8-4 - 8.4: The Binomial Distribution Another important type...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 8.4: The Binomial Distribution Another important type of random variable for which we have a for- mula for P ( X = x ) is called the binomial random variable . If X is a binomial random variable, then it has the following charac- teristics: The experiment consists of a fixed number, n , of repeated trials. The outcome of each trial is classified as either a success, or as a failure. The outcome of any one trial is independent of the outcomes of the other trials. For each trial, the probability of success, p , and the probability of failure, q , are constant. The random variable X counts the number of successes. An example of a binomial random variable is the following. Consider the experiment where a coin is flipped 100 times, and where the ran- dom variable X counts the number of times heads is flipped. This is binomial, as there are a fixed number of trials ( n = 100 flips), with each flip characterized as a success (heads) or a failure (tails). The probability of success (probability of heads) is 0 . 5 for each flip, and the probability of failure (probability of tails) is 0 . 5 for each flip. The flips are independent, and the random variable X counts the number of successes (number of heads)....
View Full Document

Page1 / 4

8-4 - 8.4: The Binomial Distribution Another important type...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online