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

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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.

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

Major Assignments/Exams

• Homework
• Quizzes
• Exams
• Final Exam

Course topics

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

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