Syllabus

Text used by Indiana University:
Finite Mathematics, Fifth Edition by D.P. Maki and M. Thompson

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Part 1: Probability Methods

Chapter 1: Sets, Partitions, and Tree Diagrams

• 1.1 Review of Sets and Set Operations
• 1.2 Venn Diagrams and Partitions
• 1.3 Sizes of Sets
• 1.4 Sets of Outcomes and Trees

Chapter 2: Probabilities, Counting, and Equally Likely Outcomes

• 2.1 Probabilities Events and Equally Likely Outcomes
• 2.2 Counting Arrangements: Permutations
• 2.3 Counting Partitions: Combinations
• 2.4 Computing Probabilities by Using Equally Likely Outcomes

Chapter 3: Probability

• 3.1 Probability Measures: Axioms and Properties
• 3.2 Conditional Probabilities
• 3.3 Stochastic Processes and Trees
• 3.4 Bayes Probabilities
• 3.5 Bernoulli Trials

Chapter 4: Random Variables, Averages, and Statistics

• 4.1 Random Variables and Probability Density Functions
• 4.2 Expected Values and Standard Deviations of Random Variables

Part 2: Linear Models

Chapter 5: Systems of Linear Equations

• 5.1 Review of Equations and Graphs of Lines
• 5.2 Formulation and Solution in Two Variables
• 5.3 Formulation and Solution in Three or More Variables

Chapter 6: Matrix Algebra and Applications

• 6.1 Matrix Notation and Algebra
• 6.2 Matrix Inverses
• 6.3 A Linear Economic Model

Chapter 7: Linear Programming: Modeling and Graphical Solution

• 7.1 Formulation of Linear Programming Problems
• 7.2 Systems of Linear Inequalities in Two or More Variables
• 7.3 Graphical Solutions of Linear Programming Problems with Two Variables

Chapter 8: Markov Chains

• 8.1 States, Transitions, Transition Diagrams, Transition Matrices
• 8.2 Basic Properties of Markov Chains
• 8.3 Regular Markov Chains