Question 1

For causal modeling, _______________ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Accepted Answer

A) descriptive statistics on the data 
B) scatter charts 
C) contingency tables 
D) pie charts 
A) descriptive statistics on the data 
B) scatter charts 
C) contingency tables 
D) pie charts

Question 2

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean squared error. ?
? $$\begin{array} { | c | c | c | } 
\hline \text { Period } & \text { Actual Demand } & \text { Forecasted Demand } \
\hline 1 & 12 & - \
\hline 2 & 15 & 12 \
\hline 3 & 14 & 15 \
\hline 4 & 18 & 16 \
\hline
\end{array}$$

Accepted Answer

A) 3.33 
B) 4.67 
C) 5.33 
D) 6.67 
A) 3.33 
B) 4.67 
C) 5.33 
D) 6.67

Question 3

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

Accepted Answer

A) To maximize forecast accuracy measures 
B) To smooth out random fluctuations in the time series 
C) To characterize the variable fluctuations by an exponential equation 
D) To transform a nonstationary time series into a stationary series 
A) To maximize forecast accuracy measures 
B) To smooth out random fluctuations in the time series 
C) To characterize the variable fluctuations by an exponential equation 
D) To transform a nonstationary time series into a stationary series

Question 4

With reference to exponential forecasting models, a parameter that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the

Accepted Answer

A) moving average. 
B) regression coefficient. 
C) smoothing constant. 
D) mean forecast error. 
A) moving average. 
B) regression coefficient. 
C) smoothing constant. 
D) mean forecast error.

Question 5

The monthly sales revenue (in hundreds of dollars) of a company for one year is listed below. $$\begin{array} { | l | c | } 
\hline \text { Month } & \text { Sales } ( \mathbf { \$ 1 0 0 s } ) \
\hline \text { January } & 12,354 \
\hline \text { February } & 13,657 \
\hline \text { March } & 14,536 \
\hline \text { April } & 13,478 \
\hline \text { May } & 16,590 \
\hline \text { June } & 19,790 \
\hline \text { July } & 17,987 \
\hline \text { August } & 18,657 \
\hline \text { September } & 19,765 \
\hline \text { October } & 18,678 \
\hline \text { November } & 20,678 \
\hline \text { December } & 23,675 \
\hline
\end{array}$$ 
a. Compute MSE using the most recent value as the forecast for the next period. What is the forecast for the next month?
b. Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for the next month?
c. Which method appears to provide the better forecast?

Accepted Answer

a. @#IMG-DLM& MSE = 42605309/11 = 3873209.91 » 387

Question 6

The following time series shows the sales of a particular commodity over the past 15 weeks. $$\begin{array} { | c | c | } 
\hline \text { Week } & \text { Sales } \
\hline 1 & 1,123 \
\hline 2 & 1,157 \
\hline 3 & 1,138 \
\hline 4 & 1,120 \
\hline 5 & 1,130 \
\hline 6 & 1,132 \
\hline 7 & 1,188 \
\hline 8 & 1,151 \
\hline 9 & 1,129 \
\hline 10 & 1,118 \
\hline 11 & 1,125 \
\hline 12 & 1,147 \
\hline 13 & 1,162 \
\hline 14 & 1,190 \
\hline 15 & 1,137 \
\hline
\end{array}$$ 
a. Construct a time series plot. What type of pattern exists in the data?
b. Use α = 0.3 to develop the exponential smoothing values for the time series and compute the forecast of demand for the next week.
c. Use trial and error to find a value of the exponential smoothing coefficient α that results in a relatively small MSE.

Accepted Answer

a. @#IMG-DLM& The time series plot shows a horizon

Question 7

The below time series gives the indices of Industrial Production in U.S for 10 consecutive years. $$\begin{array} { | c | c | } 
\hline \text { Year } & \text { IP } \
\hline 1 & 79.62 \
\hline 2 & 86.54 \
\hline 3 & 88.14 \
\hline 4 & 89.23 \
\hline 5 & 93.45 \
\hline 6 & 97.4 \
\hline 7 & 99.34 \
\hline 8 & 96.98 \
\hline 9 & 100.22 \
\hline 10 & 103.56 \
\hline
\end{array}$$ 
a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the forecast for t = 11?

