Features step-by-step examples based on actual data and connects fundamental mathematical modeling skills and decision making concepts to everyday applicabilityFeaturing key linear programming, matrix, and probability concepts, "Finite Mathematics: Models and Applications" emphasizes cross-disciplinary applications that relate mathematics to everyday life. The book provides a unique combination of practical mathematical applications to illustrate the wide use of mathematics in fields ranging from business, economics, finance, management, operations research, and the life and social sciences. In order to emphasize the main concepts of each chapter, "Finite Mathematics: Models and Applications" features plentiful pedagogical elements throughout such as special exercises, end notes, hints, select solutions, biographies of key mathematicians, boxed key principles, a glossary of important terms and topics, and an overview of use of technology. The book encourages the modeling of linear programs and their solutions and uses common computer software programs such as LINDO. In addition to extensive chapters on probability and statistics, principles and applications of matrices are included as well as topics for enrichment such as the Monte Carlo method, game theory, kinship matrices, and dynamic programming.Supplemented with online instructional support materials, the book features coverage including: Algebra SkillsMathematics of FinanceMatrix AlgebraGeometric SolutionsSimplex MethodsApplication ModelsSet and Probability RelationshipsRandom Variables and Probability DistributionsMarkov ChainsMathematical StatisticsEnrichment in Finite MathematicsAn ideal textbook, " Finite Mathematics: Models and Applications "is intended for students in fields from entrepreneurial and economic to environmental and social science, including many in the arts and humanities., Finite Mathematics: Models and Applications connects fundamental mathematical modeling skills and decision making concepts to their everyday applicability and features real-world examples from a variety of areas from business, finance, and management to the life and social sciences.In addition to key linear programming and probability topical coverage, this book emphasizes cross-disciplinary applications in an effort to relate mathematics to everyday life and various careers within business, finance, management, operations research, computer science, and the life and social sciences. Includes: Linear Equations and Mathematical ConceptsMathematics of FinanceMatrix AlgebraLinear Programming - Simplex MethodLinear Programming Application ModelsSet and Probability RelationshipsRandom Variables and Probability DistributionsMarkov ChainsMathematical StatisticsEnrichment in Finite Mathematics, In addition to key linear programming and probability topical coverage, this book emphasizes cross-disciplinary applications in an effort to relate mathematics to everyday life and various careers within business, finance, management, operations research, computer science, and the life and social sciences. The book begins with Linear Equations and Mathematical Concepts to solve linear systems of equations and includes a discussion of elementary and classical algebra concepts such as finding solutions to simple equations and how to graph these equations or systems. Mathematics of Finance addresses simple and compound interest, annuities, and amortization and includes an introduction to arithmetic and geometric sequences. Matrix Algebra includes a basic introduction to matrices for solving systems of equations in linear programming and Markov chains. Linear Programming - Geometric Solutions provides insights into the finite nature of linear programs as well as their optimal solutions, solution space, and limitations while Linear Programming - Simplex Method addresses the use of the simplex method to maximize profit or to minimize costs. Linear Programming Application Models discusses linear programming's wide usage as a mathematical optimization tool in industry and government for allocating resources, which is a prime managerial and decision making skill. Set and Probability Relationships includes an introduction to probability theory with coverage on working with sets, Venn diagrams, and tree diagrams. Random Variables and Probability Distributions addresses two key concepts in probability theory and how they can be used to gain insights to basic probability distributions and the underlying chance phenomena and assumptions. Markov Chains is presented both algebraically and with matrices to model transition diagrams and parallel decision diagrams. Mathematical Statistics discusses the role of probability in building statistical forms from sample data and includes coverage of population and sample statistics, descriptive statistics, uniform distribution, normal distribution, and surveys. The book concludes with Enrichment in Finite Mathematics , which is divided into five sections with a focus on the related applications of game theory, finance and economics, life and social sciences, Monte Carlo method, and dynamic programming.
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