Quantitative Business Analysis : Text and Cases,9780256147131

Quantitative Business Analysis : Text and Cases

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Edition: 1st
ISBN13: 9780256147131
ISBN10: 0256147132
Format: Hardcover
Pub. Date: 1997-09-01
Publisher(s): McGraw Hill College Div
List Price: $133.10

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Table of Contents

Chapter 1 Proactive Decision Making
1(8)
Routine Decisions
2(1)
The Challenges of Proactive Decision Making
3(4)
Alternatives
3(1)
Assumptions--Structure
4(1)
Assumptions--Assessments
5(1)
Performance
6(1)
Summary
7(2)
Chapter 2 Alternatives
9(10)
Small Number of Alternatives
9(2)
Sequential Decisions
11(1)
A Single Decision Quantity
12(5)
Two or More Decision Quantities
17(1)
Decision Rules
17(1)
Summary
18(1)
Chapter 3 Structuring Assumptions in Decision Making
19(23)
Structuring Relationships Using an Influence Diagram
20(6)
Structuring a Sequence of Decisions and Uncertainties Using a Decision Tree
26(5)
Influence Diagrams with Uncertain Quantities
31(3)
Final Examples of How to Develop an Influence Diagram
34(3)
The Use of Influence Diagrams and Decision Trees
37(2)
Case: Destiny Consulting Group
39(3)
Chapter 4 Assessment
42(17)
Sensitivity Analysis
43(5)
The Language of Probability
48(7)
Uncertainties with a Few Potential Outcomes
48(3)
Uncertainties with Many Potential Outcomes
51(1)
Summary Measures of Probability Distributions
52(3)
Deriving the Probability Distribution for Performance
55(1)
Summary
56(3)
Chapter 5 Performance
59(17)
Relevant Monetary Flows
59(3)
Evaluating Alternatives under Uncertainty
62(12)
Few Potential Outcomes
62(5)
Many Potential Outcomes
67(7)
Summary
74(2)
Chapter 6 Risk Management
76(11)
Value of Information
76(5)
Perfect Information
77(2)
Imperfect Information
79(2)
Value of Control
81(2)
Perfect Control
82(1)
Control of Continuously Ranging Quantities
82(1)
Adding Value and Reducing Risk
83(3)
Summary
86(1)
Chapter 7 Evaluating Multiperiod Performance
87(16)
Cash Flow
88(3)
An Example
89(2)
Time Value of Money
91(7)
Accumulated Value
92(2)
Present Value and Net Present Value
94(3)
Formulas for Accumulated and Present Value Calculations
97(1)
Streams in Perpetuity
97(1)
Pretax versus Aftertax Analyses
98(1)
The Reinvestment Rate
98(5)
Hurdle Rate
99(1)
Internal Rate of Return
99(2)
Nominal versus Effective Rates of Return
101(2)
Chapter 8 Multiobjective and Multistakeholder Choice
103(17)
The Generic Choice Problem
103(2)
Example
104(1)
First-Round Eliminations
105(2)
Dominance
105(2)
Decision Rules without Trade-off Judgments
107(2)
The Lexicographic Rule
108(1)
Satisficing
108(1)
Rate and Weight: Linear Additive Scoring Rules
109(7)
Rating Alternatives
109(1)
Weighting Attributes
110(5)
Assumptions of Rate and Weight
115(1)
Multiple Stakeholder Problems
116(1)
Appendix 1 Comments on the Dependence of Weights on the Scaling of Attributes
116(3)
Exercises
119(1)
Chapter 9 Risk Preference and Utility
120(14)
The Utility of Monetary Consequences
120(3)
Risk Aversion
123(6)
Constant Risk Aversion: Negative Exponential Utility
124(2)
Decreasing Risk Aversion: Logarithmic Utility
126(3)
Using a Utility Curve for Risk Analysis
129(2)
Separation of Risk-Return and Mean-Variance Analysis
131(1)
Corporate Risk Policy
132(1)
Exercises
133(1)
Chapter 10 Competitor Analysis
134(13)
Characterizing Competitive Situations
135(2)
Matrix Format
137(4)
Classical Structures
141(4)
No (or Little) Conflict
141(1)
Prisoner's Dilemma
142(2)
Preemption
144(1)
Summary
145(2)
Chapter 11 Probability Distributions
147(36)
The Language of Probability Distributions
147(9)
The Probability Mass Function