Accepted Answer

a. @#IMG-DLM& The time series plot shows a linear

Question 8

The yearly sales (in millions of dollars) of an automobile manufacturing company during the period 2000-2011 are given below. $$\begin{array} { | c | c | } 
\hline \text { Year } & \text { Sales (\$millions) } \boldsymbol { y } \
\hline 2000 & 470 \
\hline 2001 & 485 \
\hline 2002 & 499 \
\hline 2003 & 515 \
\hline 2004 & 532 \
\hline 2005 & 532 \
\hline 2006 & 556 \
\hline 2007 & 576 \
\hline 2008 & 583 \
\hline 2009 & 587 \
\hline 2010 & 601 \
\hline 2011 & 605 \
\hline
\end{array}$$ 
a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the sales forecast (in millions of dollars) for the year 2012?

Accepted Answer

a. @#IMG-DLM& The time series plot shows a linear

Question 9

The process of ____________ might be used to determine the value of smoothing constant that minimizes the mean squared error.

Accepted Answer

A) quantization 
B) nonlinear optimization 
C) clustering 
D) curve fitting 
A) quantization 
B) nonlinear optimization 
C) clustering 
D) curve fitting

Question 10

With reference to time series data patterns, a cyclical pattern is the component of the time series that

Accepted Answer

A) shows a periodic pattern lasting one year or less. 
B) does not vary with respect to time. 
C) shows a periodic pattern lasting more than one year. 
D) is characterized by a linear variation of the dependent variable with respect to time. 
A) shows a periodic pattern lasting one year or less. 
B) does not vary with respect to time. 
C) shows a periodic pattern lasting more than one year. 
D) is characterized by a linear variation of the dependent variable with respect to time.

Question 11

Autoregressive models

Accepted Answer

A) use the average of the most recent data values in the time series as the forecast for the next period. 
B) are used to smooth out random fluctuations in time series. 
C) relate a time series to other variables that are believed to explain or cause its behavior. 
D) occur whenever all the independent variables are previous values of the time series. 
A) use the average of the most recent data values in the time series as the forecast for the next period. 
B) are used to smooth out random fluctuations in time series. 
C) relate a time series to other variables that are believed to explain or cause its behavior. 
D) occur whenever all the independent variables are previous values of the time series.

Question 12

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a

Accepted Answer

A) geometric series. 
B) time invariant set. 
C) time series. 
D) logarithmic series. 
A) geometric series. 
B) time invariant set. 
C) time series. 
D) logarithmic series.

Question 13

A time series plot of a period of time (in years) verses revenue (in millions of dollars) is shown below .Which of the following data patterns best describes the scenario shown?

Accepted Answer

A) Linear trend pattern 
B) Nonlinear trend pattern 
C) Seasonal pattern 
D) Cyclical pattern 
A) Linear trend pattern 
B) Nonlinear trend pattern 
C) Seasonal pattern 
D) Cyclical pattern

Question 14

The moving averages method refers to a forecasting method that

Accepted Answer

A) is used when considerable trend, cyclical, or seasonal effects are present. 
B) uses regression relationship based on past time series values to predict the future time series values. 
C) relates a time series to other variables that are believed to explain or cause its behavior. 
D) uses the average of the most recent data values in the time series as the forecast for the next period. 
A) is used when considerable trend, cyclical, or seasonal effects are present. 
B) uses regression relationship based on past time series values to predict the future time series values. 
C) relates a time series to other variables that are believed to explain or cause its behavior. 
D) uses the average of the most recent data values in the time series as the forecast for the next period.

Question 15

The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast.These methods measure forecast accuracy by

Accepted Answer

A) determining how well a particular forecasting method is able to reproduce the time series data that are already available. 
B) using the current value to estimate how well the model generates previous values correctly. 
C) predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were. 
D) adjusting the scale of the data. 
A) determining how well a particular forecasting method is able to reproduce the time series data that are already available. 
B) using the current value to estimate how well the model generates previous values correctly. 
C) predicting the future values and wait for a pre-defined time period to examine how accurate the predictions were. 
D) adjusting the scale of the data.

