MCCCD Program Description

Mathematical Analysis for Business

Course: MAT218

First Term: 2021 Fall

Lecture 4.0 Credit(s) 4.0 Period(s) 4.0 Load
Subject Type: Academic
Load Formula: S - Standard Load

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 linear systems with two and three equations using various methods, including matrices. (I) 2. Use technology to solve application problems with 3+ variables. (I) 3. Solve linear programming problems using the graphical method. (II) 4. Solve multivariable optimization problems with and without constraints. (II, III) 5. Solve counting problems using various counting techniques. (IV) 6. Define probability using sample spaces, and apply to real-world scenarios. (V, VI) 7. Define basic statistics (measure of central tendency and dispersion), and apply to real-world problems. (V) 8. Describe properties of discrete and continuous probability distributions, and apply to solve real-world problems. (V, VI) 9. Describe the normal distribution and its characteristics. (VI) 10. Find probabilities for normal random variables by using the normal distribution. (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. 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 II. Linear programming A. Graphical method B. Applications III. Multivariable optimization A. Partial differentiation and 3D surfaces B. Unconstrained optimization C. Lagrange multipliers D. Applications IV. Probability A. Sample spaces and events B. Counting techniques 1. Fundamental counting principle 2. Permutations 3. Combinations C. Conditional probability D. Independent events E. Bayes’ Theorem V. Basics of statistics and discrete probability distributions A. Introduction to sampling, population versus sample, parameters versus statistics B. Measures of central tendency 1. Mean 2. Median 3. Mode C Measures of dispersion: variance, standard deviation D. Discrete random variables E. Expectation F. 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: May 25, 2021

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.