Normal Binomial Or Poisson?.

Slides:



Advertisements
Similar presentations
ฟังก์ชั่นการแจกแจงความน่าจะเป็น แบบไม่ต่อเนื่อง Discrete Probability Distributions.
Advertisements

Graphics Calculator Probability Distributions Graphics Calculator Binomial Dist Graphics Calc Poisson Dist Graphics Calc Normal Dist.
1 Set #3: Discrete Probability Functions Define: Random Variable – numerical measure of the outcome of a probability experiment Value determined by chance.
Biostatistics Unit 4 Probability.
Probability Distribution
Biostatistics Unit 4 - Probability.
Probability Distributions & STATA
Essential Question: How do you calculate the probability of a binomial experiment?
More Discrete Probability Distributions
5-1 Business Statistics Chapter 5 Discrete Distributions.
Kate Schwartz & Lexy Ellingwood CHAPTER 8 REVIEW: THE BINOMIAL AND GEOMETRIC DISTRIBUTIONS.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Discrete Random Variables Chapter 4.
1 If we can reduce our desire, then all worries that bother us will disappear.
Binomial Distributions Calculating the Probability of Success.
Binomial Distributions Introduction. There are 4 properties for a Binomial Distribution 1. Fixed number of trials (n) Throwing a dart till you get a bulls.
Poisson Random Variable Provides model for data that represent the number of occurrences of a specified event in a given unit of time X represents the.
Introduction Discrete random variables take on only a finite or countable number of values. Three discrete probability distributions serve as models for.
Geometric Distribution
Biostatistics Class 3 Discrete Probability Distributions 2/8/2000.
Discrete Probability Distributions. Random Variable Random variable is a variable whose value is subject to variations due to chance. A random variable.
Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
McGraw-Hill/IrwinCopyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 5 Discrete Random Variables.
© 2005 McGraw-Hill Ryerson Ltd. 5-1 Statistics A First Course Donald H. Sanders Robert K. Smidt Aminmohamed Adatia Glenn A. Larson.
Math b (Discrete) Random Variables, Binomial Distribution.
Definition A random variable is a variable whose value is determined by the outcome of a random experiment/chance situation.
Normal approximation of Binomial probabilities. Recall binomial experiment:  Identical trials  Two outcomes: success and failure  Probability for success.
The Binomial Distribution
4.2 Binomial Distributions
Statistics Chapter 6 / 7 Review. Random Variables and Their Probability Distributions Discrete random variables – can take on only a countable or finite.
Ch. 15H continued. * -applied to experiments with replacement ONLY(therefore…..independent events only) * -Note: For DEPENDENT events we use the “hypergeometric.
4.3 More Discrete Probability Distributions NOTES Coach Bridges.
Probability Distributions, Discrete Random Variables
6.2 BINOMIAL PROBABILITIES.  Features  Fixed number of trials (n)  Trials are independent and repeated under identical conditions  Each trial has.
Chapter 6: Continuous Probability Distributions A visual comparison.
Binomial Distribution Memory trick. bi no mi al.
This is a discrete distribution. Situations that can be modeled with the binomial distribution must have these 4 properties: Only two possible outcomes.
1 7.3 RANDOM VARIABLES When the variables in question are quantitative, they are known as random variables. A random variable, X, is a quantitative variable.
6.2 Binomial Distributions Recognize and calculate probabilities that are binomial distributions Use the probabilities and expected values to make decision.
Lesson The Normal Approximation to the Binomial Probability Distribution.
Copyright © 2011 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin Chapter 5 Discrete Random Variables.
Free Powerpoint Templates ROHANA BINTI ABDUL HAMID INSTITUT E FOR ENGINEERING MATHEMATICS (IMK) UNIVERSITI MALAYSIA PERLIS ROHANA BINTI ABDUL HAMID INSTITUT.
Chapter 6: Continuous Probability Distributions A visual comparison.
Unit 4 Review. Starter Write the characteristics of the binomial setting. What is the difference between the binomial setting and the geometric setting?
Ch5: Discrete Distributions 22 Sep 2011 BUSI275 Dr. Sean Ho HW2 due 10pm Download and open: 05-Discrete.xls 05-Discrete.xls.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Business Statistics,
Binomial Distribution. First we review Bernoulli trials--these trial all have three characteristics in common. There must be: Two possible outcomes, called.
Chap 5-1 Chapter 5 Discrete Random Variables and Probability Distributions Statistics for Business and Economics 6 th Edition.
Fractiles Given a probability distribution F(x) and a number p, we define a p-fractile x p by the following formulas.
Copyright (C) 2002 Houghton Mifflin Company. All rights reserved. 1 Understandable Statistics Seventh Edition By Brase and Brase Prepared by: Lynn Smith.
SWBAT: -Calculate probabilities using the geometric distribution -Calculate probabilities using the Poisson distribution Agenda: -Review homework -Notes:
Chapter Five The Binomial Probability Distribution and Related Topics
Special Discrete Distributions
Discrete Probability Distributions
Business Statistics Topic 4
Binomial Distribution
Discrete Random Variables
ENGR 201: Statistics for Engineers
Business Statistics, 5th ed. by Ken Black
Discrete Distributions
Chapter 4 Discrete Probability Distributions.
Business Statistics Chapter 5 Discrete Distributions.
Discrete Probability Distributions
If the question asks: “Find the probability if...”
Lecture 11: Binomial and Poisson Distributions
Introduction to Probability and Statistics
Introduction to Probability Distributions
Elementary Statistics
Bernoulli Trials Two Possible Outcomes Trials are independent.
Introduction to Probability Distributions
STARTER P = 2A + 3B E(P) = 2 x x 25 = 135
Presentation transcript:

Normal Binomial Or Poisson?

normal

N=natural

O=O STANDARD DEVIATION

R=Real life continuous

M=Mean

A=area under

An example of the normal curve is the weight of new born lambs. L= lambs An example of the normal curve is the weight of new born lambs.

Binomial Distribution Memory trick

bi

no

mi

al

Means two There are TWO possible outcomes Success or Failure bi Means two There are TWO possible outcomes Success or Failure

no No. stands for number There are a fixed number of identical trials. Discrete – bar graph not histogram!

Mi sounds like me. Me? I am dependent on no one! I am independent!

All trials have the SAME probability Add another l to get All All trials have the SAME probability Constant probability!

How do I spot it? Check each of the four conditions. You will be given, or be able to calculate the probability p. You will be given the number of trials.

BPD Binomial Probability Distribution Exactly a certain number (x) of successful trials

BCD Binomial Cumulative Distribution Up to and including a certain number (x) of successful trials

PPD Poisson Probability Distribution Exactly a certain number (x) of “occurrences of the event” with no upper limit

PCD Poisson Cumulative Distribution Up to and including a certain number (x) of occurrences of the event

Normal Binomial Or Poisson?