148(1)
The Cumulative Distribution Function
149(3)
Continuous and Many-Valued Uncertain Quantities
152(4)
Assessment: Capturing Personal Judgment
156(4)
An Example of Assessing a Probability Distribution
159(1)
Assessment: Using Historical Data as a Guide
160(8)
Identifying Suitable Data
161(1)
Using the Suitable Data as a Guide
162(5)
Adjusting Data for One Distinguishing Factor
167(1)
Assessment: Appealing to Underlying Structure
168(12)
The Binomial Distribution
169(3)
The Normal Distribution
172(5)
The Poisson Distribution
177(1)
The Exponential Distribution
178(2)
Subjective Biases and Assessment
180(2)
Summary
182(1)
Chapter 12 Sampling
183(16)
Forecasting Sample Results
184(7)
Forecasting a Sample Average
186(2)
Forecasting a Sample Proportion
188(3)
Using Sample Results to Draw Inferences about the Underlying Probability Distribution
191(4)
Inferences about the Mean of the Underlying Probability Distribution
192(2)
Inferences about the Underlying Probability
194(1)
Using Sample Results to Forecast Future Sample Results
195(3)
Using Sample Results to Forecast a Future Sample Average
196(1)
Using Sample Results to Forecast a Future Sample Proportion
197(1)
Summary
198(1)
Chapter 13 Time-Series Forecasting
199(25)
Basic Approaches for One-Period Forecasts
200(3)
Simple Approaches
200(1)
Moving Average
201(1)
Smoothed Average
202(1)
Comparison of Forecasts
203(4)
Precision
204(1)
Bias
205(2)
Exploiting Multiperiod Patterns
207(14)
Treating Seasonality
208(1)
Deseasonalizing a Time Series
208(3)
Forecasting the Deseasonalized Series
211(2)
Reseasonalizing the Forecast
213(1)
Generating the Probability Distribution Forecast
213(1)
Decomposition of Time Series into Seasonality and Trend Components
213(1)
Separating out Seasonality
214(1)
Extrapolating Trend and Cycle Components
215(2)
Holt's Model: Exponential Smoothing with Trend
217(3)
Winter's Model: Exponential Smoothing with Trend and Seasonality
220(1)
Other Advanced Techniques
221(1)
Considerations in Preparing and Using a Forecast
222(2)
Chapter 14 Regression: Forecasting Using Explanatory Factors
224(49)
The Simple Linear Model
224(3)
Fitting the Model Using "Least Squares"
227(2)
Important Properties of the Least-Squares Regression Line
229(1)
Summary Regression Statistics
230(6)
Standard Error of Estimate
232(1)
Adjusted R Square
233(2)
Standard Error of the Coefficients
235(1)
Assumptions behind the Linear Regression Model
236(8)
Linearity
237(2)
Independence
239(2)
Homoscedasticity
241(1)
Normality
242(1)
Summary of Regression Assumptions
243(1)
Model-Building Philosophy
244(11)
Uses of the Linear Model
245(1)
Nature of the Relationship among Variables
246(1)
The Importance of the Underlying Relationship to the Use of the Model
247(2)
Model-Building Procedure
249(4)
Common Mistakes
253(1)
Summary
254(1)
Forecasting Using the Linear Regression Model
255(4)
Point Forecast
255(1)
Interval Forecast
255(2)
Analogy to Simple Random Sampling
257(2)
Using Dummy Variables to Represent Categorical Variables
259(3)
Example
259(2)
Dummy Variables for More than Two Groups
261(1)
Useful Data Transformations
262(11)
Example
263(4)
Choosing a Transformation
267(3)
Transforming the Y-Variable
270(3)
Chapter 15 Discrete-Event Simulation
273(14)
An Example Application of Discrete-Event Simulation
274(9)
The Model
275(8)
Important Issues in Discrete-Event Simulation
283(3)
Calibrating the Uncertainties
283(1)
Validating the Model
284(1)
Avoiding Peculiarities Associated with Start-up
285(1)
Terminating the Model Run
285(1)
Summary
286(1)
Chapter 16 Introduction to Optimization Models
287(45)
Transforming an Evaluation Model into an Optimization Model
