only be estimated.

The idea behind the runs test is that a random number series should have a number of runs that is:

The random walk model is written as: . In this model, represents the:

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

In contrast to linear trend, an exponential trend is appropriate when the time series changes by a:

The seasonal component of a time series is more difficult to predict than the cyclic component because cyclic variation is much more regular.

The number of employees on the payroll at a computer software company is recorded at the start of each month from January 2007 to December 2009. These data are shown below. -Develop a time series plot of the data. Does the data appear random on the plot?

There are a variety of deseasonalizing methods, but they are typically variations of:

A small p-value in the rune test provides evidence of:

Introduction to Business Analytics

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 Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

Exam 12: Time Series Analysis and Forecasting

Which of the following is not one of the techniques that can be used to identify whether a time series is truly random?

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

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

The moving average method can also be referred to as a(n) ____ method.

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.

Extrapolation forecasting methods are quantitative methods that use past data of a time series variable - and nothing else, except possible time itself - to forecast values of the variable.

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

To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.

As is the case with residuals from regression, the forecast errors for nonregression methods will always average to zero.

In exponential smoothing models, the forecast is based on the level at time t, Lt, which is not observable and can only be estimated.

The idea behind the runs test is that a random number series should have a number of runs that is:

The random walk model is written as: . In this model, represents the:

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

In contrast to linear trend, an exponential trend is appropriate when the time series changes by a:

The seasonal component of a time series is more difficult to predict than the cyclic component because cyclic variation is much more regular.

The number of employees on the payroll at a computer software company is recorded at the start of each month from January 2007 to December 2009. These data are shown below. -Develop a time series plot of the data. Does the data appear random on the plot?

There are a variety of deseasonalizing methods, but they are typically variations of:

A small p-value in the rune test provides evidence of:

Introduction to Business Analytics

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 Models

Introduction to Simulation Modeling

Simulation Models

Data Mining

Importing Data Into Excel

Analysis of Variance and Experimental Design

Statistical Process Control

Filters

In exponential smoothing models, the forecast is based on the level at time t, L_t, which is not observable and can only be estimated.