Probability

Course #OA3101

Est.imated Completion Time: 3 months

Overview

Probability axioms, counting techniques, conditional probability. Discrete and continuous probability distributions: binomial, hypergeometric, negative binomial, Poisson, normal, exponential, gamma, and others. Joint probability distributions, conditional distributions and conditional expectation, linear functions. Random samples, probability plots. CO-REQUISITES: MA1118 and MA3042.

Corequisites

MA1118
MA3042

Learning Outcomes

After successfully completing this course, students will be able to:

Identify the components that comprise a probability model, and the quantities that would need to be estimated in order to draw quantitative conclusions from the model.
Use counting techniques to solve probability problems involving equally likely outcomes.
Compute conditional probabilities using their definition and using Bayes’ Rule.
Identify commonly used discrete probability distributions, articulate their underlying assumptions, and use them to compute probabilities.
Identify commonly used continuous probability distributions, articulate their underlying assumptions, and use them to compute probabilities.
Compute probabilities using joint probability distributions.

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Application Deadlines

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