288(20)
Example 1: Optimal Order Quantity
288(11)
Example 2: Product Mix Planning
299(2)
Example 3: Facility Location
301(6)
Summary of Examples
307(1)
Categorizing and Solving Optimization Models
308(11)
Example 1: Nonlinear Programming
308(4)
Example 2: Linear Programming
312(2)
Example 3: Integer Programming
314(5)
Uncertainty in Optimization Models: Sensitivity Analysis
319(7)
Lagrange Multipliers
319(3)
Linear Programming Models
322(4)
Building an Optimization Model from Scratch
326(6)
Chapter 17 The Mathematics of Optimization
332(29)
Algebraic Framework for Optimization Models
333(4)
Functions
333(2)
General Structure of an Optimization Model
335(2)
Integer Programming
337(1)
Linear Programming (LP)
337(9)
Graphical Representation of Example 2
338(3)
The Simplex Algorithm
341(3)
Some Final Comments on the Simplex Algorithm and LP
344(1)
Karmarkar's Algorithm: An Alternative Approach to Solving LP Models
345(1)
Nonlinear Programming (NLP)
346(6)
Levers to Control the GS Solution Approach
349(3)
Integer Programming (IP)
352(6)
Final Observations: LP, NLP, and IP
358(2)
Summary
360(1)
Cases
361
Case 1: American Lawbook Corporation (A)
361(11)
Case 2: American Lawbook Corporation (B)
372(3)
Case 3: Amore Frozen Foods
375(6)
Case 4: Athens Glass Works
381(3)
Case 5: Buckeye Power & Light Company
384(5)
Case 6: Buckeye Power & Light Company Supplement
389(8)
Case 7: California Oil Company
397(4)
Case 8: C. K. Coolidge, Inc. (A)
401(12)
Case 9: The Commerce Tavern
413(7)
Case 10: CyberLab: A New Business Opportunity for PRICO (A)
420(8)
Case 11: CyberLab: Supplement
428(2)
Case 12: CyberLab: A New Business Opportunity for PRICO (B)
430(2)
Case 13: Dhahran Roads (A)
432(2)
Case 14: Dhahran Roads (B)
434(2)
Case 15: Discounted Cash Flow Exercises
436(2)
Case 16: Edgcomb Metals (A)
438(9)
Case 17: Florida Glass Company (A)
447(7)
Case 18: Florida Glass Company (A) Supplement
454(3)
Case 19: Foulke Consumer Products, Inc.
457(6)
Case 20: Foulke Consumer Products, Inc., Supplement
463(12)
Case 21: Freemark Abbey Winery
475(3)
Case 22: Galaxy Micro Systems
478(2)
Case 23: Galaxy Micro Systems Supplement
480(1)
Case 24: George's T-Shirts
481(2)
Case 25: Harimann International
483(7)
Case 26: Hightower Department Stores: Imported Stuffed Animals
490(9)
Case 27: International Guidance and Controls
499(2)
Case 28: Jade Shampoo (A)
501(5)
Case 29: Jade Shampoo (B)
506(3)
Case 30: Jaikumar Textiles, Ltd.: The Nylon Division (A)
509(4)
Case 31: Jaikumar Textiles, Ltd.: The Nylon Division (B)
513(2)
Case 32: Lesser Antilles Lines: The Island of San Huberto
515(9)
Case 33: Lightweight Aluminum Company: The Lebanon Plant
524(12)
Case 34: Lorex Pharmaceuticals
536(3)
Case 35: Maxco, Inc., and the Gambit Company
539(7)
Case 36: The Oakland A's (A)
546(9)
Case 37: The Oakland A's (A) Supplement
555(8)
Case 38: The Oakland A's (B)
563(3)
Case 39: Piedmont Airlines: Discount Seat Allocation (A)
566(8)
Case 40: Piedmont Airlines: Discount Seat Allocation (B)
574(5)
Case 41: Probability Assessment Exercise
579(2)
Case 42: Problems in Regression
581(4)
Case 43: Roadway Construction Company
585(3)
Case 44: Shumway, Horch, and Sager (A)
588(3)
Case 45: Shumway, Horch, and Sager (B)
591(4)
Case 46: Sleepmore Mattress Manufacturing: Plant Consolidation
595(5)
Case 47: Sprigg Lane (A)
600(11)
Case 48: T. Rowe Price Associates
611(8)
Case 49: Wachovia Bank and Trust Company, N.A. (B)
619(3)
Case 50: Wachovia Bank and Trust Company, N.A. (B): Supplement
622(3)
Case 51: Waite First Securities
625(7)
Case 52: The Waldorf Property
632

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