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ISBN13: 978-0395909133

ISBN10: 0395909139

Cover type:

Edition: 2ND 95

Copyright: 1995

Publisher: Houghton Mifflin Harcourt

Published: 1995

International: No

ISBN10: 0395909139

Cover type:

Edition: 2ND 95

Copyright: 1995

Publisher: Houghton Mifflin Harcourt

Published: 1995

International: No

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.

ISBN10: 0395909139

Cover type:

Edition: 2ND 95

Copyright: 1995

Publisher: Houghton Mifflin Harcourt

Published: 1995

International: No

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

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.