Question 1

A time series with forecast values is presented in the following table:   If the mean square error (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 2

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 squared error (MSE)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 3

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

Accepted Answer

A) True 
 B)False

Question 4

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

Accepted Answer

A) True 
 B)False

Question 5

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 6

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 November 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 7

Using 2011 as the base year,the 2010 value of the Laspeyres Price Index is ______.

Accepted Answer

A)  69.92 
B)  144.06 
C)  100.21 
D)  79.72 
E)  99.72 
A)  69.92 
B)  144.06 
C)  100.21 
D)  79.72 
E)  99.72

Question 8

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

Accepted Answer

A) True 
 B)False

Question 9

Fitting a linear trend to 36 monthly data points (January 2011 = 1,February 2011 =2,March 2011 = 3,etc.)produced the following tables.     The projected trend value for January 2014 is ________.

Accepted Answer

A)  231.39 
B)  555.71 
C)  339.50 
D)  447.76 
E)  355.71 
A)  231.39 
B)  555.71 
C)  339.50 
D)  447.76 
E)  355.71

Question 10

The following graph of a 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 11

A time series with forecast values is presented in the following table:   On this table,a is some nondisclosed value.The mean square error (MSE)is ______% of a.

Accepted Answer

A)  4.15 
B)  0.415 
C)  4.15a 
D)  0.415a 
E)  0.00332 
A)  4.15 
B)  0.415 
C)  4.15a 
D)  0.415a 
E)  0.00332

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.     
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 13

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

Accepted Answer

A) True 
 B)False

Question 14

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

A time series with forecast values is presented in the following table: If the mean square error (MAD)is 257,then a = ______.

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 squared error (MSE)for this forecast is ___________.

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

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

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 ________.

Using 2011 as the base year,the 2010 value of the Laspeyres Price Index is ______.

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

Fitting a linear trend to 36 monthly data points (January 2011 = 1,February 2011 =2,March 2011 = 3,etc.)produced the following tables. The projected trend value for January 2014 is ________.

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

A time series with forecast values is presented in the following table: On this table,a is some nondisclosed value.The mean square error (MSE)is ______% of a.

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

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

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

A Nalysis 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

A time series with forecast values is presented in the following table: If the mean square error (MAD)is 257,then a = ______.

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 squared error (MSE)for this forecast is ___________.

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

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

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 ________.

Using 2011 as the base year,the 2010 value of the Laspeyres Price Index is ______.

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

Fitting a linear trend to 36 monthly data points (January 2011 = 1,February 2011 =2,March 2011 = 3,etc.)produced the following tables. The projected trend value for January 2014 is ________.

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

A time series with forecast values is presented in the following table: On this table,a is some nondisclosed value.The mean square error (MSE)is ______% of a.

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

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

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

A Nalysis 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