Question 1

The degree of management involvement in short-range forecasts is:

Accepted Answer

A)  none. 
B)  low. 
C)  moderate. 
D)  high. 
E)  total. 
A)  none. 
B)  low. 
C)  moderate. 
D)  high. 
E)  total.

Question 2

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.

Accepted Answer

A) True 
 B)False

Question 3

Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14. $$\begin{array} { l l } 
\text { Period } & \text { Deliveries (dozens) } \
\hline  1 & 648 \
2 & 590 \
3 & 631 \
4 & 769 \
5 & 745 \
6 & 856 \
7 & 760 \
8 & 962 \
9 & 990 \
10 & 1100
\end{array}$$

Accepted Answer

Y_t = 518.2 + 52.164t r = +.935 \[\begin{a

Question 4

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: $$\begin{array} { l l } 
\text { Week } & \text { \# Students } \
\hline 6 \text { weeks ago } & 83 \
5 \text { weeks ago } & 110 \
4 \text { weeks ago } & 95 \
3 \text { weeks ago } & 80 \
2 \text { weeks ago } & 65 \
\text { Last week } & 50
\end{array}$$ What is this week's forecast using exponential smoothing with alpha = .2, if the forecast for two weeks ago was 90?

Accepted Answer

A)  49 
B)  50 
C)  52 
D)  65 
E)  77 
A)  49 
B)  50 
C)  52 
D)  65 
E)  77

Question 5

Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1. $$\begin{array} { l l } 
\text { Period } & \text { Demand } \
\hline  1 & 19 \
2 & 20 \
3 & 18 \
4 & 19 \
5 & 17
\end{array}$$

Accepted Answer

.4(17) + .

Question 6

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: $$\begin{array} { l l } 
\text { Week } & \text { \# Students } \
\hline 6 \text { weeks ago } & 83 \
5 \text { weeks ago } & 110 \
4 \text { weeks ago } & 95 \
3 \text { weeks ago } & 80 \
2 \text { weeks ago } & 65 \
\text { Last week } & 50
\end{array}$$ What is this week's forecast using the naive approach?

Accepted Answer

A)  45 
B)  50 
C)  52 
D)  65 
E)  78 
A)  45 
B)  50 
C)  52 
D)  65 
E)  78

Question 7

Data on the last three years of demand are available as follows: $$\begin{array} { l l l l l } 
&&& { \text { Season } } \
\text { Year } & \text { Spring } & \text { Summer } & \text { Fall } & \text { Winter } \
\hline  \text { Three years ago } & 18 & 10 & 26 & 42 \
\text { Two years ago } & 26 & 18 & 34 & 50 \
\text { Last year } & 34 & 26 & 42 & 58
\end{array}$$ What is the centered moving average for spring two years ago?

Accepted Answer

The answer of Data on the last three years of...

Question 8

The naive forecast is limited in its application to series that reflect no trend or seasonality.

Accepted Answer

A) True 
 B)False

Question 9

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

Accepted Answer

A)  0 
B)  .01 
C)  .05 
D)  .10 
E)  .15 
A)  0 
B)  .01 
C)  .05 
D)  .10 
E)  .15

Question 10

Use of simple linear regression analysis assumes that:

Accepted Answer

A)  variations around the line are nonrandom. 
B)  deviations around the line are normally distributed. 
C)  predictions can easily be made beyond the range of observed values of the predictor variable. 
D)  all possible predictor variables are included in the model. 
E)  the variance of error terms (deviations) varies directly with the predictor variable. 
A)  variations around the line are nonrandom. 
B)  deviations around the line are normally distributed. 
C)  predictions can easily be made beyond the range of observed values of the predictor variable. 
D)  all possible predictor variables are included in the model. 
E)  the variance of error terms (deviations) varies directly with the predictor variable.

Question 11

The owner of Darkest Tans Unlimited in a local mall is forecasting this month's (October's) demand for the one new tanning booth based on the following historical data: $$\begin{array} { l l } 
\text { Month } & \text { Number of Visits } \
\hline  \text { April } & 100 \
\text { May } & 140 \
\text { June } & 110 \
\text { July } & 150 \
\text { August } & 120 \
\text { September } & 160
\end{array}$$ What is this month's forecast using the naive approach?

Accepted Answer

A)  100 
B)  160 
C)  130 
D)  140 
E)  120 
A)  100 
B)  160 
C)  130 
D)  140 
E)  120

Question 12

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.

Accepted Answer

A) True 
 B)False

Question 13

Professor Very Busy needs to allocate time next week to include time for office hours. He needs to forecast the number of students who will seek appointments. He has gathered the following data: $$\begin{array} { l l } 
\text { Week } & \text { \# Students } \
\hline 6 \text { weeks ago } & 83 \
5 \text { weeks ago } & 110 \
4 \text { weeks ago } & 95 \
3 \text { weeks ago } & 80 \
2 \text { weeks ago } & 65 \
\text { Last week } & 50
\end{array}$$ What is this week's forecast using the least squares trend line for these data?

Accepted Answer

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

Question 14

Which of the following is not necessarily an element of a good forecast?

Accepted Answer

A)  estimate of accuracy 
B)  timeliness 
C)  meaningful units 
D)  low cost 
E)  written 
A)  estimate of accuracy 
B)  timeliness 
C)  meaningful units 
D)  low cost 
E)  written

Question 15

The president of State University wants to forecast student enrollments for this academic year based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Enrollments } \
\hline \text { 5 years ago } & 15,000 \
4 \text { years ago } & 16,000 \
\text { 3 years ago } & 18,000 \
2 \text { years ago } & 20,000 \
\text { Last year } & 21,000
\end{array}$$ What is the forecast for this year using exponential smoothing with alpha = .5, if the forecast for two years ago was 16,000?

