Question 1

A major drawback of the aggregate price index is that

Accepted Answer

A) It does not take into account the fact that some items in the market basket are purchased more frequently than others. 
B) It is difficult to compute. 
C) It is computed by using the values from a single time series or based on a single product. 
D) Percentage comparisons cannot be made to the base year. 
A) It does not take into account the fact that some items in the market basket are purchased more frequently than others. 
B) It is difficult to compute. 
C) It is computed by using the values from a single time series or based on a single product. 
D) Percentage comparisons cannot be made to the base year.

Question 2

Consider a time series with 15 quarterly sales observations.Using the quadratic trend model the following partial computer output was obtained.   Write the prediction equation.

Accepted Answer

y = 199.62

Question 3

Consider the following data:   
Calculate S0 using simple exponential smoothing and $\alpha$ = 2.

Accepted Answer

A) 19.5 
B) 19 
C) 18.6 
D) 19.25 
A) 19.5 
B) 19 
C) 18.6 
D) 19.25

Question 4

Weighting in exponential smoothing is accomplished by the use of ____.

Accepted Answer

A) first-order autocorrelation 
B) smoothing constants 
C) Durbin-Watson 
D) multiplicative decomposing 
A) first-order autocorrelation 
B) smoothing constants 
C) Durbin-Watson 
D) multiplicative decomposing

Question 5

The linear trend equation for the following data is     Find the residual value (error)for period 7.

Accepted Answer

A) 6.49 
B) -29.93 
C) 0.07 
D) -10.67 
A) 6.49 
B) -29.93 
C) 0.07 
D) -10.67

Question 6

Consider the regression equation   = 6.04 + 0.10(t)and the data below.   Compute the predicted value of sales for period 8.

Accepted Answer

A) 48.42 
B) 14.04 
C) 6.75 
D) 6.84 
A) 48.42 
B) 14.04 
C) 6.75 
D) 6.84

Question 7

Those fluctuations that are associated with climate,holidays and related activities are referred to as ___________ variations.

Accepted Answer

A) Trend 
B) Seasonal 
C) Cyclical 
D) Irregular 
A) Trend 
B) Seasonal 
C) Cyclical 
D) Irregular

Question 8

The no trend time series model is given by

Accepted Answer

A) TRt = B0 + B1t 
B) TRt = B0 
C) TRt = B0 + B1t + B2t2 
D) TRt = B0 + B1ln(t) 
A) TRt = B0 + B1t 
B) TRt = B0 
C) TRt = B0 + B1t + B2t2 
D) TRt = B0 + B1ln(t)

Question 9

In the Durbin-Watson test,if the calculated d-statistic is greater than the upper value of the d-statistic,then

Accepted Answer

A) We do not reject H0 which says the error terms are not autocorrelated 
B) We do reject H0 which says the error terms are not autocorrelated 
C) The test is inconclusive 
D) We do reject H0,which says the error terms are positively or negatively autocorrelated 
A) We do not reject H0 which says the error terms are not autocorrelated 
B) We do reject H0 which says the error terms are not autocorrelated 
C) The test is inconclusive 
D) We do reject H0,which says the error terms are positively or negatively autocorrelated

Question 10

The basic difference between MAD and MSE is that MSE,unlike MAD,penalizes a forecasting technique much more for _____ errors than for _____ errors.

Accepted Answer

A) large,small 
B) small,large 
C) small,zero 
D) zero,large 
A) large,small 
B) small,large 
C) small,zero 
D) zero,large

Question 11

Consider the following data and calculations.Calculate the estimated value of b1 and b0 and state the linear trend regression prediction equation.

Accepted Answer

A) 2.5 + 1.43t 
B) 2.86 + 2.5t 
C) 1.43 + 2.5t 
D) 4.00 + 1.43t 
A) 2.5 + 1.43t 
B) 2.86 + 2.5t 
C) 1.43 + 2.5t 
D) 4.00 + 1.43t

Question 12

Time series decomposition method would not be used to forecast seasonal data.

Accepted Answer

A) True 
 B)False

Question 13

When the magnitude of the seasonal swing does not depend on the level of a time series,we call this _________ variation.

Accepted Answer

A) Increasing seasonal 
B) Cyclical seasonal 
C) Constant seasonal 
D) Decreasing seasonal 
E) No seasonal 
A) Increasing seasonal 
B) Cyclical seasonal 
C) Constant seasonal 
D) Decreasing seasonal 
E) No seasonal

Question 14

Consider the following set of quarterly sales data given in thousands of dollars.   Write an appropriate dummy variable model that incorporates a linear trend and constant seasonal variation.

Accepted Answer

y (t)= B₀ + B_1t + B_Q1(Q1)+ B_Q2(Q2)+ B_Q3(Q3)+ E_t Th

Question 15

Cyclical variation exists when the magnitude of the seasonal swing does not depend on the level of a time series.

Accepted Answer

A) True 
 B)False

Question 16

Dummy variable regression would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Accepted Answer

A) True 
 B)False

Question 17

Holt - Winter's double exponential smoothing would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Accepted Answer

A) True 
 B)False

Question 18

Use this equation to forecast the demand for this product and calculate the MSD.

