1. Random Variables Binomial and Hypergeometric Probabilities
Chapter 3
Random Variables
Binomial and Hypergeometric Probabilities

2. Z = the length of the fish in inches
Random Variables
Informally: A rule for assigning a real number to each outcome of a random experiment
A fish is randomly selected from a lake. Let the random variable
Z = the length of the fish in inches
A voter is randomly selected and asked if he or she supports a candidate running for office. Let the random variable

3. Random Variables
Definition Let S be the sample space of a random experiment. A random variable is a function The set of possible values of X is called the range of X (also called the support of X).

4. Random Variables A random variable is a function.
A random variable is not a probability.
Random variables can be defined in practically any way. Their values do not have to be positive or between 0 and 1 as with probabilities.
Random variables are typically named using capital letters such as X, Y , or Z. Values of random variables are denoted with their respective lower-case letters. Thus, the expression
X = x
means that the random variable X has the value x.

5. Discrete Random Variables
Definition A random variable is discrete if its range is either finite or countable.
Chapter 3 – Continuous random variables

6. 2.2 – Probability Mass Functions
Definition Let X be a discrete random variable and let R be the range of X. The probability mass function (abbreviated p.m.f.) of X is a function f : R → ℝ that satisfies the following three properties:

7. Distribution
Definition The distribution of a random variable is a description of the probabilities of the values of variable.

8. Example 2.2.1
A bag contains one red cube and one blue cube. Consider the random experiment of selecting two cubes with replacement. The two cubes we select are called the sample. The same space is S = {RR, RB, BR, BB}, and we assume that each outcome is equally likely. Define the random variable X = the number of red cubes in the sample

9. Example 2.2.1 Values of X: Values of the p.m.f.:
X(RR) = 2, X(RB) = 1, X(BR) = 1, X(BB) = 0
Values of the p.m.f.:

10. Example 2.2.1
The Distribution Table Probability Histogram Formula

11. Uniform Distribution
Definition Let X be a discrete random variable with k elements in its range R. X has a uniform distribution (or be uniformally distributed) if its p.m.f. is

12. Example 2.2.2
Consider the random experiment of rolling a fair six-sided die. The sample space is S ={1, 2, 3, 4, 5, 6} Let the random variable X be the number of dots on the side that lands up. Then (i.e. X has a uniform distribution)

13. Cumulative Distribution Function
Definition The cumulative distribution function (abbreviated c.d.f.) of a discrete random variable X is

14. Example 2.2.4
Consider the random experiment of rolling two fair six-sided dice and calculating the sum. Let the random variable X be this sum.

15. Example 2.2.4
Consider the random experiment of rolling two fair six-sided dice and calculating the sum. Let the random variable X be this sum.

16. Mode and Median
Definition The mode of a discrete random variable X is a value of X at which the p.m.f. f is maximized. The median of X is the smallest number m such that

17. Example 2.2.1
Mode = 1
Median = 1 since

18. 2.3 – Hypergeometric and Binomial Distributions
Definition Consider a random experiment that meets the following requirements:
We select n objects from a population of N without replacement (n ≤ N).
The N objects are of two types, call them type I and type II, where N1 are of type I and N2 are of type II (N1 + N2 = N).
Define the random variable
X = the number of type I objects in the sample of n.

19. Hypergeometric Distribution
Definition (continued) Then X has a hypergeometric distribution and its p.m.f. is
The numbers N, n, N1, and N2 are called the parameters of the random variable.

20. Example 2.3.2
A manufacturer of radios receives a shipment of 200 transistors, four of which are defective. To determine if they will accept the shipment, they randomly select 10 transistors and test each. If there is more than one defective transistor in the sample, they reject the entire shipment. Find the probability that the shipment is not rejected.

21. Example 2.3.2 X = number of defectives in the sample of 10
X has a hypergeometric distribution with
N = 200, n = 10, N1 = 4, and N2 = 196.

22. Bernoulli Experiment
Definition A random experiment is called a Bernoulli experiment if each outcome is classified into exactly one of two distinct categories.
These two categories are often called success and failure.
If a Bernoulli experiment is repeated several times in such a way that the probability of a success does not change from one iteration to the next, the experiments are said to be independent.
A sequence of n Bernoulli trials is a sequence of n independent Bernoulli experiments.

23. Binomial Distribution
Definition Consider a random experiment that meets the following requirements:
A sequence of n Bernoulli trials is performed
The probability of a success in any one trial is p
Define the random variable
X = the number of successes in the n trials

24. Binomial Distribution
Definition (continued) Then X has a binomial distribution and its p.m.f. is
The numbers n and p are the parameters of the distribution
The phrase “X is b(n, p)” means the variable X has a binomial distribution with parameters n and p

25. Example 2.3.4
If a family with nine children is randomly chosen, find the probability of selecting a family with exactly four boys.
Let X = the number of boys in the family
X is b(9, 0.5)

26. Unusual Events
Unusual Event Principle: If we make an assumption (or a claim) about a random experiment, and then observe an event with a very small probability based on that assumption (called an “unusual event”), then we conclude that the assumption is most likely incorrect.

27. Unusual Events
Unusual Event Guideline: If X is b(n, p) and represents the number of successes in a random experiment, then an observed event consisting of exactly x successes is considered “unusual” if

28. Example 2.3.7
Suppose a university student body consists of 70% females. To discuss ways of improving dormitory policies, the President selects a panel of 15 students. He claims to have randomly selected the students, however, one administrator questions this claim because there are only five females on the panel. Use the rare event principle to test the claim of randomness.

29. Example 2.3.7 Let X = number of females on the panel of 15
If randomly selected, then X would be approximately b(15, 0.7)
Conclusion: Reject the claim of randomness