Normal Distribution. A lot of our real life experience could be explained by normal distribution. Normal distribution is important for statisticians for.

Slides:



Advertisements
Similar presentations
The Normal Distribution
Advertisements

1.3 Density Curves and Normal Distributions. What is a density curve?
Statistical Review for Chapters 3 and 4 ISE 327 Fall 2008 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including:
CONTINUOUS RANDOM VARIABLES These are used to define probability models for continuous scale measurements, e.g. distance, weight, time For a large data.
Chapter 8 – Normal Probability Distribution A probability distribution in which the random variable is continuous is a continuous probability distribution.
Statistics Lecture 14. Example Consider a rv, X, with pdf Sketch pdf.
Chap 6-1 Statistics for Business and Economics, 6e © 2007 Pearson Education, Inc. Chapter 6 Continuous Random Variables and Probability Distributions Statistics.
Chapter 6: Some Continuous Probability Distributions:
Normal distribution and introduction to continuous random variables and continuous probability density functions...
Chris Morgan, MATH G160 March 2, 2012 Lecture 21
Business Statistics: A First Course, 5e © 2009 Prentice-Hall, Inc. Chap 6-1 Chapter 6 The Normal Distribution Business Statistics: A First Course 5 th.
Continuous Probability Distributions A continuous random variable can assume any value in an interval on the real line or in a collection of intervals.
6.3 Use Normal Distributions
Chap 6-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall Chapter 6 The Normal Distribution Business Statistics: A First Course 6 th.
Copyright © 2013, 2009, and 2007, Pearson Education, Inc. 1 PROBABILITIES FOR CONTINUOUS RANDOM VARIABLES THE NORMAL DISTRIBUTION CHAPTER 8_B.
1 Normal Random Variables In the class of continuous random variables, we are primarily interested in NORMAL random variables. In the class of continuous.
QBM117 Business Statistics Probability and Probability Distributions Continuous Probability Distributions 1.
Normal distribution (2) When it is not the standard normal distribution.
Business Statistics: A Decision-Making Approach, 6e © 2005 Prentice-Hall, Inc. Chap 5-1 Introduction to Statistics Chapter 6 Continuous Probability Distributions.
Dan Piett STAT West Virginia University Lecture 7.
COMPLETE f o u r t h e d i t i o n BUSINESS STATISTICS Aczel Irwin/McGraw-Hill © The McGraw-Hill Companies, Inc., l Using Statistics l The Normal.
Chapter 5 The Normal Curve. In This Presentation  This presentation will introduce The Normal Curve Z scores The use of the Normal Curve table (Appendix.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Continuous Random Variables Chapter 6.
Chapter 6 The Standard Deviation as a Ruler and the Normal Model.
Continuous distributions For any x, P(X=x)=0. (For a continuous distribution, the area under a point is 0.) Can ’ t use P(X=x) to describe the probability.
Math 10 Chapter 6 Notes: The Normal Distribution Notation: X is a continuous random variable X ~ N( ,  ) Parameters:  is the mean and  is the standard.
JMB Ch6 Lecture2 Review EGR 252 Spring 2011 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
Normal distributions The most important continuous probability distribution in the entire filed of statistics is the normal distributions. All normal distributions.
Random Variables Presentation 6.. Random Variables A random variable assigns a number (or symbol) to each outcome of a random circumstance. A random variable.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Stats 95. Normal Distributions Normal Distribution & Probability Events that will fall in the shape of a Normal distribution: –Measures of weight, height,
Continuous Random Variables. Probability Density Function When plotted, discrete random variables (categories) form “bars” A bar represents the # of.
CONTINUOUS RANDOM VARIABLES
Review Continuous Random Variables Density Curves
Basic Business Statistics, 11e © 2009 Prentice-Hall, Inc. Chap 6-1 The Normal Distribution.
1 7.5 CONTINUOUS RANDOM VARIABLES Continuous data occur when the variable of interest can take on anyone of an infinite number of values over some interval.
The Abnormal Distribution
Normal Distributions.
Section 6-1 Overview. Chapter focus is on: Continuous random variables Normal distributions Overview Figure 6-1 Formula 6-1 f(x) =  2  x-x-  )2)2.
Chap 6-1 Chapter 6 The Normal Distribution Statistics for Managers.
Properties of Normal Distributions 1- The entire family of normal distribution is differentiated by its mean µ and its standard deviation σ. 2- The highest.
PROBABILITY DISTRIBUTION. Probability Distribution of a Continuous Variable.
What does data from a normal distribution look like? The shape of histograms developed from small samples drawn from a normal population are somewhat.
4.3 Probability Distributions of Continuous Random Variables: For any continuous r. v. X, there exists a function f(x), called the density function of.
Review Continuous Random Variables –Density Curves Uniform Distributions Normal Distributions –Probabilities correspond to areas under the curve. –the.
Chapter 3: Normal R.V.
Unit 13 Normal Distribution
MTH 161: Introduction To Statistics
4.3 Probability Distributions of Continuous Random Variables:
CONTINUOUS RANDOM VARIABLES
STAT 1301 Chapter 5(a) The Normal Curve
Uniform and Normal Distributions
The Normal Probability Distribution Summary
Chapter 5 Continuous Random Variables and Probability Distributions
4.3 Probability Distributions of Continuous Random Variables:
Use the graph of the given normal distribution to identify μ and σ.
STA 291 Summer 2008 Lecture 9 Dustin Lueker.
10-5 The normal distribution
MATH 2311 Section 4.3.
The normal distribution
Calculating probabilities for a normal distribution
Chapter 6: Some Continuous Probability Distributions:
HS 67 - Intro Health Stat The Normal Distributions
Standard Deviation and the Normal Model
Chapter 6 Continuous Probability Distributions
IE 360: Design and Control of Industrial Systems I
Chapter 5 Continuous Random Variables and Probability Distributions
STA 291 Spring 2008 Lecture 9 Dustin Lueker.
Presentation transcript:

Normal Distribution

A lot of our real life experience could be explained by normal distribution. Normal distribution is important for statisticians for some other reasons. The mean and variance/standard deviation make more sense. Different ways of calculating probability.

Normal distribution PDF: = mean = standard deviation = e =

Normal distribution The parameters of normal distribution: and Shape: pdf and cdf.

Normal distribution What can we say about the shape of the bell curve? ◦1. Bell shaped. ◦2. Symmetric about the mean. ◦3. The highest point is the mean. ◦4. The tails are thin.

Normal Distribution What the parameters mean to us? ◦Mean: Location of the center of the bell curve.  If mean increases, the curve shifts to the right.  If mean decreases, the curve shifts to the left. ◦Standard deviation: Shape of the bell curve (flat?, wide?, tall?)

Normal distribution What else can standard deviation tell us? Actually, the standard deviation of normal distribution can tell us a lot more than the standard deviations of other distributions.

Normal distribution If we have a random variable that follows a normal distribution, then: ◦68.3% of its values fall within ONE standard deviation. ◦95.4% of its values fall within TWO standard deviations. ◦99.7% of its values fall within THREE standard deviations.

Standard Normal Distribution Given a random variable, X, with mean and standard deviation, we can create a standardized version of this random variable: ◦ ◦If we do this to a normally distributed random variable, we get a standard normal random variable. ◦Usually, we use the letter Z to represent it.

Standard Normal Distribution The standardized normal random variable, Z, follows a standard normal distribution. Regardless of the original mean and variance for X, the one before standardization, the mean and standard deviation of Z are: and

Standard Normal Distribution The pdf for Z is: Therefore, whenever we talk about standard normal distribution, we know both its functional form and its parameters.

What is the use of standard normal? For any random variable, we need to know ◦1. How is it defined? ◦2. How can it be used? ◦3. How to find probability under it? ◦4. How to find its mean and variance? Think about how to find probability under normal distribution.

Standard normal distribution 1. If we use the formula for mean and variance: That is hard!!!

Standard Normal Distribution 2. Another way of finding probability under normal distribution, using CDF. NOT CDF of any normal random variable, but CDF of a standard normal random variable. This function is usually denoted as: And that is defined as

Standard Normal Distribution Some probabilities under standard normal distribution: P(Z<1)= ? P(Z<0)=? P(Z>0)=? P(-1<Z<1)=? P(Z<-2)=? P(Z>3)=?

A tip to finding probability under normal distribution Be prepared to draw plots!

Some other probabilities under standard normal distribution P(0<Z<0.5)=? P(1.5<Z<2.5)=? Probabilities like that can be found using standard normal probability table, or Z table. Appendix B in your textbook.

How about just a normal r.v.? If we have a normal random variable, X, instead of standard normal random variable to start with, we can always standardize X to Z and look up the probability in the Z table. Example: X~N(5,4), find the probability P(X<13), P(X<8.6) and P(X<-3.5) Also, find P(2 10|X>3), P(X>10|X>5), P(X<8|X<3) and P(X<3|X<8)

Example A STAT301 midterm has a mean of 71 and standard deviation of What is the probability that someone’s grade is between 60 and 80? 2. What is the probability that someone’s grade is greater than 72? 3. What is the probability that someone’s grade is below 50?

Example If half of the class is going to get A in the above course, so what will be the cutoff for A? This problem uses something we learned before, the percentile. This kind of problem can be solved using a different form of the standardization formula: In the formula, both and are given and the percentile is given by Z.

Example If 10% of students will get A, what is the cutoff? If 25% of students will get A, what is the cutoff?