# 24140 Theory of Probability

## Course Description

Topics include: Sample spaces, events, the addition rule and multiplication rule, conditional probabilities, the Baye’s theorem, counting techniques(tree diagram, permutations, compinations), the binomial probability distributions, random variables, discrete probability distribution, expected values under variance, the expected value and variance for the binomial probability distribution.

## Learning Objective

• At the end of this course, students are able to:-
• 1. Define probability, use permutations and combinations concepts and apply knowledge gained in probability.
• 2. Explain addition rule, independent events, multiplication rule, conditional probability and Baye’s theorem.
• 3. Analyze binomial distribution, Poison distribution, Hyper geometric distribution, rectangular distribution, Normal distribution, Gama distribution, exponential distribution and Chi-Square distribution.

3

## Books for this Course

• 1. introduction to mathematical Statistics , Paul G.Hoel , 5th edition
• 2. Statistics concept and applications , David R. Anderson,dennis J Sweeney and thomas williams

• September
• January

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