Excursions in Modern Mathematics, 10th edition — by Peter Tannenbaum | Complete Test Bank (Ch. 1-17)

TEST BANK
Excursions in Modern Mathematics, 10th edition
by Peter Tannenbaum
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, Table of Content
PART I: SOCIAL CHOICE
1. The Mathematics of Elections: The Paradoxes of Democracy
1.1 The Basic Elements of an Election
1.2 The Plurality Method
1.3 The Borda Count Method
1.4 The Plurality-with-Elimination Method
1.5 The Method of Pairwise Comparisons
1.6 Fairness Criteria and Arrow's Impossibility Theorem
Conclusion
Key Concepts
Exercise
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2. The Mathematics of Power: Weighted Voting
2.1 An Introduction to Weighted Voting
2.2 Banzhaf Power
2.3 Shapley-ShubikPower
2.4 Subsets and Permutations
Conclusion
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Key Concepts
Exercises
3. The Mathematics of Sharing: Fair-Division Games
3.1 Fair-Division Games
3.2 The Divider-Chooser Method
3.3 The Lone-Divider Method
3.4 The Lone-Chooser Method
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3.5 The Method of Sealed Bids
3.6 The Method of Markers
Conclusion
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Key Concepts
Exercises
4. The Mathematics of Apportionment: Making the Rounds
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4.1 Apportionment Problems and Apportionment Methods
4.2 Hamilton's Method
4.3 Jefferson's Method
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4.4 Adams's and Webster's Methods
4.5 The Huntington-Hill Method
4.6 The Quota Rule and Apportionment Paradoxes
Conclusion
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Key Concepts
Exercises
PART II: MANAGEMENT SCIENCE
5. The Mathematics of Getting Around: Euler Paths and Circuits
5.1 Street-Routing Problems
5.2 An Introduction to Graphs
5.3 Euler's Theorems and Fleury's Algorithm
5.4Eulerizingand Semi-EulerizingGraphs
Conclusion
Key Concepts
Exercises
6. The Mathematics of Touring: Traveling Salesman Problems

,6.1 What Is a Traveling Salesman Problem?
6.2 Hamilton Paths and Circuits
6.3 The Brute-Force Algorithm
6.4 The Nearest-Neighbor and Repetitive Nearest-Neighbor Algorithms
6.5 The Cheapest-Link Algorithm
Conclusion
Key Concepts
Exercises
7. The Mathematics of Networks: The Cost of Being Connected
7.1 Networks and Trees
7.2 Spanning Trees, MSTs, andMaxSTs
7.3 Kruskal's Algorithm
Conclusion
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Key Concepts
Exercises
8. The Mathematics of Scheduling: Chasing the Critical Path
8.1 An Introduction to Scheduling
8.2 Directed Graphs
8.3 Priority-List Scheduling
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8.4 The Decreasing-Time Algorithm
8.5 Critical Paths and the Critical-Path Algorithm
Conclusion
Key Concepts
Exercises
PART III: GROWTH
9. Population Growth Models: There Is Strength in Numbers
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9.1 Sequences and Population Sequences
9.2 The Linear Growth Model
9.3 The Exponential Growth Model
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9.4 The Logistic Growth Model
Conclusion
Key Concepts
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Exercises
10. Financial Mathematics: Money Matters
10.1 Percentages
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10.2 Simple Interest
10.3 Compound Interest
10.4 Retirement Savings
10.5 Consumer Debt
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Conclusion
Key Concepts
Exercises
PART IV: SHAPE AND FORM
11. The Mathematics of Symmetry: Beyond Reflection
11.1 Rigid Motions
11.2 Reflections
11.3 Rotations
11.4 Translations
11.5 Glide Reflections
11.6 Symmetries and Symmetry Types
11.7 Patterns

, Conclusion
Key Concepts
Exercises
12. Fractal Geometry: The Kinky Nature of Nature
12.1 The Koch Snowflake and Self-Similarity
12.2 The Sierpinski Gasket and the Chaos Game
12.3 The Twisted Sierpinski Gasket
12.4 The Mandelbrot Set
Conclusion
Key Concepts
Exercises
13. Fibonacci Numbers and the Golden Ratio: Tales of Rabbits and Gnomons
13.1 Fibonacci Numbers
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13.2 The Golden Ratio
13.3 Gnomons
13.4 Spiral Growth in Nature
Conclusion
Key Concepts
Exercises
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PART V: STATISTICS
14.Censuses, Surveys, Polls, and Studies: The Joys of Collecting Data
14.1 Enumeration
14.2 Measurement
14.3 Cause and Effect
Conclusion
Key Concepts
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Exercises
15. Graphs, Charts, and Numbers: The Data Show and Tell
15.1 Graphs and Charts
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15.2 Means, Medians, and Percentiles
15.3 Ranges and Standard Deviations
Conclusion
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Key Concepts
Exercises
16. Probabilities, Odds, and Expectations: Measuring Uncertainty and Risk
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16.1 Sample Spaces and Events
16.2 The Multiplication Rule, Permutations, and Combinations
16.3 Probabilities and Odds
16.4 Expectations
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16.5 Measuring Risk
Conclusion
Key Concepts
Exercises

17. The Mathematics of Normality: The Call of the Bell
17.1 Approximately Normal Data Sets
17.2 Normal Curves and Normal Distributions
17.3 Modeling Approximately Normal Distributions
17.4 Normality in Random Events
Conclusion
Key Concepts