Question 1

Which of the following will not be present in a deseasonalized time series?

Accepted Answer

A) Trend effects 
B) Cyclical variation 
C) Seasonal variation 
D) Random variation 
A) Trend effects 
B) Cyclical variation 
C) Seasonal variation 
D) Random variation

Question 2

Random variation is one of the four different components of a time series.It is caused by irregular and unpredictable changes in a time series that are not caused by any other component.It tends to mask the existence of the other more predictable components.

Accepted Answer

A) True 
 B)False

Question 3

The most commonly used measures of forecast accuracy are the:

Accepted Answer

A) mean absolute deviation and the sum of squares for forecast errors 
B) sum of squares for forecast error and seasonal indexes 
C) seasonal indexes and the percentage of trend 
D) all of these choices are correct 
A) mean absolute deviation and the sum of squares for forecast errors 
B) sum of squares for forecast error and seasonal indexes 
C) seasonal indexes and the percentage of trend 
D) all of these choices are correct

Question 4

Given the following time series,compute the seasonal (quarterly)indexes,using the four-quarter centered moving averages.

Accepted Answer

The answer of Given the following time series,compute the seasonal...

Question 5

Which of the following models might be appropriate to describe a new product that has experienced a rapid early growth rate followed by the inevitable leveling off?

Accepted Answer

A) Autoregressive model 
B) Linear model for long-term trend 
C) Quadratic model for long-term trend 
D) All of these choices are true 
A) Autoregressive model 
B) Linear model for long-term trend 
C) Quadratic model for long-term trend 
D) All of these choices are true

Which of the following will not be present in a deseasonalized time series?

Random variation is one of the four different components of a time series.It is caused by irregular and unpredictable changes in a time series that are not caused by any other component.It tends to mask the existence of the other more predictable components.

The most commonly used measures of forecast accuracy are the:

Given the following time series,compute the seasonal (quarterly)indexes,using the four-quarter centered moving averages.

Which of the following models might be appropriate to describe a new product that has experienced a rapid early growth rate followed by the inevitable leveling off?

What Is Statistics

Graphical Descriptive Techniques 1

Graphical Descriptive Techniques 2

Numerical Descriptive Techniques

Data Collection and Sampling

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions

Introduction to Estimation

Introduction to Hypothesis Testing

Inference About a Population

Inference About Comparing Two Populations

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Statistics

Statistical Process Control

Decision Analysis

Conclusion

Filters

Exam 20: Time-Series Analytics and Forecasting

Which of the following will not be present in a deseasonalized time series?

Random variation is one of the four different components of a time series.It is caused by irregular and unpredictable changes in a time series that are not caused by any other component.It tends to mask the existence of the other more predictable components.

The most commonly used measures of forecast accuracy are the:

Given the following time series,compute the seasonal (quarterly)indexes,using the four-quarter centered moving averages.

Which of the following models might be appropriate to describe a new product that has experienced a rapid early growth rate followed by the inevitable leveling off?

What Is Statistics

Graphical Descriptive Techniques 1

Graphical Descriptive Techniques 2

Numerical Descriptive Techniques

Data Collection and Sampling

Probability

Random Variables and Discrete Probability Distributions

Continuous Probability Distributions

Sampling Distributions

Introduction to Estimation

Introduction to Hypothesis Testing

Inference About a Population

Inference About Comparing Two Populations

Analysis of Variance

Chi-Squared Tests

Simple Linear Regression and Correlation

Multiple Regression

Model Building

Nonparametric Statistics

Statistical Process Control

Decision Analysis

Conclusion

Filters