COLLEGE OF SCIENCE,
ENGINEERING AND
TECHNOLOGY

math 135: Mathematics for Business and Economic Analysis I – Business Algebra (3)


Course Prerequisites/Corequisites: MATH 131 or a passing score on the mathematics portion of the TSI Assessment.

Course Description:Topics from college algebra (linear equations, quadratic equations, functions and graphs, inequalities), mathematics of finance (simple and compound interest, annuities), linear programming, matrices, systems of linear equations, applications to management, economics, and business. Listed as MATH 1324 in the Texas Common Course Numbering System.

Textbook: : College Mathematics for Business, Economics, Life Sciences and Social Sciences 12/e, by Barnett/Ziegler/Byleen

Course topics

  1. A Beginning Library of Elementary Functions
  2. Linear Equations and Inequalities
  3. Graphs and Lines
  4. Linear Regression
    1. Additional Elementary Functions
  5. Functions
  6. Elementary Functions: Graphs and Transformations
  7. Quadratic Functions
  8. Polynomial and Rational Functions
  9. Exponential Functions
  10. Logarithmic Functions
  11. Mathematics of Finance
  12. Simple Interest
  13. Compound and Continuous Compound Interest
  14. Future Value of an Annuity; Sinking Funds
  15. Present Value of an Annuity; Amortization
  16. Systems of Linear Equations; Matrices
  17. Review: Systems of Linear Equations in Two Variables
  18. Systems of Linear Equations and Augmented Matrices
  19. Gauss-Jordan Elimination
  20. Matrices: Basic Operations
  21. Inverse of a Square Matrix
  22. Matrix Equations and Systems of Linear Equations
    4.7 Leontief Input-Output Analysis
  23. Linear Inequalities and Linear Programming
  24. Inequalities in Two Variables
  25. Systems of Linear Inequalities in Two Variables
  26. Linear Programming in Two Dimensions: A Geometric Approach
  27. Linear Programming: Simplex Method
  28. A Geometric Introduction to the Simplex Method
  29. The Simplex Method: Maximization with Problem Constraints of the Form _
  30. The Dual; Minimization with Problem Constraints of the form _
  31. Maximization and Minimization with Mixed Problem Constraints
  32. Logic, Sets, and Counting
  33. Logic
  34. Sets
  35. Basic Counting Principles
  36. Permutations and Combinations
  37. Probability
  38. Sample Spaces, Events, and Probability
  39. Union, Intersection, and Complement of Events; Odds
  40. Conditional Probability, Intersection, and Independence
  41. Bayes’ Formula
  42. Random Variables, Probability Distribution, and Expected Value
  43. Markov Chains
  44. Properties of Markov Chains
  45. Regular Markov Chains
  46. Absorbing Markov Chains