Question 1

When constructing a weighted aggregate price index,the weights usually are _____.

Accepted Answer

A) prices of substitute items 
B) prices of complementary items 
C) quantities of the respective items 
D) squared quantities of the respective items 
E) quality of individual items 
A) prices of substitute items 
B) prices of complementary items 
C) quantities of the respective items 
D) squared quantities of the respective items 
E) quality of individual items

Question 2

Using 2000 as the base year,the 1990 value of the Paasche' Price Index is ______.(Quantities are averages for the student body.)

Accepted Answer

A) 80.72 
B) 162.28 
C) 240.06 
D) 50.45 
E) 30.35 
A) 80.72 
B) 162.28 
C) 240.06 
D) 50.45 
E) 30.35

Question 3

Using a three-month moving average (with weights of 6,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be__________.

Accepted Answer

A) 11.60 
B) 10.00 
C) 9.67 
D) 8.60 
E) 6.11 
A) 11.60 
B) 10.00 
C) 9.67 
D) 8.60 
E) 6.11

Question 4

A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'.The actual number of new customers,the forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________.

Accepted Answer

A) -0.50 
B) 0.50 
C) 1.50 
D) 7.00 
E) 3.00 
A) -0.50 
B) 0.50 
C) 1.50 
D) 7.00 
E) 3.00

Question 5

The forecast value for August was 22 and the actual value turned out to be 19.Using exponential smoothing with $\alpha$ = 0.30,the forecast value for September would be ______.

Accepted Answer

A) 21.1 
B) 19.9 
C) 18.1 
D) 22.9 
E) 21.0 
A) 21.1 
B) 19.9 
C) 18.1 
D) 22.9 
E) 21.0

Question 6

Analysis of data for an autoregressive forecasting model produced the following tables.     The forecasting model is __________.

Accepted Answer

A) yt = 3.745787 + 0.082849yt-1 + 0.035709yt-2 
B) yt = 3.85094 + 0.70434yt-1 - 0.62669yt-2 
C) yt = 0.84426 - 1.66023yt-1 + 14.65023yt-2 
D) yt = 0.34299 + 0.13822yt-1 + 9.69yt-2 
E) yt = 0.34299 + 0.13822yt-1 - 6.69yt-2 
A) yt = 3.745787 + 0.082849yt-1 + 0.035709yt-2 
B) yt = 3.85094 + 0.70434yt-1 - 0.62669yt-2 
C) yt = 0.84426 - 1.66023yt-1 + 14.65023yt-2 
D) yt = 0.34299 + 0.13822yt-1 + 9.69yt-2 
E) yt = 0.34299 + 0.13822yt-1 - 6.69yt-2

Question 7

Which of the following is not a component of time series data?

Accepted Answer

A) Trend 
B) Seasonal fluctuations 
C) Cyclical fluctuations 
D) Normal fluctuations 
E) Irregular fluctuations 
A) Trend 
B) Seasonal fluctuations 
C) Cyclical fluctuations 
D) Normal fluctuations 
E) Irregular fluctuations

Question 8

The motivation for using an index number is to ________________.

Accepted Answer

A) transform the data to a standard normal distribution 
B) transform the data for a linear model 
C) eliminate bias from the sample 
D) reduce data to an easier-to-use,more convenient form 
E) reduce the variance in the data 
A) transform the data to a standard normal distribution 
B) transform the data for a linear model 
C) eliminate bias from the sample 
D) reduce data to an easier-to-use,more convenient form 
E) reduce the variance in the data

Question 9

Naïve forecasting models have no useful applications because they do not take into account data trend,cyclical effects or seasonality.

Accepted Answer

A) True 
 B)False

Question 10

Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables.     
Using $\alpha$ = 0.05 the critical value of the Durbin-Watson statistic,dL,is _________.

Accepted Answer

A) 1.24 
B) 1.22 
C) 1.13 
D) 1.15 
E) 1.85 
A) 1.24 
B) 1.22 
C) 1.13 
D) 1.15 
E) 1.85

Question 11

For large datasets,the mean error (ME)and mean absolute deviation (MAD)always have the same numerical value.

Accepted Answer

A) True 
 B)False

Question 12

If autocorrelation occurs in regression analysis,then the confidence intervals and tests using the t and F distributions are no longer strictly applicable.

Accepted Answer

A) True 
 B)False

Question 13

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-differences.

Accepted Answer

A) True 
 B)False

Question 14

Jim Royo,manager of Billings Building Supply (BBS),wants to develop a model to forecast BBS's monthly sales (in $1,000's).He selects the dollar value of residential building permits (in $10,000)as the predictor variable.An analysis of the data yielded the following tables.    
 Using $\alpha$ = 0.05 the critical value of the Durbin-Watson statistic,dU,is _________.

Accepted Answer

A) 1.54 
B) 1.42 
C) 1.43 
D) 1.44 
E) 1.85 
A) 1.54 
B) 1.42 
C) 1.43 
D) 1.44 
E) 1.85

Question 15

Two popular general categories of smoothing techniques are exponential models and logarithmic models.

Accepted Answer

A) True 
 B)False

Question 16

A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'.The actual number of new customers,the forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___________.

Accepted Answer

A) -0.50 
B) 0.50 
C) 1.50 
D) 7.00 
E) 3.00 
A) -0.50 
B) 0.50 
C) 1.50 
D) 7.00 
E) 3.00

Question 17

Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___________.

