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Applied Finite Mathematics provides balanced and comprehensive coverage of the math topics that business, economics, and life and social science majors need to know. The author employs both traditional math methods and interesting applications to show relationships among topics.
Note: Each Chapter includes a Chapter Review.
I. Fundamentals of Linear Mathematics
1. Linear Equations and Linear Systems
1.1 Equations and Graphing
1.2 Linear Equations: More on Graphing
1.3 Applications of Linear Equations
1.4 Solving Linear Equations with Two Variables
2. Matrices and Linear Systems
2.1 Introduction to Matrices
2.2 Matrix Multiplication
2.3 Matrix Methods for Linear Equation Systems
2.4 Gauss-Jordan Techniques
2.5 Matrix Inverses
2.6 Input-Output Analysis.
II. Strategic Decision Making with Linear Programming
3. A Geometrical Approach to Linear Programming
3.1 What Is Linear Programming?
3.2 Linear Inequalities and Their Graphs
3.3 Geometric Solutions to Linear Programming Problems
3.4 Applications
3.5 When the Geometric Method Fails
4. An Algebraic Approach to Linear Programming
4.1 Introducing the Algebraic Approach
4.2 Maximization Using the Simplex Method
4.3 Matrices and the Simplex Method
4.4 Duality and Minimum Problems
4.5 Maximization with Mixed Constraints
III. Decisions Under Uncertainty
5. Sets and Counting
5.1 Introduction to Sets
5.2 Set Operations
5.3 Counting Elements of Sets
5.4 Counting and Permutations
5.5 Combinations
6. Introduction to Probability
6.1 Sample Spaces
Basic Properties of Probability
6.2 Equally Likely Probabilities
6.3 Probability and Set Theory
6.4 Conditional Probability
6.5 Bayes' Theorem
6.6 Expectation
7. Markov Chains
7.1 Introduction to Markov Chains
7.2 Regular Markov Chains
7.3 Absorbing Markov Chains
8. Decisions and Games
8.1 Introduction to Game Theory
8.2 Games of Mixed Strategy
8.3 Optimal Mixed Strategies
IV. Prediction and Planning
9. Statistics
9.1 Descriptive Statistics
9.2 Statistics and Probability
Organizing Data
9.3 Averages and Measures of Central Tendency
9.4 Distribution Functions
The Normal Distribution
9.5 The Binomial Distribution
10. Financial Mathematics
10.1 Simple and Compound Interest
10.2 Annuities and Their Values
10.3 Present Value: Amortization
10.4 Application: Inflation Financial Planning
11. Introducing Difference Equations
11.1 Difference Equations and Change
11.2 Solving Difference Equations
11.3 Long-Term Behavior of Difference Equations
11.4 Uses and Applications of Difference Equations
12. Using Graphs to Plan
12.1 Networks and an Introduction to CPM
12.2 The Critical Plan Method
12.3 Uncertain Times of Completion
Appendices:
A: Algebraic Review
B: Geometric Series
C: Hypothesis Testing: Chi-Square Methods
D: Mathematical Tables
Credits
Index.
Applied Finite Mathematics provides balanced and comprehensive coverage of the math topics that business, economics, and life and social science majors need to know. The author employs both traditional math methods and interesting applications to show relationships among topics.
Table of Contents
Note: Each Chapter includes a Chapter Review.
I. Fundamentals of Linear Mathematics
1. Linear Equations and Linear Systems
1.1 Equations and Graphing
1.2 Linear Equations: More on Graphing
1.3 Applications of Linear Equations
1.4 Solving Linear Equations with Two Variables
2. Matrices and Linear Systems
2.1 Introduction to Matrices
2.2 Matrix Multiplication
2.3 Matrix Methods for Linear Equation Systems
2.4 Gauss-Jordan Techniques
2.5 Matrix Inverses
2.6 Input-Output Analysis.
II. Strategic Decision Making with Linear Programming
3. A Geometrical Approach to Linear Programming
3.1 What Is Linear Programming?
3.2 Linear Inequalities and Their Graphs
3.3 Geometric Solutions to Linear Programming Problems
3.4 Applications
3.5 When the Geometric Method Fails
4. An Algebraic Approach to Linear Programming
4.1 Introducing the Algebraic Approach
4.2 Maximization Using the Simplex Method
4.3 Matrices and the Simplex Method
4.4 Duality and Minimum Problems
4.5 Maximization with Mixed Constraints
III. Decisions Under Uncertainty
5. Sets and Counting
5.1 Introduction to Sets
5.2 Set Operations
5.3 Counting Elements of Sets
5.4 Counting and Permutations
5.5 Combinations
6. Introduction to Probability
6.1 Sample Spaces
Basic Properties of Probability
6.2 Equally Likely Probabilities
6.3 Probability and Set Theory
6.4 Conditional Probability
6.5 Bayes' Theorem
6.6 Expectation
7. Markov Chains
7.1 Introduction to Markov Chains
7.2 Regular Markov Chains
7.3 Absorbing Markov Chains
8. Decisions and Games
8.1 Introduction to Game Theory
8.2 Games of Mixed Strategy
8.3 Optimal Mixed Strategies
IV. Prediction and Planning
9. Statistics
9.1 Descriptive Statistics
9.2 Statistics and Probability
Organizing Data
9.3 Averages and Measures of Central Tendency
9.4 Distribution Functions
The Normal Distribution
9.5 The Binomial Distribution
10. Financial Mathematics
10.1 Simple and Compound Interest
10.2 Annuities and Their Values
10.3 Present Value: Amortization
10.4 Application: Inflation Financial Planning
11. Introducing Difference Equations
11.1 Difference Equations and Change
11.2 Solving Difference Equations
11.3 Long-Term Behavior of Difference Equations
11.4 Uses and Applications of Difference Equations
12. Using Graphs to Plan
12.1 Networks and an Introduction to CPM
12.2 The Critical Plan Method
12.3 Uncertain Times of Completion
Appendices:
A: Algebraic Review
B: Geometric Series
C: Hypothesis Testing: Chi-Square Methods
D: Mathematical Tables
Credits
Index.