# chapter6 - Introduction to Probability and Statistics...

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

2011/09/13 1 Introduction to Probability and Statistics Thirteenth Edition Chapter 6 The Normal Probability Distribution Continuous Random Variables • Continuous random variables can assume the infinitely many values corresponding to points on a line interval. Examples: –Heights, weights –length of life of a particular product – experimental laboratory error Continuous Random Variables • A smooth curve describes the probability distribution of a continuous random variable. •The depth or density of the probability, which varies with x , may be described by a mathematical formula f ( x ) , called the probability distribution or probability density function for the random variable x . Properties of Continuous Probability Distributions • The area under the curve is equal to 1. • P(a x b) = area under the curve between a and b. •There is no probability attached to any single value of x . That is, P( x = a) = 0. Continuous Probability Distributions There are many different types of continuous random variables We try to pick a model that Fits the data well Allows us to make the best possible inferences using the data. One important continuous random variable is the normal random variable . The Normal Distribution deviation. standard and mean population the are and 1416 . 3 7183 . 2 for 2 1 ) ( 2 2 1   e x e x f x • The shape and location of the normal curve changes as the mean and standard deviation change.

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

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

### Page1 / 4

chapter6 - Introduction to Probability and Statistics...

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

View Full Document
Ask a homework question - tutors are online