Chapter7

# Chapter7 - Chapter 7 KVANLI PAVUR KEELING Click to edit...

This preview shows pages 1–12. Sign up to view the full content.

Click to edit Master subtitle style 3/13/11 Chapter 7 Continuous Probability Distributions KVANLI PAVUR KEELING

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

View Full Document
3/13/11 Chapter Objectives v Keeping thinking about two questions: 1. What is the difference between a discrete distribution and a continuous distribution?
3/13/11 Continuous Random Variables v In Chapter 6, X = v In Chapter 7, X = v Examples : height, weight, length, length of time, … v Example : X = height (male, adult) v X is a continuous random variable counting somethi ng measuri ng somethi ng This is a continuous random variable This is a discrete random variable

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

View Full Document
3/13/11 Continuous Random Variables v What are the chances X is exactly 6’? Claims he is exactly 6’ Suppose we have a measuring device that can measure heights to any number of decimal places It turns out that his height is 6.000000000000000000000 00001
3/13/11 P(X=?) = 0 v Is this person’s height exactly 6’? v No - - really close, but not exactly 6’ v What are the chances that a male height is exactly 6’? v Very small - - how small? v In fact, it is zero ! v So, P(X = 6’) is 0

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

View Full Document
3/13/11 Continuous Random Variables v How about the chances that a male height is exactly 5.5’? v This is also zero v So, P(X = 5.5’) is 0 v In fact, P(X = any value) is 0 v Does it make sense to talk about probabilities for X = height (any continuous random variable)? v Yes, as we will see, but not “=“ This is 5’ 6”
3/13/11 Continuous Random Variables v In Chapter 6, we could list the values of X __ with probability __ with probability X = __ with probability __ with probability __ with probability

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

View Full Document
3/13/11 Continuous Random Variables v We can find these probabilities: 1. Chances that a height is more than 6’ is (not zero) Written: P(X > 6’) 2. Chances that a height is less than 5.5’ is (not zero) Written: P(X < 5.5’)
3/13/11 Continuous Random Variables v A nice thing in this chapter (any continuous random variable) is that you need not worry about whether you should include the equal sign, “=“, in your inequalities v For example, P(X > 6’) is the same as P(X ≥ 6’) since P(X = 6’) is zero and P(X > 6’) is the same as P(X ≥ 6’)

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

View Full Document
3/13/11 Continuous Random Variables v We will assume that male heights (and female heights) follow a bell-shaped curve (normal) v The next slide illustrates the bell- shaped curve for male heights v Central Limit Theorem : When obtaining large samples (generally n>30) from any population, the
3/13/11 Bell-Shaped Curve for Male Heights X = ht. Point where the curve changes shape – called an inflection point It turns out that this is the standard deviation of X Symbol: σ (sigma) We’ll assume σ = .25’ (3”) Need to know: 1. Where is the middle? 2. How wide is it?

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.

## This note was uploaded on 03/12/2011 for the course DSCI 2710 taught by Professor Hossain during the Summer '08 term at North Texas.

### Page1 / 64

Chapter7 - Chapter 7 KVANLI PAVUR KEELING Click to edit...

This preview shows document pages 1 - 12. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online