Accepted Answer

A)  18,750 
B)  19,500 
C)  21,000 
D)  22,650 
E)  22,800 
A)  18,750 
B)  19,500 
C)  21,000 
D)  22,650 
E)  22,800

Question 16

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.

Accepted Answer

A) True 
 B)False

Question 17

Exponential smoothing is a form of weighted averaging.

Accepted Answer

A) True 
 B)False

Question 18

The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Enrollment } \
\hline \text { Four years ago } & 2000 \
\text { Three years ago } & 2200 \
\text { Two years ago } & 2800 \
\text { Last year } & 3000
\end{array}$$ What is this year's forecast using exponential smoothing with alpha = .4, if last year's smoothed forecast was 2,600?

Accepted Answer

A)  2,600 
B)  2,760 
C)  2,800 
D)  3,840 
E)  3,000 
A)  2,600 
B)  2,760 
C)  2,800 
D)  3,840 
E)  3,000

Question 19

The dean of a school of business is forecasting total student enrollment for this year's summer session classes based on the following historical data: $$\begin{array} { l l } 
\text { Year } & \text { Enrollment } \
\hline \text { Four years ago } & 2000 \
\text { Three years ago } & 2200 \
\text { Two years ago } & 2800 \
\text { Last year } & 3000
\end{array}$$ What is the annual rate of change (slope) of the least squares trend line for these data?

Accepted Answer

A)  0 
B)  200 
C)  400 
D)  180 
E)  360 
A)  0 
B)  200 
C)  400 
D)  180 
E)  360

Question 20

Which of the following corresponds to the predictor variable in simple linear regression?

Accepted Answer

A)  regression coefficient 
B)  dependent variable 
C)  independent variable 
D)  predicted variable 
E)  demand coefficient 
A)  regression coefficient 
B)  dependent variable 
C)  independent variable 
D)  predicted variable 
E)  demand coefficient

The degree of management involvement in short-range forecasts is:

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.

Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14. Period Deliveries (dozens) 1 648 2 590 3 631 4 769 5 745 6 856 7 760 8 962 9 990 10 1100

Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1. Period Demand 1 19 2 20 3 18 4 19 5 17

Data on the last three years of demand are available as follows: Season Year Spring Summer Fall Winter Three years ago 18 10 26 42 Two years ago 26 18 34 50 Last year 34 26 42 58 What is the centered moving average for spring two years ago?

The naive forecast is limited in its application to series that reflect no trend or seasonality.

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

Use of simple linear regression analysis assumes that:

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.

Which of the following is not necessarily an element of a good forecast?

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.

Exponential smoothing is a form of weighted averaging.

Which of the following corresponds to the predictor variable in simple linear regression?

Introduction to Operations Management

Competitiveness, Strategy, and Productivity

Product and Service Design

Extension: Reliability

Strategic Capacity Planning for Products and Services

Extension: Decision Theory

Process Selection and Facility Layout

Work Design and Measurement

Extension: Learning Curves

Location Planning and Analysis

Extension: The Transportation Model

Management of Quality

Quality Control

Extension: Acceptance Sampling

Aggregate Planning and Master Scheduling

MRP and ERP

Inventory Management

Jit and Lean Operations

Extension: Maintenance

Supply Chain Management

Scheduling

Project Management

Management of Waiting Lines

Linear Programming

Filters

Exam 3: Forecasting

The degree of management involvement in short-range forecasts is:

Forecasting techniques that are based on time-series data assume that future values of the series will duplicate past values.

Develop a linear trend equation for the data on bread deliveries shown below. Forecast deliveries for period 11 through 14. Period Deliveries (dozens) 1 648 2 590 3 631 4 769 5 745 6 856 7 760 8 962 9 990 10 1100

Given the data below, develop a forecast for period 6 using a four-period weighted moving average and weights of .4, .3, .2 and .1. Period Demand 1 19 2 20 3 18 4 19 5 17

Data on the last three years of demand are available as follows: Season Year Spring Summer Fall Winter Three years ago 18 10 26 42 Two years ago 26 18 34 50 Last year 34 26 42 58 What is the centered moving average for spring two years ago?

The naive forecast is limited in its application to series that reflect no trend or seasonality.

Which of the following possible values of alpha would cause exponential smoothing to respond the most quickly to forecast errors?

Use of simple linear regression analysis assumes that:

A forecast method is generally deemed to perform adequately when the errors exhibit an identifiable pattern.

Which of the following is not necessarily an element of a good forecast?

If a pattern appears when a dependent variable is plotted against time, one should use time series analysis instead of regression analysis.

Exponential smoothing is a form of weighted averaging.

Which of the following corresponds to the predictor variable in simple linear regression?

Introduction to Operations Management

Competitiveness, Strategy, and Productivity

Product and Service Design

Extension: Reliability

Strategic Capacity Planning for Products and Services

Extension: Decision Theory

Process Selection and Facility Layout

Work Design and Measurement

Extension: Learning Curves

Location Planning and Analysis

Extension: The Transportation Model

Management of Quality

Quality Control

Extension: Acceptance Sampling

Aggregate Planning and Master Scheduling

MRP and ERP

Inventory Management

Jit and Lean Operations

Extension: Maintenance

Supply Chain Management

Scheduling

Project Management

Management of Waiting Lines

Linear Programming

Filters