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CISC 171 Computational Probability and Statistics Units: 3.00
Introduction to the theory of probability. Discrete and continuous random variables, expectation, and variance. Independent random variables, conditional probability, and Bayes Theorem. Discrete and continuous probability distribution functions, including joint distribution functions. One-variable and two-variable statistical measures and hypothesis testing. Applications of statistics in computing. This course may contain group work at the discretion of the instructor.
Learning Hours: 120 (36 Lecture, 84 Private Study)
Requirements: Prerequisite Registration in a Bachelor of Computing Program.
Corequisite CISC 101/3.0 or CISC 102/3.0 or CISC 121/3.0.
Exclusion Maximum of one course from: BIOL 243/3.0; CHEE 209/3.5; CISC 171/3.0; COMM 162/3.0; ECON 250/3.0; GPHY 247/3.0; HSCI 190/3.0; KNPE 251/3.0; NURS 323/3.0; POLS 285/3.0; POLS 385/3.0*; PSYC 202/3.0; SOCY 211/3.0; STAM 200/3.0; STAT 161/3.0; STAT 263/3.0.
Offering Faculty: Faculty of Arts and Science
Course Learning Outcomes:
- Apply probability theory to determine whether events are independent; apply Bayes Theorem when appropriate.
- Analyze statistical measures, including single variable variance and two variable correlation, to draw reasonable conclusions from the data.
- Apply theory of probability distributions to determine an appropriate related distribution for a set of data.
- Compute statistical measures and perform hypothesis testing to determine statistical significance.