Probability for Life Science

Lecturer: Prof. Herbert Enderton
College/University: University of California, Los Angeles (UCLA)

1. Events, Sample Space, and Probability Function
   
2. Basic Properties of the Probability Function, Multiplication Principle, Addition Principle, Permutation
   
3. Combinations, Permutation of n Objects Not All Distinct
   
4. Other Properties of the Probabilty Function
   
5. Conditional Probability
   
6. Law of Total Probability, Bayes' Rule, and Independent Events
   
7. Independence of Two or More Events
   
8. Discrete Random Variables, Probability Mass Function, Cumulative Distribution Function, and Expected Value
   
9. Properties of Expected Value, Variance and Standard Deviation of a Random Variable
   
10. Binomial Distribution, Joint Distributions, Independent Random Variables and Other Properties
    
11. Review
    
12. Multinomial Distribution, Geometric Distribution
    
13. Expected Value and Standard Deviation of a Geometric Distribution, Negative Binomial Distribution
    
14. Poisson Distribution
    
15. Expected Value, Variance and Standard Deviation of the Poisson Distribution
    
16. Continuous Random Variable, Probability Density Function
    
17. Mean and Variance of Continuous Random Variable, Exponential Distribution
    
18. Variance and Standard Deviation of an Exponential Distribution, Exponential Distribution with Parameter Lamda as a Function of Another Variable
    
19. Standard Normal Distribution and Its Properties
    
20. Normal Distribution with Mean "Mu" and Standard Deviation "Sigma"
    
21. Central Limit Theorem
    
22. Applications of Central Limit Theorem
    
23. Review
    
24. Confidence Interval (Part I)
    
25. Confidence Interval (Part II)
    
26. Markov's Inequality, Chebyshev's Inequality, Law of Large Numbers
    
27. Determining Sample Size and Confidence Interval
    
28. Review