STAT 2040 Lecture Notes - Lecture 4: Hypergeometric Distribution, Random Variable, Binomial Distribution

• School: University of Guelph
• Department: Statistics
• Course: STAT 2040
• Professor: Jeremy Balka

Document Summary

**not covered in stat*2040*: introduction to the multinomial distribution (11:15) (http://youtu. be/syvw7dgvuay) Other supporting videos for this chapter (not given elsewhere in this chapter): discrete probability distributions: some examples (binomial, poisson, hypergeo- metric, geometric) (14:51) (http://youtu. be/jm_ch-iesbg, overview of some discrete probability distributions (binomial,geometric,hypergeometric,poisson,negative binomial)(6:21) (http://youtu. be/uroxrvg9oye) The concept of a random variable is an important one in statistics, and it arises frequently in statistical inference. A random variable is a variable that takes on numerical values according to a chance process. Random variables can be either discrete or continuous. Discrete random variables can take on a countable number of possible values. Continuous random variables can take on an in nite number of possible values, correspond- ing to all values in an interval. 0 we are subject to certain measuring constraints, but conceptually, all values are possible. Height (cm) (a) the distribution of the number of heads in 25 tosses (b) the distribution of the height of adult cana- dian males.