Lecture29 - Lecture 29 Confidence Intervals Section 8.1...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
Confidence Intervals Section 8.1 Lecture 29
Background image of page 1

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

View Full DocumentRight Arrow Icon
Confidence Intervals The Central Limit Theorem tells us that the average of a large number of independent, identically distributed random variables is approximately normally distributed: 2 1 1 ~, n i i X X N nn   
Background image of page 2
Example Let X be the outcome of the roll of a fair six- sided die. Then E ( X ) = 3.5 and Var ( X ) = 2.917. If we roll the die many times, then we would expect the average outcome to be close to μ = 3.5. But how close? And how certain can we be?
Background image of page 3

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

View Full DocumentRight Arrow Icon
Definition The number μ is an attribute of the dice. It may be unknown in practice. A fair die has μ = 3.5 but you may not be sure that the die is fair. μ is called a parameter of the distribution of X . The value depends on the experiment. If another person rolls the die many times, he may obtain a slightly different . This number is called a statistic . What allows us to do computations is that we know the distribution of the statistic.
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 02/06/2012 for the course STAT 225 taught by Professor Martin during the Spring '08 term at Purdue University.

Page1 / 13

Lecture29 - Lecture 29 Confidence Intervals Section 8.1...

This preview shows document pages 1 - 5. Sign up to view the full document.

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