OA3101 Probability

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.

Corequisite

MA1118, MA3042

Lecture Hours

4

Lab Hours

1

Course 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.