Question 1

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

Accepted Answer

A) True 
 B)False

Question 2

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 3

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016.   The Paasche price index for 2016 using 2000 as base year is ______.

Accepted Answer

A) 139.87 
B) 137.25 
C) 140.33 
D) 133.25 
E) 131.87 
A) 139.87 
B) 137.25 
C) 140.33 
D) 133.25 
E) 131.87

Question 4

Weighted aggregate price indexes are also known as _______.

Accepted Answer

A) unbalanced indexes 
B) balanced indexes 
C) value indexes 
D) multiplicative indexes 
E) overall indexes 
A) unbalanced indexes 
B) balanced indexes 
C) value indexes 
D) multiplicative indexes 
E) overall indexes

Question 5

Analysis of data for an autoregressive forecasting model produced the following tables.     The results indicate that __________.

Accepted Answer

A) the first predictor, yt-1, is significant at the 10% level 
B) the second predictor, yt-2, is significant at the 1% level 
C) all predictor variables are significant at the 5% level 
D) none of the predictor variables are significant at the 5% level 
E) the overall regression model is not significant at 5% level 
A) the first predictor, yt-1, is significant at the 10% level 
B) the second predictor, yt-2, is significant at the 1% level 
C) all predictor variables are significant at the 5% level 
D) none of the predictor variables are significant at the 5% level 
E) the overall regression model is not significant at 5% level

Question 6

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 7

A time series with forecast values is presented in the following table: $$\begin{array} { | r | r | r | } 
\hline \text { Month } & \text { Actual. } & \text { Forecast } \
\hline J a n & a & \
\hline \text { Mar } & 12 a & 1.1 a \
\hline \text { May} & 1.15 a & 1.2 a \
\hline J ul & 1.25 a & 12.21 a \
\hline \mathrm { Sep } & 1.3 a & 1.25 a \
\hline
\end{array}$$
 If the mean absolute deviation (MAD)is 257, then a = ______.

Accepted Answer

A) 4283.33 
B) 428.33 
C) 15.42 
D) 42.833 
E) 1.542 
A) 4283.33 
B) 428.33 
C) 15.42 
D) 42.833 
E) 1.542

Question 8

The actual value of a variable for July was 195.The researcher is using exponential smoothing with α = 0.30 and determines that the forecast value for August is 205.5.Then the forecast value for July was ______.

Accepted Answer

A) 200 
B) 202 
C) 205 
D) 207 
E) 210 
A) 200 
B) 202 
C) 205 
D) 207 
E) 210

Question 9

Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic D = 1.409.Assume that n = 30 and α = 0.01.Then your decision is to ______.

Accepted Answer

A) fail to reject the null hypothesis D = 0 
B) reject the null hypothesis D = 0 
C) fail to reject the null hypothesis ρ = 0 
D) reject the null hypothesis ρ = 0 
E) fail to reject the null hypothesis ρ > 0 
A) fail to reject the null hypothesis D = 0 
B) reject the null hypothesis D = 0 
C) fail to reject the null hypothesis ρ = 0 
D) reject the null hypothesis ρ = 0 
E) fail to reject the null hypothesis ρ > 0

Question 10

A seasonal index for quarterly data is found as the ratio of ____________ to ___________ and is then multiplied by 100.

Accepted Answer

A) actuals; medians 
B) moving average; 8 
C) actuals; moving averages 
D) actuals; 4 
E) 100; actuals 
A) actuals; medians 
B) moving average; 8 
C) actuals; moving averages 
D) actuals; 4 
E) 100; actuals

Question 11

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016.   If the Paasche price index for 2016 using 2000 as base year is 137.75, then P = ______.

Accepted Answer

A) 4.75 
B) 4.88 
C) 5.23 
D) 5.67 
E) 5.72 
A) 4.75 
B) 4.88 
C) 5.23 
D) 5.67 
E) 5.72

Question 12

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 13

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 14

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 the error terms are presented in the following table.The mean squared error (MSE)for this forecast is ___________. $\begin{array}{|r|r|r|r|}
\hline \text { Monthh } & \text { Actual } & \text { Forecast} & \text { Error } \
\hline \text { July } &4 & & \
\hline \text { Aug } & 6 & 5 &-1 \
\hline \text { Sept } & 3 & 6 &3 \
\hline \text { Oct } & 9 & 8& -1 \
\hline \text { Nov } &8 &9& 1 \
\hline
\end{array}$

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 15

Autocorrelation in a regression forecasting model can be detected by the F test.

