Question 1

The following data on prices and quantities for the years 1995 and 2000 are given for three products.   Calculate the 2000 aggregate price index.

Accepted Answer

The answer of The following data on prices and quantities...

Question 2

A simple index is obtained by dividing the current value of a time series by the value of a time series in the _____________ time period and by multiplying this ratio by 100.

Accepted Answer

A)  base 
B)  final 
C)  current 
D)  shortest 
A)  base 
B)  final 
C)  current 
D)  shortest

Question 3

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean absolute deviation?

Accepted Answer

A)  9 
B)  0 
C)  3 
D)  −3 
A)  9 
B)  0 
C)  3 
D)  −3

Question 4

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean squared error?

Accepted Answer

A)  9 
B)  0 
C)  3 
D)  −3 
E)  2 
A)  9 
B)  0 
C)  3 
D)  −3 
E)  2

Question 5

In the multiplicative decomposition method,the centered moving averages provide an estimate of a trend's ____________.

Accepted Answer

A)  index 
B)  cycle 
C)  seasonal variation 
D)  irregular variation 
A)  index 
B)  cycle 
C)  seasonal variation 
D)  irregular variation

Question 6

All of the following are forecasting methods except

Accepted Answer

A)  Holt-Winters double exponential smoothing. 
B)  simple exponential smoothing. 
C)  time series regression. 
D)  MAD autocorrelation. 
A)  Holt-Winters double exponential smoothing. 
B)  simple exponential smoothing. 
C)  time series regression. 
D)  MAD autocorrelation.

Question 7

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values.   Calculate the mean squared deviation (MSD)for Model 1.

Accepted Answer

MSD₁ = 1.84

Question 8

If a time series exhibits increasing seasonal variation,one approach is to first use a(n)______________ transformation that produces a transformed time series that exhibits constant seasonal variation.Then,_________ variables can be used to model the time series with constant seasonal variation.

Accepted Answer

A)  autocorrelation,dummy 
B)  fractional power,dummy 
C)  fractional power,constant 
D)  autocorrelation,constant 
A)  autocorrelation,dummy 
B)  fractional power,dummy 
C)  fractional power,constant 
D)  autocorrelation,constant

Question 9

Based on the following data,a forecaster used simple exponential smoothing and determined the following: S0 = 19,S1 = 18.6,S2 = 19.08,S3 = 19.064,S4 = 19.851,and S5 = 19.481.   Calculate the Mean Squared Deviation (MSD or MSE).

Accepted Answer

MSD = 3.67

Question 10

The smoothing constant is a number that determines how much weight is attached to each observation.

Accepted Answer

A) True 
 B)False

Question 11

Multiplicative decompositions assumes that time series components remain essentially constant over time.

Accepted Answer

A) True 
 B)False

Question 12

The Box-Jenkins methodology can be used to identify what is called an autoregressive-moving average model.

Accepted Answer

A) True 
 B)False

Question 13

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 14

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values.   Calculate the mean squared deviation (MSD)for Model 2

Accepted Answer

MSD₂ = .937

Question 15

Simple exponential smoothing is a forecasting method that applies equal weights to the time series observations.

Accepted Answer

A) True 
 B)False

Question 16

Consider the following set of quarterly sales data,given in thousands of dollars.   The following dummy variable model that incorporates a linear trend and constant seasonal variation was used: y(t)= B0 + B1t + BQ1(Q1)+ BQ2(Q2)+ BQ3(Q3)+ Et.In this model,there are 3 binary seasonal variables (Q1,Q2,and Q3),where Qi is a binary (0,1)variable defined as:
Qi = 1,if the time series data is associated with quarter i;
Qi = 0,if the time series data is not associated with quarter i.The results associated with this data and model are given in the following Minitab computer output.The regression equation is
Sales = 2442 + 6.2Time − 693Q1 − 1499Q2 + 153Q3   Provide a managerial interpretation of the regression coefficients for the variables Q1 (quarter 1),Q2 (quarter 2),and Q3 (quarter 3).

Accepted Answer

Excluding trend,estimated sales in quart

Question 17

A sequence of values of some variable or composite of variables taken at successive,uninterrupted time periods is called a

Accepted Answer

A)  least squares (linear)trend line. 
B)  moving average. 
C)  cyclical component. 
D)  time series. 
E)  seasonal factor. 
A)  least squares (linear)trend line. 
B)  moving average. 
C)  cyclical component. 
D)  time series. 
E)  seasonal factor.

