Question 1

In a moving averages method, which of the following represent(s) the number of terms in the moving average?

Accepted Answer

A)  a smoothing constant 
B)  the explanatory variables 
C)  an alpha value 
D)  a span 
A)  a smoothing constant 
B)  the explanatory variables 
C)  an alpha value 
D)  a span D

Question 2

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 A

Question 3

Suppose that simple exponential smoothing with   is used to forecast monthly Pepsi sales at a small grocery store. After March's demand is observed, the forecasted demand for April is 5000 cans of Pepsi.
-At the beginning of April, what is the forecast of June's Pepsi sales?

Accepted Answer

Forecast for June is 5000 cans.

Question 4

If a random series has too few runs, then it is zigzagging too often.

Accepted Answer

A) True 
 B)False

Question 5

The data shown below contains the monthly sales (in thousands of dollars) at a local department store for each of the past 24 months.  
-(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. T

Question 6

What is a component of a time series?

Accepted Answer

A)  base series 
B)  trend 
C)  seasonal component 
D)  cyclic component 
E)  all of these choices 
A)  base series 
B)  trend 
C)  seasonal component 
D)  cyclic component 
E)  all of these choices

Question 7

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 8

If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component.

Accepted Answer

A) True 
 B)False

Question 9

In a multiplicative seasonal model, we multiply a "base" forecast by an appropriate seasonal index. These indexes, one for each season, typically average to 1.

Accepted Answer

A) True 
 B)False

Question 10

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 11

The quarterly numbers of applications for home mortgage loans at a branch office of a large bank are recorded in the table below.  
-Analysts are looking at data in select technology industries during the first decade of the century before forecasting possible trends in the second decade. Below is data for startups in one industry in the years 2005-2010.
​
(A) Perform a runs test and compute a few autocorrelations to determine whether this time series is random.
​
(B) Obtain a time series chart. Which of the exponential smoothing models do you think should be used for forecasting based on this chart? Why?
​
(C) Use simple exponential smoothing to forecast these data, using no holdout period and requesting 4 quarters of future forecasts. Use the default smoothing constant of 0.10.
​
(D) Repeat (C), optimizing the smoothing constant. Does it make much of an improvement?

Accepted Answer

(A) @#IMG-DLM& There is almost no evidence of nonr

Question 12

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.

Accepted Answer

A) True 
 B)False

Question 13

The runs test uses a series of 0's and 1's. The 0's and 1's typically 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 14

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 15

Winter's method is an exponential smoothing method, which is appropriate for a series with trend but no seasonality.

Accepted Answer

A) True 
 B)False

Question 16

The table below contains 5 years of monthly data on sales (number of units sold) for a particular company, in addition to extra columns containing information needed to answer some of the questions. The company suspects that except for random noise, its sales are growing by a constant percentage each month and that they will continue to do so for at least the near future.  
-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 c

Question 17

In a regression model with seasonal dummy variables, the coefficients on the dummy variables represent the additive factor relative to the reference quarter value, not the multiplicative factor.

Accepted Answer

A) True 
 B)False

Question 18

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

Accepted Answer

A) True 
 B)False

Question 19

A small sporting goods store is reviewing its sales data over the past decade to determine whether or not to open a new location. The data shown below contains total monthly retail sales (in dollars) for the years 2006-2008, the first three years of its business.  
-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

Question 20

Perform a runs test and compute a few autocorrelations to determine whether this time series is random.

Accepted Answer

@#IMG-DLM& The data are clearly nonrandom. The p-v

In a moving averages method, which of the following represent(s) the number of terms in the moving average?

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

Suppose that simple exponential smoothing with is used to forecast monthly Pepsi sales at a small grocery store. After March's demand is observed, the forecasted demand for April is 5000 cans of Pepsi. -At the beginning of April, what is the forecast of June's Pepsi sales?

If a random series has too few runs, then it is zigzagging too often.

What is a component of a time series?

Extrapolation methods attempt to:

If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component.

In a multiplicative seasonal model, we multiply a "base" forecast by an appropriate seasonal index. These indexes, one for each season, typically average to 1.

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

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

In a random walk model, the:

Winter's method is an exponential smoothing method, which is appropriate for a series with trend but no seasonality.

In a regression model with seasonal dummy variables, the coefficients on the dummy variables represent the additive factor relative to the reference quarter value, not the multiplicative factor.

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

Perform a runs test and compute a few autocorrelations to determine whether this time series is random.

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

In a moving averages method, which of the following represent(s) the number of terms in the moving average?

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

Suppose that simple exponential smoothing with is used to forecast monthly Pepsi sales at a small grocery store. After March's demand is observed, the forecasted demand for April is 5000 cans of Pepsi. -At the beginning of April, what is the forecast of June's Pepsi sales?

If a random series has too few runs, then it is zigzagging too often.

What is a component of a time series?

Extrapolation methods attempt to:

If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component.

In a multiplicative seasonal model, we multiply a "base" forecast by an appropriate seasonal index. These indexes, one for each season, typically average to 1.

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

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

In a random walk model, the:

Winter's method is an exponential smoothing method, which is appropriate for a series with trend but no seasonality.

In a regression model with seasonal dummy variables, the coefficients on the dummy variables represent the additive factor relative to the reference quarter value, not the multiplicative factor.

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

Perform a runs test and compute a few autocorrelations to determine whether this time series is random.

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