Question 1

Index numbers facilitate comparison of ___.

Accepted Answer

A) means 
B) data over time 
C) variances 
D) samples 
E) deviations 
A) means 
B) data over time 
C) variances 
D) samples 
E) deviations

Question 2

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 3

In an autoregressive forecasting model, the independent variable(s)is (are)___.

Accepted Answer

A) time-lagged values of the dependent variable 
B) first-order differences of the dependent variable 
C) second-order, or higher, differences of the dependent variable 
D) first-order quotients of the dependent variable 
E) time-lagged values of the independent variable 
A) time-lagged values of the dependent variable 
B) first-order differences of the dependent variable 
C) second-order, or higher, differences of the dependent variable 
D) first-order quotients of the dependent variable 
E) time-lagged values of the independent variable

Question 4

Calculating the "ratios of actuals to moving average" is a common step in time series decomposition.The results (the quotients)of this step estimate the ___.

Accepted Answer

A) trend and cyclical components 
B) seasonal and irregular components 
C) cyclical and irregular components 
D) trend and seasonal components 
E) irregular components 
A) trend and cyclical components 
B) seasonal and irregular components 
C) cyclical and irregular components 
D) trend and seasonal components 
E) irregular components

Question 5

Time-series data are data gathered on a desired characteristic at a particular point in time.

Accepted Answer

A) True 
 B)False

Question 6

The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter)in time series decomposition.The rationale for this is to ___.

Accepted Answer

A) reduce the sample size 
B) eliminate autocorrelation 
C) minimize serial correlation 
D) eliminate the irregular component 
E) eliminate the trend 
A) reduce the sample size 
B) eliminate autocorrelation 
C) minimize serial correlation 
D) eliminate the irregular component 
E) eliminate the trend

Question 7

Analysis of data for an autoregressive forecasting model produced the following tables:     The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The predicted (forecast)value for August is ___.

Accepted Answer

A) 174.41 
B) 83.67 
C) 218.71 
D) 36.91 
E) 191 
A) 174.41 
B) 83.67 
C) 218.71 
D) 36.91 
E) 191

Question 8

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) 7.67 
B) 8 
C) 9 
D) 6.89 
E) 11 
A) 7.67 
B) 8 
C) 9 
D) 6.89 
E) 11

Question 9

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) 3.10 
B) 12.40 
C) 2.48 
D) 6.67 
E) 5.10 
A) 3.10 
B) 12.40 
C) 2.48 
D) 6.67 
E) 5.10

Question 10

One of the main techniques for isolating the effects of seasonality is reconstitution.

Accepted Answer

A) True 
 B)False

Question 11

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 November in the following time series is ___.

Accepted Answer

A) 11.60 
B) 10.00 
C) 9.67 
D) 8.06 
E) 8.60 
A) 11.60 
B) 10.00 
C) 9.67 
D) 8.06 
E) 8.60

Question 12

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:     Jim's calculated value for the Durbin-Watson statistic is 1.14.Using $\alpha$ = 0.05, the appropriate decision is: ___.

Accepted Answer

A) do not reject H0: $\rho$ = 0 
B) reject H0: $\rho$= 0 
C) do not reject H0: $\rho$$\neq$ 0 
D) the test is inconclusive 
E) reject H0: $\rho$ ≠ 0 
A) do not reject H0: $\rho$ = 0 
B) reject H0: $\rho$= 0 
C) do not reject H0: $\rho$$\neq$ 0 
D) the test is inconclusive 
E) reject H0: $\rho$ ≠ 0

Question 13

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 14

A time series with forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___.

