Question 1

A manager uses the equation y = 40,000 + 150x to predict monthly receipts.What is the forecast for July if x = 0 in April?

Accepted Answer

A) 40,450. 
B) 40,600. 
C) 42,100. 
D) 42,250. 
E) 42,400. 
A) 40,450. 
B) 40,600. 
C) 42,100. 
D) 42,250. 
E) 42,400.

Question 2

The difference between a forecast and what turns out to be the true value is called the mean absolute deviation.

Accepted Answer

A) True 
 B)False

Question 3

Forecasting techniques such as moving-average,exponential smoothing,and the last-value method all represent averaged values of time-series data.

Accepted Answer

A) True 
 B)False

Question 4

A time-series is said to be smooth if its underlying probability distribution usually remains the same from one period to the next.

Accepted Answer

A) True 
 B)False

Question 5

Figure
The business analyst for Ace Business Machines,Inc.wants to forecast this year's demand for manual typewriters based on the following historical data:  
-What is the forecast for this year using exponential smoothing with $\alpha$ = 0.4,if the forecast for two years ago was 750?

Accepted Answer

A) 163. 
B) 180. 
C) 300. 
D) 467. 
E) 510. 
A) 163. 
B) 180. 
C) 300. 
D) 467. 
E) 510.

Question 6

Time-series data may exhibit which of the following behaviors?

Accepted Answer

A) Trend. 
B) Seasonality. 
C) Cycles. 
D) Irregularities. 
E) All of the above. 
A) Trend. 
B) Seasonality. 
C) Cycles. 
D) Irregularities. 
E) All of the above.

Question 7

Figure
Professor Z needs to allocate time among several tasks next week to include time for students' appointments.Thus,he needs to forecast the number of students who will seek appointments.He has gathered the following data:  
-What is the forecast for this year using exponential smoothing with trend if $\alpha$ = 0.5 and $\alpha$ = 0.1? Assume the forecast for last week was 65 and the forecast for two weeks ago was 75,and that the trend estimate for last years forecast was -5.

Accepted Answer

A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78. 
A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78.

Question 8

The last-value forecasting method is most useful when conditions are stable over time.

Accepted Answer

A) True 
 B)False

Question 9

Figure
Professor Z needs to allocate time among several tasks next week to include time for students' appointments.Thus,he needs to forecast the number of students who will seek appointments.He has gathered the following data:  
-What is the forecast for this year using the last-value forecasting method?

Accepted Answer

A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78. 
A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78.

Question 10

If significant changes in conditions are occurring relatively frequently,then a smaller smoothing constant is needed.

Accepted Answer

A) True 
 B)False

Question 11

Statistical models to forecast economic trends are called econometric models.

Accepted Answer

A) True 
 B)False

Question 12

Figure
Professor Z needs to allocate time among several tasks next week to include time for students' appointments.Thus,he needs to forecast the number of students who will seek appointments.He has gathered the following data:  
-What is the forecast for this year using a moving-average forecast based on the last three weeks?

Accepted Answer

A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78. 
A) 49. 
B) 50. 
C) 52. 
D) 65. 
E) 78.

Question 13

Linear regression can be used to approximate the relationship between independent and dependent variables.

Accepted Answer

A) True 
 B)False

Question 14

The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered.

Accepted Answer

A) True 
 B)False

Question 15

When statistical forecasting methods are used,it is no longer necessary to use judgmental methods as well.
Multiple Choice Questions

Accepted Answer

A) True 
 B)False

Question 16

Gradual,long-term movement in time-series values is called:

Accepted Answer

A) seasonal variation. 
B) trend. 
C) cycles. 
D) irregular variation. 
E) random variation. 
A) seasonal variation. 
B) trend. 
C) cycles. 
D) irregular variation. 
E) random variation.

Question 17

Causal forecasting obtains a forecast for a dependent variable by relating it directly to one or more independent variables.

Accepted Answer

A) True 
 B)False

Question 18

Removing the seasonal component from a time-series can be accomplished by dividing each value by its appropriate seasonal factor.

Accepted Answer

A) True 
 B)False

Question 19

Exponential smoothing with trend was designed for time-series that have great variability both up and down.

Accepted Answer

A) True 
 B)False

Question 20

The averaging method uses all the data points in the time-series.

