Syllabus

download pdf

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

* We are not affiliated with Indiana University, the authors, or publishers of the textbook.
   We do not use specific content or problems from the text.

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