Accepted Answer

A) 13.33 
B) 17.94 
C) 89.71 
D) 22.42 
E) 32.34 
A) 13.33 
B) 17.94 
C) 89.71 
D) 22.42 
E) 32.34

Question 15

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

Accepted Answer

A) yt = 5.745787 + 0.062849 yt-1 + 0.065709 yt-2 
B) yt = 4.85094 - 0.10434 yt-1 + 0.962669 yt-2 
C) yt = 0.84426 - 1.66023 yt-1 + 14.65023 yt-2 
D) yt = 0.40299 + 0.103822 yt-1 + 9.yt-2 
E) yt = 0.40299 + 0.103822 yt-1 - 9.yt-2 
A) yt = 5.745787 + 0.062849 yt-1 + 0.065709 yt-2 
B) yt = 4.85094 - 0.10434 yt-1 + 0.962669 yt-2 
C) yt = 0.84426 - 1.66023 yt-1 + 14.65023 yt-2 
D) yt = 0.40299 + 0.103822 yt-1 + 9.yt-2 
E) yt = 0.40299 + 0.103822 yt-1 - 9.yt-2

Question 16

Linear regression models cannot be used to analyze quadratic trends in time-series data.

Accepted Answer

A) True 
 B)False

Question 17

When forecasting with exponential smoothing, data from previous periods is ___.

Accepted Answer

A) given equal importance 
B) given exponentially increasing importance 
C) ignored 
D) given exponentially decreasing importance 
E) linearly decreasing importance 
A) given equal importance 
B) given exponentially increasing importance 
C) ignored 
D) given exponentially decreasing importance 
E) linearly decreasing importance

Question 18

When the error terms of a regression forecasting model are correlated the problem of multicollinearity occurs.

Accepted Answer

A) True 
 B)False

Question 19

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:     Jim's calculated value for the Durbin-Watson statistic is 1.93.Using $\alpha$ = 0.05, the appropriate decision is: ___.

Accepted Answer

A) do not reject H0: $\rho$ = 0 
B) reject H0:$\rho$ ≠ 0 
C) do not reject: $\rho$ $\neq$ 0 
D) the test is inconclusive 
E) reject H0:$\rho$= 0 
A) do not reject H0: $\rho$ = 0 
B) reject H0:$\rho$ ≠ 0 
C) do not reject: $\rho$ $\neq$ 0 
D) the test is inconclusive 
E) reject H0:$\rho$= 0

Question 20

An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a naïve forecasting model.

Accepted Answer

A) True 
 B)False

Index numbers facilitate comparison of ___.

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

In an autoregressive forecasting model, the independent variable(s)is (are)___.

Calculating the "ratios of actuals to moving average" is a common step in time series decomposition.The results (the quotients)of this step estimate the ___.

Time-series data are data gathered on a desired characteristic at a particular point in time.

The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter)in time series decomposition.The rationale for this is to ___.

Analysis of data for an autoregressive forecasting model produced the following tables: The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The predicted (forecast)value for August is ___.

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 ___.

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

One of the main techniques for isolating the effects of seasonality is reconstitution.

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 November in the following time series is ___.

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

A time series with forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___.

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

Linear regression models cannot be used to analyze quadratic trends in time-series data.

When forecasting with exponential smoothing, data from previous periods is ___.

When the error terms of a regression forecasting model are correlated the problem of multicollinearity occurs.

An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a naïve forecasting model.

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

Index numbers facilitate comparison of ___.

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

In an autoregressive forecasting model, the independent variable(s)is (are)___.

Calculating the "ratios of actuals to moving average" is a common step in time series decomposition.The results (the quotients)of this step estimate the ___.

Time-series data are data gathered on a desired characteristic at a particular point in time.

The high and low values of the "ratios of actuals to moving average" are ignored when finalizing the seasonal index for a period (month or quarter)in time series decomposition.The rationale for this is to ___.

Analysis of data for an autoregressive forecasting model produced the following tables: The actual values of this time series, y, were 228, 54, and 191 for May, June, and July, respectively.The predicted (forecast)value for August is ___.

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 ___.

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

One of the main techniques for isolating the effects of seasonality is reconstitution.

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 November in the following time series is ___.

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

A time series with forecast values and error terms is presented in the following table.The mean squared error (MSE)for this forecast is ___.

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

Linear regression models cannot be used to analyze quadratic trends in time-series data.

When forecasting with exponential smoothing, data from previous periods is ___.

When the error terms of a regression forecasting model are correlated the problem of multicollinearity occurs.

An exponential smoothing technique in which the smoothing constant alpha is equal to one is equivalent to a naïve forecasting model.

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