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Larry Goldstein has received several distinguished teaching awards, given more than fifty Conference and Colloquium talks & addresses, and written more than fifty books in math and computer programming. He received his PhD at Princeton and his BA and MA at the University of Pennsylvania. He also teaches part time at Drexel University.
David Schneider, who is known widely for his tutorial software, holds a BA degree from Oberlin College and a PhD from MIT. He is currently an associate professor of mathematics at the University of Maryland. He has authored eight widely used math texts, fourteen highly acclaimed computer books, and three widely used mathematics software packages. He has also produced instructional videotapes at both the University of Maryland and the BBC.
Martha Siegel holds a BA from Russell Sage College, attended Rensselear Polytechnic Institute as a special student, and received his PhD at the University of Rochester. From 1966 until 1971 she taught at Goucher University in Baltimore. Since 1971 she has been a professor at Towson State University, also in Maryland. Professor Siegel has been on the writing team of this book since the fifth edition and is also the co-author of a precalculus reform book.
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| Preface | |
| Index of Applications | |
| Linear Equations and Straight Lines | |
| Coordinate Systems and Graphs | |
| Linear Inequalities | |
| The Intersection Point of a Pair of Lines | |
| The Slope of a Straight Line | |
| The Method of Least Squares | |
| Break-even Analysis | |
| Matrices | |
| Solving Systems of Linear Equations, I | |
| Solving Systems of Linear Equations, II | |
| Arithmetic Operations on Matrices | |
| The Inverse of a Matrix | |
| The Gauss-Jordan Method for Calculating Inverses | |
| Input-Output Analysis | |
| Population Dynamics | |
| Linear Programming, A Geometric Approach | |
| A Linear Programming Problem | |
| Linear Programming, I | |
| Linear Programming, II | |
| Shadow Prices | |
| The Simplex Method | |
| Slack Variables and the Simplex Tableau | |
| The Simplex Method, I: Maximum Problems | |
| The Simplex Method, II: Minimum Problems | |
| Sensitivity Analysis and Matrix Formulations of Linear Programming Problems | |
| Duality | |
| Shadow Prices | |
| Sets and Counting | |
| Sets | |
| A Fundamental Principle of Counting | |
| Venn Diagrams and Counting | |
| The Multiplication Principle | |
| Permutations and Combinations | |
| Further Counting Problems | |
| The Binomial Theorem | |
| Multinomial Coefficients and Partitions | |
| Pascal's Triangle | |
| Probability | |
| Introduction | |
| Experiments, Outcomes, Samples and Events | |
| Assignment of Probabilities | |
| Calculating Probabilities of Events | |
| Conditional Probability and Independence | |
| Tree Diagrams | |
| Bayes' Theorem | |
| Simulation | |
| Two Paradoxes | |
| Probability and Statistics | |
| Representation of Data | |
| Frequency and Probability Distributions | |
| Binomial Trials | |
| The Mean | |
| The Variance and Standard Deviation | |
| The Normal Distribution | |
| Normal Approximation to the Binomial Distribution | |
| An Unexpected Expected Value | |
| Markov Processes | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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