**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**

- A Beginning Library of Elementary Functions
- Linear Equations and Inequalities
- Graphs and Lines
- Linear Regression
- Additional Elementary Functions

- Functions
- Elementary Functions: Graphs and Transformations
- Quadratic Functions
- Polynomial and Rational Functions
- Exponential Functions
- Logarithmic Functions
- Mathematics of Finance
- Simple Interest
- Compound and Continuous Compound Interest
- Future Value of an Annuity; Sinking Funds
- Present Value of an Annuity; Amortization
- Systems of Linear Equations; Matrices
- Review: Systems of Linear Equations in Two Variables
- Systems of Linear Equations and Augmented Matrices
- Gauss-Jordan Elimination
- Matrices: Basic Operations
- Inverse of a Square Matrix
- Matrix Equations and Systems of Linear Equations

4.7 Leontief Input-Output Analysis - Linear Inequalities and Linear Programming
- Inequalities in Two Variables
- Systems of Linear Inequalities in Two Variables
- Linear Programming in Two Dimensions: A Geometric Approach
- Linear Programming: Simplex Method
- A Geometric Introduction to the Simplex Method
- The Simplex Method: Maximization with Problem Constraints of the Form _
- The Dual; Minimization with Problem Constraints of the form _
- Maximization and Minimization with Mixed Problem Constraints
- Logic, Sets, and Counting
- Logic
- Sets
- Basic Counting Principles
- Permutations and Combinations
- Probability
- Sample Spaces, Events, and Probability
- Union, Intersection, and Complement of Events; Odds
- Conditional Probability, Intersection, and Independence
- Bayes’ Formula
- Random Variables, Probability Distribution, and Expected Value
- Markov Chains
- Properties of Markov Chains
- Regular Markov Chains
- Absorbing Markov Chains