1 Week 3.. 2 3 NORMAL DISTRIBUTION BERNOULLI TRIALS BINOMIAL DISTRIBUTION EXPONENTIAL DISTRIBUTION UNIFORM DISTRIBUTION POISSON DISTRIBUTION.

Slides:



Advertisements
Similar presentations
Special random variables Chapter 5 Some discrete or continuous probability distributions.
Advertisements

Discrete Uniform Distribution
STA291 Statistical Methods Lecture 13. Last time … Notions of: o Random variable, its o expected value, o variance, and o standard deviation 2.
The Bernoulli distribution Discrete distributions.
ฟังก์ชั่นการแจกแจงความน่าจะเป็น แบบไม่ต่อเนื่อง Discrete Probability Distributions.
Probability Distribution
5.1 Sampling Distributions for Counts and Proportions.
NORMAL APPROXIMATION TO THE BINOMIAL A Bin(n, p) random variable X counts the number of successes in n Bernoulli trials with probability of success p on.
Bernoulli Distribution
A random variable that has the following pmf is said to be a binomial random variable with parameters n, p The Binomial random variable.
Copyright © 2014 by McGraw-Hill Higher Education. All rights reserved.
Chapter 5 Discrete Probability Distributions
Mutually Exclusive: P(not A) = 1- P(A) Complement Rule: P(A and B) = 0 P(A or B) = P(A) + P(B) - P(A and B) General Addition Rule: Conditional Probability:
The Binomial Distribution. Introduction # correct TallyFrequencyP(experiment)P(theory) Mix the cards, select one & guess the type. Repeat 3 times.
Discrete and Continuous Distributions G. V. Narayanan.
Section 15.8 The Binomial Distribution. A binomial distribution is a discrete distribution defined by two parameters: The number of trials, n The probability.
Statistics for Engineer Week II and Week III: Random Variables and Probability Distribution.
Statistics 1: Elementary Statistics Section 5-4. Review of the Requirements for a Binomial Distribution Fixed number of trials All trials are independent.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. All Rights Reserved. Section 5-3 Binomial Probability Distributions.
Binomial Distributions Calculating the Probability of Success.
BINOMIAL DISTRIBUTION Success & Failures. Learning Goals I can use terminology such as probability distribution, random variable, relative frequency distribution,
The Binomial and Geometric Distribution
Chapter 7 Lesson 7.5 Random Variables and Probability Distributions
4.5 Comparing Discrete Probability Distributions.
Random Variables. A random variable X is a real valued function defined on the sample space, X : S  R. The set { s  S : X ( s )  [ a, b ] is an event}.
Copyright © 1998, Triola, Elementary Statistics Addison Wesley Longman 1 Binomial Experiments Section 4-3 & Section 4-4 M A R I O F. T R I O L A Copyright.
Bernoulli Trials Two Possible Outcomes –Success, with probability p –Failure, with probability q = 1  p Trials are independent.
CHAPTER Discrete Models  G eneral distributions  C lassical: Binomial, Poisson, etc Continuous Models  G eneral distributions 
Discrete Probability Distributions. Random Variable Random variable is a variable whose value is subject to variations due to chance. A random variable.
Binomial Probability Distribution
Math 22 Introductory Statistics Chapter 8 - The Binomial Probability Distribution.
Binomial Distributions. Quality Control engineers use the concepts of binomial testing extensively in their examinations. An item, when tested, has only.
COMP 170 L2 L17: Random Variables and Expectation Page 1.
1 Since everything is a reflection of our minds, everything can be changed by our minds.
LECTURE 19 THURSDAY, 29 OCTOBER STA 291 Fall
Methodology Solving problems with known distributions 1.
The Binomial Distribution
LECTURE 12 TUESDAY, 10 MARCH STA 291 Spring
IE 300, Fall 2012 Richard Sowers IESE. 8/30/2012 Goals: Rules of Probability Counting Equally likely Some examples.
Chapter 3 Discrete Random Variables and Probability Distributions  Random Variables.2 - Probability Distributions for Discrete Random Variables.3.
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.
Probability Models Chapter 17. Bernoulli Trials  The basis for the probability models we will examine in this chapter is the Bernoulli trial.  We have.
LECTURE 18 TUESDAY, 27 OCTOBER STA 291 Fall
Business Statistics: A Decision-Making Approach, 7e © 2008 Prentice-Hall, Inc. Chap 5-1 Business Statistics: A Decision-Making Approach 7 th Edition Chapter.
Section 9-3 Probability. Probability of an Event if E is an event in a sample space, S, of equally likely outcomes, then the probability of the event.
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.
Business Statistics, A First Course (4e) © 2006 Prentice-Hall, Inc. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Business Statistics,
Chap 5-1 Chapter 5 Discrete Random Variables and Probability Distributions Statistics for Business and Economics 6 th Edition.
2.2 Discrete Random Variables 2.2 Discrete random variables Definition 2.2 –P27 Definition 2.3 –P27.
Statistics -Continuous probability distribution 2013/11/18.
Probability Distributions ( 확률분포 ) Chapter 5. 2 모든 가능한 ( 확률 ) 변수의 값에 대해 확률을 할당하는 체계 X 가 1, 2, …, 6 의 값을 가진다면 이 6 개 변수 값에 확률을 할당하는 함수 Definition.
SWBAT: -Calculate probabilities using the geometric distribution -Calculate probabilities using the Poisson distribution Agenda: -Review homework -Notes:
Binomial Distribution
Math 4030 – 4a More Discrete Distributions
Chapter 5 Joint Probability Distributions and Random Samples
Chapter 4 Continuous Random Variables and Probability Distributions
Chapter 3 Discrete Random Variables and Probability Distributions
STATISTICS AND PROBABILITY
Probability Review for Financial Engineers
Discrete random variable X Examples: shoe size, dosage (mg), # cells,…
Normal Approximations to the Binomial Distribution
Binomial Distribution
Section 3: Estimating p in a binomial distribution
If the question asks: “Find the probability if...”
Calculating probabilities for a normal distribution
Chapter 3 : Random Variables
Bernoulli Trials Two Possible Outcomes Trials are independent.
Each Distribution for Random Variables Has:
Chapter 11 Probability.
Presentation transcript:

1 Week 3.

2

3 NORMAL DISTRIBUTION BERNOULLI TRIALS BINOMIAL DISTRIBUTION EXPONENTIAL DISTRIBUTION UNIFORM DISTRIBUTION POISSON DISTRIBUTION

4 note the point of inflexion note the balance point

5 SD=15 MEAN = 100 point of inflexion

6 5 50

8 ~68% Illustrated for the Standard Normal Mean=0, SD=1

9 ~95% Illustrated for the Standard normal Mean=0, SD=1

~68/2 =34% ~95/2=47.5%

~68/2 =34% ~95/2=47.5%

12 15 IQ Z Standard Normal

13

14 P(Z > 0) = P(Z < 0 ) = 0.5 P(Z > ) = P(0 < Z < ) = = P(Z < ) = P(0 < Z < ) = =

15 x p(x) 1 p (1 denotes “success”) 0 q (0 denotes “failure”) __ 1 0 < p < 1 q = 1 - p

16 P(success) = P(X = 1) = p P(failure) = P(X = 0) = q e.g. X = “sample voter is Democrat” Population has 48% Dem. p = 0.48, q = 0.52 P(X = 1) = 0.48

17 P(S1 S2 F3 F4 F5 F6 S7) = p 3 q 4 just write P(SSFFFFS) = p 3 q 4 “the answer only depends upon how many of each, not their order.” e.g. 48% Dem, 5 sampled, with-repl: P(Dem Rep Dem Dem Rep) =

18 e.g. P(exactly 2 Dems out of sample of 4) = P(DDRR) + P(DRDR) + P(DDRR) + P(RDDR) + P(RDRD) + P(RRDD) = ~ There are 6 ways to arrange 2D 2R.

19 e.g. P(exactly 3 Dems out of sample of 5) = P(DDDRR) + P(DDRDR) + P(DDRRD) + P(DRDDR) + P(DRDRD) + P(DRRDD) + P(RDDDR) +P(RDDRD) + P(RDRDD) + P(RRDDD) = ~ There are 10 ways to arrange 3D 2R. Same as the number of ways to select 3 from 5.

20 5! ways to arrange 5 things in a line Do it thus (1:1 with arrangements): select 3 of the 5 to go first in line, arrange those 3 at the head of line then arrange the remaining 2 after. 5! = (ways to select 3 from 5) 3! 2! So num ways must be 5! /( 3! 2!) = 10.

21 Let random variable X denote the number of “S” in n independent Bernoulli p-Trials. By definition, X has a Binomial Distribution and for each of x = 0, 1, 2, …, n P(X = x) = (n!/(x! (n-x)!) ) p x q n-x e.g. P(44 Dems in sample of 100 voters) = (100!/(44! 56!)) =

22 n!/(x! (n-x)!) is the count of how many arrangments there are of a string of x letters “S” and n-x letters “F.”. p x q n-x is the shared probability of each string of x letters “S” and n-x letters “F.” (define 0! = 1, p 0 = q 0 = 1 and the formula goes through for every one of x = 0 through n) is short for the arrangement count =

23