# Academics  /  Courses  /  DescriptionsIEMS 302: Probability

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### Prerequisites

Co-requisite: Math 228-2

### Description

Introduction to probability theory and its applications. Conditional probabilities and expectation values. Random variables and distributions, including binomial, Poisson, exponential, and normal. Joint distributions and limit laws for foundation of and connection to statistics. Examples in reliability, inventory, finance, and statistics.

• This course is a major requirement for Industrial Engineering
• Students may not receive credit for both 302 and any of the following:  EECS 302; Math 310-1, 314, 385; STAT 320-1, 383

LEARNING OBJECTIVES

• Students will know and be able to apply the axioms of probability
• Students will understand the properties of probability distributions and will be able to use them to compute relevant probabilities
• Students will be able to model some problem contexts using an appropriate probability distribution
• Students will be able to recognize and utilize independence, and will understand the limitations when independence does not hold
• Students will be able to identify and apply conditional probabilities

TOPICS

• Basic probability concepts, events and random variables
• Conditional probability and independence
• Discrete and Continuous Random Variables, probability functions
• Independent trials; Binomial, Geometric, and Poisson distributions
• Uniform, Exponential, and Normal distributions
• Joint distributions, conditional distributions
• Limit Theorems

MATERIALS

Recommended: Probability, 9th ed, Sheldon Ross, ISBN-13: 978-0321794772