MAT 265 - Introduction to Probability, Experimental

 
Description
This course is an introduction to probability theory geared toward students in Science, Technology, Engineering and  Mathematics (STEM) fields.  This course will develop the theoretical framework of probability and will demonstrate how those concepts are applied to real-world problems.  Topics include basic probability; probability distributions and densities; expectation, higher moments and moment generating functions; joint, marginal and conditional distributions; and transformations and functions of random variables.

1. Apply probability rules and counting techniques to solve problems related to basic probability.
2. Combine basic probability and conditional probability to solve real-world problems.
3. Analyze relevant properties of several important probability distributions and densities (including Bernoulli, binomial, Poisson, geometric, uniform, exponential, gamma, beta and normal). 
4. Utilize moment generating functions to find moments of densities.
5. Combine the concepts of joint, marginal and conditional distributions to analyze models using more than one random variable. 
6. Solve problems using transformations and functions of random variables.

1. Basic probability: probability rules and counting 
2. Conditional probability, independence and Bayes’ Theorem
3. Probability distributions and densities (including Bernoulli, binomial, Poisson, geometric, uniform, exponential, gamma, beta and normal)
4. Expectation, higher moments and moment generating functions
5. Joint, marginal and conditional distributions
6. Transformations and functions of random variables

• Critical Thinking & Problem Solving
• Information Literacy
• Quantitative & Scientific Reasoning

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