UNR, MATH/STAT 352, Spring 2007. 2 summands UNR, MATH/STAT 352, Spring 2007.

Slides:



Advertisements
Similar presentations
JMB Chapter 6 Part 1 v4 EGR 252 Spring 2012 Slide 1 Continuous Probability Distributions Many continuous probability distributions, including: Uniform.
Advertisements

Generating Multivariate Gaussian
Kin 304 Regression Linear Regression Least Sum of Squares
E(X 2 ) = Var (X) = E(X 2 ) – [E(X)] 2 E(X) = The Mean and Variance of a Continuous Random Variable In order to calculate the mean or expected value of.
Normal distribution (3) When you don’t know the standard deviation.
Copyright © 2008 Pearson Education, Inc. Chapter 11 Probability and Calculus Copyright © 2008 Pearson Education, Inc.
Variance and Standard Deviation The Expected Value of a random variable gives the average value of the distribution The Standard Deviation shows how spread.
1 Engineering Computation Part 6. 2 Probability density function.
Statistics Lecture 16. Gamma Distribution Normal pdf is symmetric and bell-shaped Not all distributions have these properties Some pdf’s give a.
UNR, MATH/STAT 352, Spring Random variable (rv) – central concept of probability theory Numerical description of experiment.
UNR, MATH/STAT 352, Spring Time EruptionWaiting timeEruption.
Rules for means Rule 1: If X is a random variable and a and b are fixed numbers, then Rule 2: If X and Y are random variables, then.
CSE 221: Probabilistic Analysis of Computer Systems Topics covered: Confidence intervals.
UNR * STAT 758 * Fall White noise Periodogram of white noise.
Standard Normal Distribution
2003/04/23 Chapter 3 1頁1頁 3.6 Expected Value of Random Variables.
UNR, MATH/STAT 352, Spring MATH/STAT 352: Quiz 0.
UNR, MATH/STAT 352, Spring Binomial(n,p) UNR, MATH/STAT 352, Spring 2007.
The moment generating function of random variable X is given by Moment generating function.
UNR, MATH/STAT 352, Spring Head Tail Tossing a symmetric coin You are paying $1 How much should you get to make the game fair?
UNR, MATH/STAT 352, Spring Definition: Corollary:
UNR, MATH/STAT 352, Spring Radar target detection How reliable is the signal on the screen? (Is it a target of a false alarm?)
511 Friday March 30, 2001 Math/Stat 511 R. Sharpley Lecture #27: a. Verification of the derivation of the gamma random variable b.Begin the standard normal.
1 Confidence Intervals for Means. 2 When the sample size n< 30 case1-1. the underlying distribution is normal with known variance case1-2. the underlying.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
5-1 Two Discrete Random Variables Example Two Discrete Random Variables Figure 5-1 Joint probability distribution of X and Y in Example 5-1.
Normal and Sampling Distributions A normal distribution is uniquely determined by its mean, , and variance,  2 The random variable Z = (X-  /  is.
Basics of Sampling Theory P = { x 1, x 2, ……, x N } where P = population x 1, x 2, ……, x N are real numbers Assuming x is a random variable; Mean/Average.
Further distributions
Normal distribution (2) When it is not the standard normal distribution.
Review of Probability Concepts ECON 4550 Econometrics Memorial University of Newfoundland Adapted from Vera Tabakova’s notes SECOND.
Normal distribution and intro to continuous probability density functions...
Derivation of the Beta Risk Factor
Probability and Statistics Dr. Saeid Moloudzadeh Random Variables/ Distribution Functions/ Discrete Random Variables. 1 Contents Descriptive.
§ 5.3 Normal Distributions: Finding Values. Probability and Normal Distributions If a random variable, x, is normally distributed, you can find the probability.
Statistics. A two-dimensional random variable with a uniform distribution.
By Satyadhar Joshi. Content  Probability Spaces  Bernoulli's Trial  Random Variables a. Expectation variance and standard deviation b. The Normal Distribution.
Statistics and Probability Theory
Topic 6: The distribution of the sample mean and linear combinations of random variables CEE 11 Spring 2002 Dr. Amelia Regan These notes draw liberally.
1 Sampling distributions The probability distribution of a statistic is called a sampling distribution. : the sampling distribution of the mean.
CE 525. ESRI VIDEO Take notes! is/player.cfm
Section 10.4 Analysis of Variance. Section 10.4 Objectives Use one-way analysis of variance to test claims involving three or more means Introduce (mention.
Statistics -Continuous probability distribution 2013/11/18.
8.2 The Geometric Distribution 1.What is the geometric setting? 2.How do you calculate the probability of getting the first success on the n th trial?
Ch5.4 Central Limit Theorem
Random Variable 2013.
1 Random, normal, es =
Project Management Simulation, U-Distribution
Linear Combination of Two Random Variables
AP Statistics: Chapter 7
Means and Variances of Random Variables
An Example of {AND, OR, Given that} Using a Normal Distribution
Random Sampling Population Random sample: Statistics Point estimate
Review of Probability Concepts
CI for μ When σ is Unknown
اختر أي شخصية واجعلها تطير!
Probability & Statistics Probability Theory Mathematical Probability Models Event Relationships Distributions of Random Variables Continuous Random.
أهداف الفصل الفرق بين الموازنة المرنة والثابتة .
The Normal Probability Distribution Summary
Working with Continuous Random Variables and the Normal Distribution
Consider the following problem
Estimation Interval Estimates Industrial Engineering
Two-way analysis of variance (ANOVA)
Tutorial 9 Suppose that a random sample of size 10 is drawn from a normal distribution with mean 10 and variance 4. Find the following probabilities:
Random WALK, BROWNIAN MOTION and SDEs
Use the graph of the given normal distribution to identify μ and σ.
The Geometric Distributions
Combining Random Variables
Chapter 7 The Normal Distribution and Its Applications
RANDOM NUMBERS SET # 1:
Presentation transcript:

UNR, MATH/STAT 352, Spring 2007

2 summands UNR, MATH/STAT 352, Spring 2007

3 summands UNR, MATH/STAT 352, Spring 2007

4 summands UNR, MATH/STAT 352, Spring 2007

5 summands UNR, MATH/STAT 352, Spring 2007

6 summands UNR, MATH/STAT 352, Spring 2007

7 summands UNR, MATH/STAT 352, Spring 2007

8 summands UNR, MATH/STAT 352, Spring 2007

9 summands UNR, MATH/STAT 352, Spring 2007

10 summands UNR, MATH/STAT 352, Spring 2007

20 summands UNR, MATH/STAT 352, Spring 2007

30 summands UNR, MATH/STAT 352, Spring 2007

40 summands UNR, MATH/STAT 352, Spring 2007

50 summands UNR, MATH/STAT 352, Spring 2007

100 summands UNR, MATH/STAT 352, Spring 2007

1000 summands UNR, MATH/STAT 352, Spring 2007

Consider Normal random variable X with mean 5 and variance 1

UNR, MATH/STAT 352, Spring 2007

Consider Normal random variable X with mean 5 and variance 1, consider now Y = X 2

UNR, MATH/STAT 352, Spring 2007

Consider Normal random variable X with mean 5 and variance 1, consider now Y = ( X-5 ) 2

UNR, MATH/STAT 352, Spring 2007