Section4.2-R
Introduction
Topic: Binomial Distribution
Presenter: Eddy Kwessi, Department of Mathematics, Trinity University
Date: October 22, 2024
Goals
Study two discrete distributions:
Bernoulli Distribution
Binomial Distribution
Bernoulli Distribution
Definition: A Bernoulli trial has two outcomes: success or failure.
Random Variable Representation:
Let X = 1 for success and X = 0 for failure.
Probabilities: Pr(X = 1) = p, Pr(X = 0) = 1 - p.
Notation: X ∼ Bernoulli(p)
Probability Mass Function (p.m.f):
p(x) = p^x (1 - p)^(1 - x), where x = 0, 1
Population Average:
µX = p
Population Standard Deviation:
σX = sqrt(p(1 - p))
Examples of Bernoulli Trials
Coin Flip:
Success: Getting a head
Pr(X = 1) = 1/2, Pr(X = 0) = 1/2
Die Roll:
Success: Getting an ace
Pr(X = 1) = 1/6, Pr(X = 0) = 5/6
Binomial Distribution
Definition: Conduct n independent Bernoulli trials, success probability p.
Random Variable: X = number of successes in n trials.
Notation: X ∼ Binom(n, p)
p.m.f:
p(x) = (n choose x) * p^x * (1 - p)^(n - x), where x = 0, 1, ..., n
Population Average:
µX = n * p
Population Standard Deviation:
σX = sqrt(n * p * (1 - p))
Example of Binomial Distribution
Rolling a Die:
Let X be the number of 6's rolled in 3 trials.
Possible values: 0, 1, 2, 3.
p.m.f displayed as probability table and general formula.
R-Commands for Binomial Distribution
Calculate Pr(X = k):
dbinom(k, n, p)
Calculate Pr(X ≤ k):
pbinom(k, n, p)
Calculate Pr(X ≥ k):
1 - pbinom(k - 1, n, p)
Practice Problems
Die Example: Probability of rolling a 6 exactly twice with 8 rolls.
Solution involves understanding Binomial distribution parameters and applying p.m.f.
Health Insurance Example: Finding the distribution of insured Americans in a sample.
Use probabilities based on specific criteria and R-commands.
Homework
Read Section 4.2 (Pages 212-213)
Complete Problems: 4, 8, 13, 17, 20 by October 30.