  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
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