Accepted Answer

A) MSD = 6 
B) MSD = 3.3333 
C) MSD = 7.0 
D) MSD = 2 
E) MSD = 2.4 
A) MSD = 6 
B) MSD = 3.3333 
C) MSD = 7.0 
D) MSD = 2 
E) MSD = 2.4

Question 19

Consider the following data and calculate S1 using simple exponential smoothing and = 0.3.

Accepted Answer

A) 19.14 
B) 19.00 
C) 18.40 
D) 18.55 
A) 19.14 
B) 19.00 
C) 18.40 
D) 18.55

Question 20

The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales.The following partial computer output was obtained:   What is the predicted value of y when t = 17?

Accepted Answer

The answer of The linear regression trend model was applied...

A major drawback of the aggregate price index is that

Consider a time series with 15 quarterly sales observations.Using the quadratic trend model the following partial computer output was obtained. Write the prediction equation.

Consider the following data: $Consider the following data: Calculate S<sub>0</sub> using simple exponential smoothing and \alpha = 2.$ Calculate S₀ using simple exponential smoothing and $\alpha$ = 2.

Weighting in exponential smoothing is accomplished by the use of ____.

The linear trend equation for the following data is Find the residual value (error)for period 7.

Consider the regression equation = 6.04 + 0.10(t)and the data below. Compute the predicted value of sales for period 8.

Those fluctuations that are associated with climate,holidays and related activities are referred to as ___________ variations.

The no trend time series model is given by

In the Durbin-Watson test,if the calculated d-statistic is greater than the upper value of the d-statistic,then

The basic difference between MAD and MSE is that MSE,unlike MAD,penalizes a forecasting technique much more for _ errors than for _ errors.

Consider the following data and calculations.Calculate the estimated value of b₁ and b₀ and state the linear trend regression prediction equation.

Time series decomposition method would not be used to forecast seasonal data.

When the magnitude of the seasonal swing does not depend on the level of a time series,we call this _________ variation.

Consider the following set of quarterly sales data given in thousands of dollars. Write an appropriate dummy variable model that incorporates a linear trend and constant seasonal variation.

Cyclical variation exists when the magnitude of the seasonal swing does not depend on the level of a time series.

Dummy variable regression would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Holt - Winter's double exponential smoothing would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Use this equation to forecast the demand for this product and calculate the MSD.

Consider the following data and calculate S1 using simple exponential smoothing and = 0.3.

The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales.The following partial computer output was obtained: What is the predicted value of y when t = 17?

An Introduction to Business Statistics

Descriptive Statistics: Tabular and Graphical Methods

Descriptive Statistics: Numerical Methods

Probability

Discrete Random Variables

Continuous Random Variables

Sampling and Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Multiple Regression

Model Building and Model Diagnostics

Process Improvement Using Control Charts

Nonparametric Methods

Decision Theory

Filters

Exam 16: Time Series Forecasting

A major drawback of the aggregate price index is that

Consider a time series with 15 quarterly sales observations.Using the quadratic trend model the following partial computer output was obtained. Write the prediction equation.

Consider the following data: Calculate S0 using simple exponential smoothing and α\alphaα = 2.

Weighting in exponential smoothing is accomplished by the use of ____.

The linear trend equation for the following data is Find the residual value (error)for period 7.

Consider the regression equation = 6.04 + 0.10(t)and the data below. Compute the predicted value of sales for period 8.

Those fluctuations that are associated with climate,holidays and related activities are referred to as ___________ variations.

The no trend time series model is given by

In the Durbin-Watson test,if the calculated d-statistic is greater than the upper value of the d-statistic,then

The basic difference between MAD and MSE is that MSE,unlike MAD,penalizes a forecasting technique much more for _____ errors than for _____ errors.

Consider the following data and calculations.Calculate the estimated value of b1 and b0 and state the linear trend regression prediction equation.

Time series decomposition method would not be used to forecast seasonal data.

When the magnitude of the seasonal swing does not depend on the level of a time series,we call this _________ variation.

Consider the following set of quarterly sales data given in thousands of dollars. Write an appropriate dummy variable model that incorporates a linear trend and constant seasonal variation.

Cyclical variation exists when the magnitude of the seasonal swing does not depend on the level of a time series.

Dummy variable regression would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Holt - Winter's double exponential smoothing would be an appropriate method to use to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns.

Use this equation to forecast the demand for this product and calculate the MSD.

Consider the following data and calculate S1 using simple exponential smoothing and = 0.3.

The linear regression trend model was applied to a time series of sales data based on the last 16 months of sales.The following partial computer output was obtained: What is the predicted value of y when t = 17?

An Introduction to Business Statistics

Descriptive Statistics: Tabular and Graphical Methods

Descriptive Statistics: Numerical Methods

Probability

Discrete Random Variables

Continuous Random Variables

Sampling and Sampling Distributions

Confidence Intervals

Hypothesis Testing

Statistical Inferences Based on Two Samples

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Multiple Regression

Model Building and Model Diagnostics

Process Improvement Using Control Charts

Nonparametric Methods

Decision Theory

Filters

Consider the following data: $Consider the following data: Calculate S<sub>0</sub> using simple exponential smoothing and \alpha = 2.$ Calculate S₀ using simple exponential smoothing and $\alpha$ = 2.

The basic difference between MAD and MSE is that MSE,unlike MAD,penalizes a forecasting technique much more for _ errors than for _ errors.

Consider the following data and calculations.Calculate the estimated value of b₁ and b₀ and state the linear trend regression prediction equation.