Question 16

A time series plot of a period of time period (quarterly) verses quarterly sales (in $1,000s) is shown below. Which of the following data patterns best describes the scenario shown?

Accepted Answer

A) Linear trend and cyclical pattern 
B) Linear trend and horizontal pattern 
C) Seasonal and cyclical patterns 
D) Seasonal pattern and linear trend 
A) Linear trend and cyclical pattern 
B) Linear trend and horizontal pattern 
C) Seasonal and cyclical patterns 
D) Seasonal pattern and linear trend

Question 17

Suppose for a particular week, the forecasted sales were $4,000. The actual sales were $3,000. What is the value of the mean absolute percentage error?

Accepted Answer

A) -33.3% 
B) -25% 
C) 25% 
D) 33.3% 
A) -33.3% 
B) -25% 
C) 25% 
D) 33.3%

Question 18

____________________ is the amount by which the predicted value differs from the observed value of the time series variable.

Accepted Answer

A) Mean forecast error 
B) Mean absolute error 
C) Smoothing constant 
D) Forecast error 
A) Mean forecast error 
B) Mean absolute error 
C) Smoothing constant 
D) Forecast error

For causal modeling, _______________ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean squared error. ? ? Period Actual Demand Forecasted Demand 1 12 - 2 15 12 3 14 15 4 18 16

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

With reference to exponential forecasting models, a parameter that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the

The process of ____________ might be used to determine the value of smoothing constant that minimizes the mean squared error.

With reference to time series data patterns, a cyclical pattern is the component of the time series that

Autoregressive models

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a

A time series plot of a period of time (in years) verses revenue (in millions of dollars) is shown below .Which of the following data patterns best describes the scenario shown?

The moving averages method refers to a forecasting method that

The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast.These methods measure forecast accuracy by

A time series plot of a period of time period (quarterly) verses quarterly sales (in $1,000s) is shown below. Which of the following data patterns best describes the scenario shown?

Suppose for a particular week, the forecasted sales were $4,000. The actual sales were $3,000. What is the value of the mean absolute percentage error?

____________________ is the amount by which the predicted value differs from the observed value of the time series variable.

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Predictive Data Mining

Spreadsheet Models

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Monte Carlo Simulation

Decision Analysis

Filters

Exam 8: Time Series Analysis and Forecasting

For causal modeling, _______________ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Demand for a product and the forecasting department's forecast (naïve model) for a product are shown below. Compute the mean squared error. ? ? Period Actual Demand Forecasted Demand 1 12 - 2 15 12 3 14 15 4 18 16

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

With reference to exponential forecasting models, a parameter that provides the weight given to the most recent time series value in the calculation of the forecast value is known as the

The process of ____________ might be used to determine the value of smoothing constant that minimizes the mean squared error.

With reference to time series data patterns, a cyclical pattern is the component of the time series that

Autoregressive models

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a

A time series plot of a period of time (in years) verses revenue (in millions of dollars) is shown below .Which of the following data patterns best describes the scenario shown?

The moving averages method refers to a forecasting method that

The mean absolute error, mean squared error, and mean absolute percentage error are all methods to measure the accuracy of a forecast.These methods measure forecast accuracy by

A time series plot of a period of time period (quarterly) verses quarterly sales (in $1,000s) is shown below. Which of the following data patterns best describes the scenario shown?

Suppose for a particular week, the forecasted sales were $4,000. The actual sales were $3,000. What is the value of the mean absolute percentage error?

____________________ is the amount by which the predicted value differs from the observed value of the time series variable.

Introduction

Descriptive Statistics

Data Visualization

Descriptive Data Mining

Probability: an Introduction to Modeling Uncertainty

Statistical Inference

Linear Regression

Predictive Data Mining

Spreadsheet Models

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Monte Carlo Simulation

Decision Analysis

Filters