Accepted Answer

A) True 
 B)False

A manager uses the equation y = 40,000 + 150x to predict monthly receipts.What is the forecast for July if x = 0 in April?

The difference between a forecast and what turns out to be the true value is called the mean absolute deviation.

Forecasting techniques such as moving-average,exponential smoothing,and the last-value method all represent averaged values of time-series data.

A time-series is said to be smooth if its underlying probability distribution usually remains the same from one period to the next.

Time-series data may exhibit which of the following behaviors?

The last-value forecasting method is most useful when conditions are stable over time.

If significant changes in conditions are occurring relatively frequently,then a smaller smoothing constant is needed.

Statistical models to forecast economic trends are called econometric models.

Linear regression can be used to approximate the relationship between independent and dependent variables.

The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered.

When statistical forecasting methods are used,it is no longer necessary to use judgmental methods as well. Multiple Choice Questions

Gradual,long-term movement in time-series values is called:

Causal forecasting obtains a forecast for a dependent variable by relating it directly to one or more independent variables.

Removing the seasonal component from a time-series can be accomplished by dividing each value by its appropriate seasonal factor.

Exponential smoothing with trend was designed for time-series that have great variability both up and down.

The averaging method uses all the data points in the time-series.

Introduction

Linear Programming: Basic Concepts

Linear Programming: Formulation and Applications

The Art of Modeling With Spreadsheets

What-If Analysis for Linear Programming

Network Optimization Problems

Using Binary Integer Programming to Deal With Yes-Or-No Decisions

Nonlinear Programming

Decision Analysis

Cd Supplement - Decision Analysis

Queueing Models

CD Supplement - Additional Queueing Models

Computer Simulation: Basic Concepts

CD Supplement - the Inverse Transformation Method for Generating Random Observations

Computer Simulation With Crystal Ball

CD - Solution Concepts for Linear Programming

CD - Transportation and Assignment Problems

CD - Pertcpm Models for Project Management

CD - Goal Programming

CD - Inventory Management With Known Demand

CD - Inventory Management With Uncertain Demand

Filters

Exam 11: Forecasting

A manager uses the equation y = 40,000 + 150x to predict monthly receipts.What is the forecast for July if x = 0 in April?

The difference between a forecast and what turns out to be the true value is called the mean absolute deviation.

Forecasting techniques such as moving-average,exponential smoothing,and the last-value method all represent averaged values of time-series data.

A time-series is said to be smooth if its underlying probability distribution usually remains the same from one period to the next.

Figure The business analyst for Ace Business Machines,Inc.wants to forecast this year's demand for manual typewriters based on the following historical data: -What is the forecast for this year using exponential smoothing with α\alphaα = 0.4,if the forecast for two years ago was 750?

Time-series data may exhibit which of the following behaviors?

The last-value forecasting method is most useful when conditions are stable over time.

If significant changes in conditions are occurring relatively frequently,then a smaller smoothing constant is needed.

Statistical models to forecast economic trends are called econometric models.

Linear regression can be used to approximate the relationship between independent and dependent variables.

The moving-average forecasting method is a very good one when conditions remain pretty much the same over the time period being considered.

When statistical forecasting methods are used,it is no longer necessary to use judgmental methods as well. Multiple Choice Questions

Gradual,long-term movement in time-series values is called:

Causal forecasting obtains a forecast for a dependent variable by relating it directly to one or more independent variables.

Removing the seasonal component from a time-series can be accomplished by dividing each value by its appropriate seasonal factor.

Exponential smoothing with trend was designed for time-series that have great variability both up and down.

The averaging method uses all the data points in the time-series.

Introduction

Linear Programming: Basic Concepts

Linear Programming: Formulation and Applications

The Art of Modeling With Spreadsheets

What-If Analysis for Linear Programming

Network Optimization Problems

Using Binary Integer Programming to Deal With Yes-Or-No Decisions

Nonlinear Programming

Decision Analysis

Cd Supplement - Decision Analysis

Queueing Models

CD Supplement - Additional Queueing Models

Computer Simulation: Basic Concepts

CD Supplement - the Inverse Transformation Method for Generating Random Observations

Computer Simulation With Crystal Ball

CD - Solution Concepts for Linear Programming

CD - Transportation and Assignment Problems

CD - Pertcpm Models for Project Management

CD - Goal Programming

CD - Inventory Management With Known Demand

CD - Inventory Management With Uncertain Demand

Filters