Accepted Answer

A) the actual value for the current period 
B) the actual value for the previous period 
C) the forecast for the current period 
D) the forecast for the previous period 
E) the forecast for the next period 
A) the actual value for the current period 
B) the actual value for the previous period 
C) the forecast for the current period 
D) the forecast for the previous period 
E) the forecast for the next period

Question 18

The city golf course is interested in starting a junior golf program.The golf pro has collected data on the number of youths under 13 that have played golf during the last 4 months.Using a three-month moving average (with weights of 5,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be __________.

Accepted Answer

A) 24 
B) 21 
C) 21.56 
D) 19.22 
E) 22 
A) 24 
B) 21 
C) 21.56 
D) 19.22 
E) 22

Question 19

A time series with forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________.

Accepted Answer

A) 3.54 
B) 7.41 
C) 4.43 
D) 17.72 
E) 4.34 
A) 3.54 
B) 7.41 
C) 4.43 
D) 17.72 
E) 4.34

Question 20

Although seasonal effects can confound a trend analysis,a regression model is robust to these effects and the researcher does not need to adjust for seasonality prior to using a regression model to analyze trends.

Accepted Answer

A) True 
 B)False

When constructing a weighted aggregate price index,the weights usually are _____.

Using 2000 as the base year,the 1990 value of the Paasche' Price Index is ______.(Quantities are averages for the student body.)

Using a three-month moving average (with weights of 6,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be__________.

A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'.The actual number of new customers,the forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________.

The forecast value for August was 22 and the actual value turned out to be 19.Using exponential smoothing with $\alpha$ = 0.30,the forecast value for September would be ______.

Analysis of data for an autoregressive forecasting model produced the following tables. The forecasting model is __________.

Which of the following is not a component of time series data?

The motivation for using an index number is to ________________.

Naïve forecasting models have no useful applications because they do not take into account data trend,cyclical effects or seasonality.

For large datasets,the mean error (ME)and mean absolute deviation (MAD)always have the same numerical value.

If autocorrelation occurs in regression analysis,then the confidence intervals and tests using the t and F distributions are no longer strictly applicable.

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-differences.

Two popular general categories of smoothing techniques are exponential models and logarithmic models.

A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'.The actual number of new customers,the forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___________.

Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___________.

A time series with forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________.

Although seasonal effects can confound a trend analysis,a regression model is robust to these effects and the researcher does not need to adjust for seasonality prior to using a regression model to analyze trends.

Introduction to Statistics

Charts and Graphs

Descriptive Statistics

Probability

Discrete Distributions

Continuous Distributions

Sampling and Sampling Distributions

Statistical Inference: Estimation for Single Populations

Statistical Inference: Hypothesis Testing for Single Populations

Statistical Inferences About Two Populations

Analysis of Variance and Design of Experiments

Simple Regression Analysis and Correlation

Multiple Regression Analysis

Building Multiple Regression Models

Analysis of Categorical Data

Nonparametric Statistics

Statistical Quality Control

Decision Analysis

Filters

Exam 15: Time-Series Forecasting and Index Numbers

When constructing a weighted aggregate price index,the weights usually are _____.

Using 2000 as the base year,the 1990 value of the Paasche' Price Index is ______.(Quantities are averages for the student body.)

Using a three-month moving average (with weights of 6,3,and 1 for the most current value,next most current value and oldest value,respectively),the forecast value for October made at the end of September in the following time series would be__________.

The forecast value for August was 22 and the actual value turned out to be 19.Using exponential smoothing with α\alphaα = 0.30,the forecast value for September would be ______.

Analysis of data for an autoregressive forecasting model produced the following tables. The forecasting model is __________.

Which of the following is not a component of time series data?

The motivation for using an index number is to ________________.

Naïve forecasting models have no useful applications because they do not take into account data trend,cyclical effects or seasonality.

For large datasets,the mean error (ME)and mean absolute deviation (MAD)always have the same numerical value.

If autocorrelation occurs in regression analysis,then the confidence intervals and tests using the t and F distributions are no longer strictly applicable.

One of the ways to overcome the autocorrelation problem in a regression forecasting model is to transform the variables by taking the first-differences.

Two popular general categories of smoothing techniques are exponential models and logarithmic models.

A time series analysis was performed to determine the number of new online customers that joined the 'Jelly of the Month Club'.The actual number of new customers,the forecast values and error terms is presented in the following table.The mean error (ME)for this forecast is ___________.

Use of a smoothing constant value greater than 0.5 in an exponential smoothing model gives more weight to ___________.

A time series with forecast values and error terms is presented in the following table.The mean absolute deviation (MAD)for this forecast is ___________.

Although seasonal effects can confound a trend analysis,a regression model is robust to these effects and the researcher does not need to adjust for seasonality prior to using a regression model to analyze trends.

Introduction to Statistics

Charts and Graphs

Descriptive Statistics

Probability

Discrete Distributions

Continuous Distributions

Sampling and Sampling Distributions

Statistical Inference: Estimation for Single Populations

Statistical Inference: Hypothesis Testing for Single Populations

Statistical Inferences About Two Populations

Analysis of Variance and Design of Experiments

Simple Regression Analysis and Correlation

Multiple Regression Analysis

Building Multiple Regression Models

Analysis of Categorical Data

Nonparametric Statistics

Statistical Quality Control

Decision Analysis

Filters

The forecast value for August was 22 and the actual value turned out to be 19.Using exponential smoothing with $\alpha$ = 0.30,the forecast value for September would be ______.