Course Description
Basic classical models of probability, Random experiment, Sample space, Events, Axioms of probability, definition of probability, Conditional probability , Bayes theorem, Random variables and their types, Mathematical expectation , Independent random variables, Central and noncentral moments , Measures of skewness and kurtosis, Distributions of function of one and two random variable, Moment generating functions , Probability generating function , Special discrete and continuous distribution, Random vectors and their distributions, Marginal and conditional distributions ,Transformations , Law of large numbers and the central limit theorem.
Course Objectives & Outcomes
The objectives of this course are :
- Get a good knowledge of probability theory, various properties and distributions and random variables Properties.
- Get a good ability to structure and analyze practical problems using probability theory.
Upon successful completion of this course, the student will be able to:
- Identify basic concepts and methods of probability theory
- Identify the probability of discrete and continuous random variables
- Define the meaning and uses of the law of large numbers and the central limit theorem.
- List the concepts and applications of the moment and probability generating function.
- Different between discrete and continuous probability distributions.
- Solve the applications of marginal and conditional distributions for single and bivariate random variables and their expectations.
- Relate the distributions by transformations.
- Discuss random vectors and their distributions
References
1. Ross.S.(2010), A First Course In Probability,8th ed., Pearson.ISBN-13: 978-0-13-603313-4
ISBN-10: 0-13-603313-X.
2. Devore, J. , Berk, K.(2012),Modern Mathematical Statistics with Applications , Thomson ISBN 978-1-4614-0391-3.
Course ID: STAT 306
Credit hours | Theory | Practical | Laboratory | Lecture | Studio | Contact hours | Pre-requisite | 3 | 2 | 2 | 4 | MATH 303 - MATH 205 |
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