MCCCD Program Description

Mathematical Analysis for Business

Course: MAT217

First Term: 2006 Fall

Lecture 3.0 Credit(s) 3.0 Period(s) 3.0 Load
Subject Type: Academic
Load Formula: S

Description: An introduction to the mathematics required for the study of business. Includes multivariable optimization, Lagrange multipliers, linear programming, linear algebra, probability, random variables, discrete and continuous distributions

MCCCD Official Course Competencies
1. Solve multivariable optimization problems with and without constraints. (I, II) 2. Solve linear programming problems using Duality Theory. (II) 3. Solve linear systems with two and three equations using various matrix methods. Use technology to solve application problems. (III) 4. Solve counting problems using various counting techniques. (IV) 5. Solve probability applications. (V, VI) 6. Distinguish between continuous and discrete variables. (VI) 7. Find probabilities for normal random variables by using the standard normal distribution. (VI) 8. Describe the normal distribution and its characteristics. (VI)
MCCCD Official Course Competencies must be coordinated with the content outline so that each major point in the outline serves one or more competencies. MCCCD faculty retains authority in determining the pedagogical approach, methodology, content sequencing, and assessment metrics for student work. Please see individual course syllabi for additional information, including specific course requirements.

MCCCD Official Course Outline
I. Multivariable optimization A. Partial differentiation and 3D surfaces B. Unconstrained optimization C. Lagrange multipliers D. Applications II. Linear programming A. Duality theory B. Applications III. Systems of linear equations and matrices A. Matrices applied to a system of linear equations B. Solving systems of linear equations using the Gauss-Jordan and elimination methods C. Inverse matrices and their applications to solve a system of linear equations D. Determinants E. Cramer`s rule IV. Probability A. Sample Spaces and Events B. Fundamental principle of counting C. Conditional probability D. Independent events E. Bayes` theorem V. Discrete probability distributions A. Discrete random variables B. Expectation C. Bernouilli trials and the binomial distribution VI. Continuous probability distributions A. Review of Integration B. Continuous random variables C. Uniform and exponential distributions D. Standard Normal Curve E. Normal Curves F. Normally Distributed Populations G. Normally Distributed Random Variables

MCCCD Governing Board Approval Date: 4/25/2006

All information published is subject to change without notice. Every effort has been made to ensure the accuracy of information presented, but based on the dynamic nature of the curricular process, course and program information is subject to change in order to reflect the most current information available.