Accepted Answer

A) True 
 B)False

Question 16

Typically, the denominator used to calculate an index number is a measurement for the ____________ period.

Accepted Answer

A) base 
B) current 
C) spanning 
D) intermediate 
E) peak 
A) base 
B) current 
C) spanning 
D) intermediate 
E) peak

Question 17

The following graph of time-series data suggests a _______________ trend.

Accepted Answer

A) linear 
B) quadratic 
C) cosine 
D) tangential 
E) flat 
A) linear 
B) quadratic 
C) cosine 
D) tangential 
E) flat

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, the forecast value for October made at the end of September in the following time series would be ____________. $$\begin{array} { | c | r | } 
\hline \text { Joly } & 28 \
\hline \text { Ang } & 27 \
\hline \text { Sept } & 17 \
\hline \text { Oct } & 19 \
\hline
\end{array}$$

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

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

Accepted Answer

A) True 
 B)False

Question 20

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, the forecast value for November in the following time series would be ____________. $$\begin{array} { | c | r | } 
\hline \text { Joly } & 28 \
\hline \text { Ang } & 27 \
\hline \text { Sept } & 17 \
\hline \text { Oct } & 19 \
\hline
\end{array}$$

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

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

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

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016. The Paasche price index for 2016 using 2000 as base year is ______.

Weighted aggregate price indexes are also known as _______.

Analysis of data for an autoregressive forecasting model produced the following tables. The results indicate that __________.

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

A time series with forecast values is presented in the following table: Month Actual. Forecast Jan a Mar 12a 1.1a May 1.15a 1.2a Jul 1.25a 12.21a 1.3a 1.25a If the mean absolute deviation (MAD)is 257, then a = ______.

The actual value of a variable for July was 195.The researcher is using exponential smoothing with α = 0.30 and determines that the forecast value for August is 205.5.Then the forecast value for July was ______.

Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic D = 1.409.Assume that n = 30 and α = 0.01.Then your decision is to ______.

A seasonal index for quarterly data is found as the ratio of __ to _ and is then multiplied by 100.

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016. If the Paasche price index for 2016 using 2000 as base year is 137.75, then P = ______.

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

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

Autocorrelation in a regression forecasting model can be detected by the F test.

Typically, the denominator used to calculate an index number is a measurement for the ____________ period.

The following graph of time-series data suggests a _______________ trend.

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

Introduction to Statistics and Business Analytics

Visualizing Data With 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

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

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

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016. The Paasche price index for 2016 using 2000 as base year is ______.

Weighted aggregate price indexes are also known as _______.

Analysis of data for an autoregressive forecasting model produced the following tables. The results indicate that __________.

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

A time series with forecast values is presented in the following table: Month Actual. Forecast Jan a Mar 12a 1.1a May 1.15a 1.2a Jul 1.25a 12.21a 1.3a 1.25a If the mean absolute deviation (MAD)is 257, then a = ______.

The actual value of a variable for July was 195.The researcher is using exponential smoothing with α = 0.30 and determines that the forecast value for August is 205.5.Then the forecast value for July was ______.

Suppose that for a time-series model with one predictor, you compute a Durbin-Watson statistic D = 1.409.Assume that n = 30 and α = 0.01.Then your decision is to ______.

A seasonal index for quarterly data is found as the ratio of ____________ to ___________ and is then multiplied by 100.

The table below shows the prices in $ and quantities (thousands)for five specialized electronic components for 2000 and 2016. If the Paasche price index for 2016 using 2000 as base year is 137.75, then P = ______.

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

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

Autocorrelation in a regression forecasting model can be detected by the F test.

Typically, the denominator used to calculate an index number is a measurement for the ____________ period.

The following graph of time-series data suggests a _______________ trend.

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

Introduction to Statistics and Business Analytics

Visualizing Data With 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

A seasonal index for quarterly data is found as the ratio of __ to _ and is then multiplied by 100.