Question 18

Given the following data,compute the mean squared error (deviation).

Accepted Answer

MSD = 659.

Question 19

Simple exponential smoothing is an appropriate method for prediction purposes when there is a significant trend present in a time series.

Accepted Answer

A) True 
 B)False

Question 20

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.   Calculate the 4-period (quarter)centered moving average for the entire time series.

Accepted Answer

16,18,20.2

The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the 2000 aggregate price index.

A simple index is obtained by dividing the current value of a time series by the value of a time series in the _____________ time period and by multiplying this ratio by 100.

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean absolute deviation?

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean squared error?

In the multiplicative decomposition method,the centered moving averages provide an estimate of a trend's ____________.

All of the following are forecasting methods except

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD)for Model 1.

If a time series exhibits increasing seasonal variation,one approach is to first use a(n)______ transformation that produces a transformed time series that exhibits constant seasonal variation.Then,_ variables can be used to model the time series with constant seasonal variation.

Based on the following data,a forecaster used simple exponential smoothing and determined the following: S₀ = 19,S₁ = 18.6,S₂ = 19.08,S₃ = 19.064,S₄ = 19.851,and S₅ = 19.481. Calculate the Mean Squared Deviation (MSD or MSE).

The smoothing constant is a number that determines how much weight is attached to each observation.

Multiplicative decompositions assumes that time series components remain essentially constant over time.

The Box-Jenkins methodology can be used to identify what is called an autoregressive-moving average model.

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.

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD)for Model 2

Simple exponential smoothing is a forecasting method that applies equal weights to the time series observations.

A sequence of values of some variable or composite of variables taken at successive,uninterrupted time periods is called a

Given the following data,compute the mean squared error (deviation).

Simple exponential smoothing is an appropriate method for prediction purposes when there is a significant trend present in a time series.

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below. Calculate the 4-period (quarter)centered moving average for the entire time series.

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 for Means and Proportions

Statistical Inferences for Population Variances

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters

Exam 15: Multiple Regression and Model Building

The following data on prices and quantities for the years 1995 and 2000 are given for three products. Calculate the 2000 aggregate price index.

A simple index is obtained by dividing the current value of a time series by the value of a time series in the _____________ time period and by multiplying this ratio by 100.

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean absolute deviation?

If the errors produced by a forecasting method for 3 observations are +3,+3,and −3,then what is the mean squared error?

In the multiplicative decomposition method,the centered moving averages provide an estimate of a trend's ____________.

All of the following are forecasting methods except

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD)for Model 1.

If a time series exhibits increasing seasonal variation,one approach is to first use a(n)______________ transformation that produces a transformed time series that exhibits constant seasonal variation.Then,_________ variables can be used to model the time series with constant seasonal variation.

Based on the following data,a forecaster used simple exponential smoothing and determined the following: S0 = 19,S1 = 18.6,S2 = 19.08,S3 = 19.064,S4 = 19.851,and S5 = 19.481. Calculate the Mean Squared Deviation (MSD or MSE).

The smoothing constant is a number that determines how much weight is attached to each observation.

Multiplicative decompositions assumes that time series components remain essentially constant over time.

The Box-Jenkins methodology can be used to identify what is called an autoregressive-moving average model.

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.

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values. Calculate the mean squared deviation (MSD)for Model 2

Simple exponential smoothing is a forecasting method that applies equal weights to the time series observations.

A sequence of values of some variable or composite of variables taken at successive,uninterrupted time periods is called a

Given the following data,compute the mean squared error (deviation).

Simple exponential smoothing is an appropriate method for prediction purposes when there is a significant trend present in a time series.

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below. Calculate the 4-period (quarter)centered moving average for the entire time series.

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 for Means and Proportions

Statistical Inferences for Population Variances

Experimental Design and Analysis of Variance

Chi-Square Tests

Simple Linear Regression Analysis

Time Series Forecasting and Index Numbers

Nonparametric Methods

Decision Theory

Filters

If a time series exhibits increasing seasonal variation,one approach is to first use a(n)______ transformation that produces a transformed time series that exhibits constant seasonal variation.Then,_ variables can be used to model the time series with constant seasonal variation.

Based on the following data,a forecaster used simple exponential smoothing and determined the following: S₀ = 19,S₁ = 18.6,S₂ = 19.08,S₃ = 19.064,S₄ = 19.851,and S₅ = 19.481. Calculate the Mean Squared Deviation (MSD or MSE).