Question 1

The Consumer Price Index and the Producer Price Index are both calculated using the _________ index formula.

Accepted Answer

A) Paasche 
B) Weighted aggregate 
C) Laspeyres 
D) Cyclical (seasonal) 
A) Paasche 
B) Weighted aggregate 
C) Laspeyres 
D) Cyclical (seasonal) C

Question 2

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

Accepted Answer

14.5,17.5,18.5,22,23.5,24.5,26.25,27.25,29.5

Question 3

The _______ component of time series consists of erratic and unsystematic fluctuations in a time series data.

Accepted Answer

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

Question 4

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.   Calculate the ratio of actual production to the centered moving average values (snt * irt)for the entire time series.

Accepted Answer

1.00,.944,1.037,.879,1.25,.906,.935,.951

Question 5

Seasonal variations are periodic patterns in a time series that are repeated over time.For which one of the following time series variables,it is not possible to recognize seasonal variations?

Accepted Answer

A) Quarters of the year 
B) Months of the year 
C) Days of the week 
D) Hours of the day 
E) Years 
A) Quarters of the year 
B) Months of the year 
C) Days of the week 
D) Hours of the day 
E) Years

Question 6

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

The answer of Two forecasting models were used to predict...

Question 7

Given the following data   Compute the mean squared error (deviation).

Accepted Answer

A) 225 
B) 28.1 
C) 496.9 
D) 3975 
A) 225 
B) 28.1 
C) 496.9 
D) 3975

Question 8

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values:   Which model is the most accurate? Why?

Accepted Answer

Model 2 is most accu

Question 9

Which of the following time-series forecasting methods would not be used to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns?

Accepted Answer

A) Dummy variable regression 
B) Linear trend regression 
C) Holt Winter's double exponential smoothing 
D) Multiplicative Winter's method 
E) Both A and D 
A) Dummy variable regression 
B) Linear trend regression 
C) Holt Winter's double exponential smoothing 
D) Multiplicative Winter's method 
E) Both A and D

Question 10

Given the following data   Compute the mean squared deviation (error).

Accepted Answer

A) 100 
B) 125 
C) 750 
D) 16.7 
A) 100 
B) 125 
C) 750 
D) 16.7

Question 11

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

Accepted Answer

A) MAD = 1.333 
B) MAD = 1.6 
C) MAD = 2.0 
D) MAD = 2.333 
E) MAD = 2.5 
A) MAD = 1.333 
B) MAD = 1.6 
C) MAD = 2.0 
D) MAD = 2.333 
E) MAD = 2.5

Question 12

Given the following data   Compute the mean absolute deviation.

Accepted Answer

The answer of Given the following data   Compute...

Question 13

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

Accepted Answer

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

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 absolute deviation (MAD)for Model 1.

Accepted Answer

The answer of Two forecasting models were used to predict...

Question 15

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 absolute deviation (MAD)for Model 2.

Accepted Answer

The answer of Two forecasting models were used to predict...

Question 16

Removing the seasonal affect by dividing the actual time series observation by the estimated seasonal factor associated with the time series observation is called deseasonalization.

Accepted Answer

A) True 
 B)False

Question 17

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below.The normalized (adjusted)seasonal factors are .9982,.9263,1.139,.9365 for winter,spring,summer and fall respectively.   Based on the following deseasonalized observations (dt)given below,a trend line was estimated.The linear regression trend equation is: trt = 10.1 + 1.91 (t).Based on this trend equation,the following trend values are calculated for each period in the time series:   Isolate the cyclical and irregular components by calculating the estimate of CLt* IRt for the first four quarters in the time series.

Accepted Answer

.75,1.242,

Question 18

A forecasting method that weights recent observations more heavily is called ____.

Accepted Answer

A) time series analysis 
B) first-order autocorrelation 
C) multiplicative decomposition 
D) exponential smoothing 
A) time series analysis 
B) first-order autocorrelation 
C) multiplicative decomposition 
D) exponential smoothing

Question 19

Use the following information for the three grains.   Calculate the Paasche index.

Accepted Answer

The answer of Use the following information for the three...

Question 20

Listed below are the price of a pair of men's boots over a 50 year time period.   Find the simple index numbers for the data with 1950 as the base year.

Accepted Answer

100.0,144.

The Consumer Price Index and the Producer Price Index are both calculated using the _________ index formula.

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

The _______ component of time series consists of erratic and unsystematic fluctuations in a time series data.

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below. Calculate the ratio of actual production to the centered moving average values (sn_t * ir_t)for the entire time series.

Seasonal variations are periodic patterns in a time series that are repeated over time.For which one of the following time series variables,it is not possible to recognize seasonal variations?

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

Given the following data Compute the mean squared error (deviation).

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: Which model is the most accurate? Why?

Which of the following time-series forecasting methods would not be used to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns?

Given the following data Compute the mean squared deviation (error).

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

Given the following data Compute the mean absolute deviation.

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

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 absolute deviation (MAD)for Model 1.

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 absolute deviation (MAD)for Model 2.

Removing the seasonal affect by dividing the actual time series observation by the estimated seasonal factor associated with the time series observation is called deseasonalization.

A forecasting method that weights recent observations more heavily is called ____.

Use the following information for the three grains. Calculate the Paasche index.

Listed below are the price of a pair of men's boots over a 50 year time period. Find the simple index numbers for the data with 1950 as the base year.

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

The Consumer Price Index and the Producer Price Index are both calculated using the _________ index formula.

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

The _______ component of time series consists of erratic and unsystematic fluctuations in a time series data.

Consider the quarterly production data (in thousands of units)for the XYZ manufacturing company below. Calculate the ratio of actual production to the centered moving average values (snt * irt)for the entire time series.

Seasonal variations are periodic patterns in a time series that are repeated over time.For which one of the following time series variables,it is not possible to recognize seasonal variations?

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

Given the following data Compute the mean squared error (deviation).

Two forecasting models were used to predict the future values of a time series.The forecasts are shown below with the actual values: Which model is the most accurate? Why?

Which of the following time-series forecasting methods would not be used to forecast a time series that exhibits a linear trend with no seasonal or cyclical patterns?

Given the following data Compute the mean squared deviation (error).

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

Given the following data Compute the mean absolute deviation.

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

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 absolute deviation (MAD)for Model 1.

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 absolute deviation (MAD)for Model 2.

Removing the seasonal affect by dividing the actual time series observation by the estimated seasonal factor associated with the time series observation is called deseasonalization.

A forecasting method that weights recent observations more heavily is called ____.

Use the following information for the three grains. Calculate the Paasche index.

Listed below are the price of a pair of men's boots over a 50 year time period. Find the simple index numbers for the data with 1950 as the base year.

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 quarterly production data (in thousands of units)for the XYZ manufacturing company below. Calculate the ratio of actual production to the centered moving average values (sn_t * ir_t)for the entire time series.