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Apr 16, 2024
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MA 3620 - Random Variables and Statistics3 lecture hours 0 lab hours 3 credits
Course Description
This course introduces elementary probability theory, which includes basic probability concepts such as conditional probability, independent events, multiplication rule, law of total probability and Bayes’ theorem; theory of random variables, both discrete and continuous, single and multiple. This course also introduces elementary inferential statistics, including hypothesis testing . (prereq: MA-232)
Course Learning Outcomes
Upon successful completion of this course, the student will be able to:
• calculate basic probabilities
• recognize and calculate conditional probabilities
• recognize random variables and use them to calculate moments
• distinguish discrete and continuous random variables
• determine cumulative distribution functions and probability density functions and recognize their relevance
• perform hypothesis testing and interpret the results
• perform hypothesis testing and interpret the results for both one- and two-sample testing
Prerequisites by Topic
• Algebra
• Trigonometry
• Differentiation
• Integration
Course Topics
• Course introduction (1 class)
• Basic probability concepts (2 classes)
• Conditional probability/Bayes’ Rule (3 classes)
• Introduction to random variables (1 class)
• Introduction of cumulative distribution functions, probability density functions, and moment-generating functions (4 classes)
• Discussion of various probability distributions including uniform, binomial, Poisson, geometric, exponential, Gaussian. (4 classes)
• Functions of two random variables including joint distribution and density functions and conditional densities (5 classes)
• Basics of descriptive statistics (2-3 classes)
• Exams (2 classes plus the final exam)
Coordinator
Yvonne Yaz
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