Chapter 12
Computer Simulation: Basic Concepts
1
Simulation models are fairly complicated and can only be used for specific
decisions.
F
2
When dealing with relatively complex systems, computer simula
CD Chapter 16
PERT/CPM Models for Project Management
1
In an Activity-On-Arc project network, the nodes are used to separate an activity
from each of its immediate predecessors.
T
2
If two paths are t
Chapter 3
Linear Programming: Formulation and Applications
1
When formulating a linear programming model on a spreadsheet, the constraints
are located in the data cells.
F
2
A mathematical model will
CHAPTER 6
NETWORK OPTIMIZATION PROBLEMS
SOLUTION TO SOLVED PROBLEMS
6.S1 Distribution at Heart Beats
Heart Beats is a manufacturer of medical equipment. The companys primary product is a
Chapter 13
Computer Simulation with Risk Solver Platform
1
To use RSPE, the results cell must be a linear function.
F
2
The standard error gives an indication of how accurate the estimated mean is.
T
CD 18-1
CHAPTER 18
INVENTORY MANAGEMENT WITH KNOWN DEMAND
Learning Objectives
After completing this chapter, you should be able to
1. Identify the cost components of inventory models.
2. Describe the
Chapter 2
Linear Programming: Basic Concepts
1
Linear programming problems may have only one goal or objective specified.
T
2
A feasible solution is one that satisfies at least one of the constraints
Chapter 4
The Art of Modeling with Spreadsheets
1
There is only one correct way to set up a spreadsheet model.
F
2
In the Everglade Golden Years Company problem, the long-term loan had a lower
interes
CD 16-1
CHAPTER 16
PERT/CPM MODELS FOR PROJECT MANAGEMENT
Learning objectives
After completing this chapter, you should be able to
1. Describe the kind of help that PERT/CPM can provide a project mana
Chapter 5
What-if Analysis for Linear Programming
1
If the optimal solution will remain the same over only a small range of values for
a particular coefficient in the objective function, then manageme
S 12-1
SUPPLEMENT TO CHAPTER 12
THE INVERSE TRANSFORMATION METHOD FOR
GENERATING RANDOM OBSERVATIONS
Section 12.1 mentioned that a general mathematical procedure is available for generating random
obs
CD Chapter 19
Inventory Management with Uncertain Demand
1
The objective of inventory management is to minimize inventory levels.
F
2
The overall objective of inventory management is to balance custom
INTERS SIMPLE
Capital
Tasa de
inters:
8% mensual
(.08)
$15000
Inters
+
15000(.08) = 1200
$15 000
1 mes
15 000 + 1 200 = 16 200
Monto
INTERS SIMPLE
Capital
Inters
Tasa de
inters:
C
i
C
+
I Ci
1 periodo
CD S Ch 6-1
SUPPLEMENT TO CHAPTER 6
MINIMUM SPANNING-TREE PROBLEMS
Chapter 6 focuses on network optimization problems. These are problems that can be described in
terms of a complete network that has
214 CHAPTER 9. SIMPLE LINEAR REGRESSION
a: is coefcient. Often the 1 subscript in 51 is replaced by the name of the
explanatory variable or some abbreviation of it.
So the structural model says that f
9.1. THE MODEL BEHIND LINEAR REGRESSION 215
we need to be sure that the size of the error in measuring a: is small compared to
the variability of Y at any given :10 value. For more on this topic, see
220 CHAPTER 9. SIMPLE LINEAR REGRESSION
xed-x. In some cases, we can actually perform repeated measurements of a: on
the same case to see the spread of ac and then do the same thing for y at each of
a
218 CHAPTER 9. SIMPLE LINEAR REGRESSION
9.2 Statistical hypotheses
For simple linear regression, the chief null hypothesis is H0 : B1 = 0, and the
corresponding alternative hypothesis is H1 : 51 7E 0.
9.3. SIMPLE LINEAR REGRESSION EXAMPLE 219
Final Weight (gm)
300 400 500 600
200
100
O 20 40 60 80 1 00
Soil Nitrogen (mg/pot)
Figure 9.2: Scatterplot of corn data.
mg.
EDA, in the form of a scatterp
Chapter 9
Simple Linear Regression
An analysis appropriate for a quantitative outcome and a single quantitative ex-
planatory variable.
9.1 The model behind linear regression
When we are examining the
222 CHAPTER 9. SIMPLE LINEAR REGRESSION
By plugging different values for 33 into this equation we can nd the corre-
sponding y values that are on the line drawn. For any given b0 and b1 we get a
poten
216 CHAPTER 9. SIMPLE LINEAR REGRESSION
variable each time, serial correlation is extremely likely. Breaking the assumption
of independent errors does not indicate that no analysis is possible, only t
9.1. THE MODEL BEHIND LINEAR REGRESSION 217
15
10
Figure 9.1: Mnemonic for the simple regression model.
than ANOVA. If the truth is non-linearity, regression will make inappropriate
predictions, but a
CHAPTER 20
COMPUTER SIMULATION WITH CRYSTAL BALL
SOLUTION TO SOLVED PROBLEMS
20.S1 Saving for Retirement
Patrick Gordon is ten years away from retirement. He has accumulated a $100,000 nest egg that
h
Chapter 9
Decision Analysis
1
Prior probabilities refer to the relative likelihood of past events.
F
2
Bayes' decision rule says to choose the alternative with the largest possible payoff.
F
3
The EVP
A
B
C
D
E
F
G
H
I
J
K
L
1 Capital Budgeting with Contingency Constraints
2
Project Project Project Project Project Project Project Project
3
1
2
3
4
5
6
7
8
4
NPV ($million) 10
12
11
15
24
17
16
18
5
A
B
C
D
1 Production and Inventory Planning
2
Unit Cost (Reg) $125
3
Unit Cost (OT) $135
4
Selling Price $200
5
Holding Cost
$5
6
Starting Inventory
5
7
8
Jan
Feb
9
Regular Production
10
14
10
<=
<=
1
CD Chapter 15
Transportation and Assignment Problems
1
Transportation problems are concerned with distributing commodities from
sources to destinations in such a way as to maximize the total amount sh