'Discrete random variables' diaporamas de présentation

Chapter 7

Chapter 7. Please pick up an assignment sheet and notes packet. Random Variable -. A grocery store manager might be interested in the number of broken eggs in each carton (dozen of eggs). OR An environmental scientist might be interested in the amount of ozone in an air sample.

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Risk Analysis & Modelling

Risk Analysis & Modelling. Lecture 3: Statistical Distributions & Dynamic Financial Analysis. www.angelfire.com/linux/riskanalysis riskcoursehq@hotmail.com. Statistical Distributions & Risk Modelling.

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Chapter 6: Modeling Random Events... Normal & Binomial Models

Chapter 6: Modeling Random Events... Normal & Binomial Models. Models. Help us predict what is likely to happen Remember LSRLs (a model for linear, bi- variate data); not exact (some points above line, some below); but best model we have

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Review of Statistical Mechanics

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Combining Probabilities and Conditional Probability

Combining Probabilities and Conditional Probability. Stat 700 Lecture 4 9/11/2001-9/13/2001. Overview of Lecture. Algebra of Events and Probabilities Conditional Probability and its Importance Probability Updating: Bayes Rule Independent Events Discrete Random Variables

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Part 2: Named Discrete Random Variables

Part 2: Named Discrete Random Variables. http://www.answers.com/topic/binomial-distribution. Chapter 13: Bernoulli Random Variables. http://www.boost.org/doc/libs/1_42_0/libs/math/doc/sf_and_dist/html / math_toolkit / dist / dist_ref / dists /bernoulli_dist.html.

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Module 2: Probability

Module 2: Probability . Outline . basic probability definitions random variables probability distributions and densities expected values. Probability. When we perform experiments, there is a random component.

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Chapter 4

Chapter 4. Discrete Random Variables. Discrete Random Variables. 4.1 Two Types of Random Variables 4.2 Discrete Probability Distributions 4.3 The Binomial Distribution 4.4 The Poisson Distribution 4.5 The Hypergeometric Distribution . Random Variables. L01. L02.

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Probability Distribution

Probability Distribution. Binomial Distribution. Probability of Binary Events. Probability of success = p p(success) = p Probability of failure = q p(failure) = q p+q = 1 q = 1-p. Binomial Probability Distribution. A fixed number of observations (trials), n

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Basics on Probability

Basics on Probability. Jingrui He 09/11/2007. Coin Flips. You flip a coin Head with probability 0.5 You flip 100 coins How many heads would you expect. Coin Flips cont. You flip a coin Head with probability p Binary random variable Bernoulli trial with success probability p

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Discrete Random Variables

Discrete Random Variables. Introduction. In previous lectures we established a foundation of the probability theory; we applied the probability theory to some applications; w e covered combinatorial problems; w e studied Bayesian theorem. In this lecture

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Random Variables and Probability Distributions I

Random Variables and Probability Distributions I. Lecture IV. Conditional Probability and Independence. In order to define the concept of a conditional probability it is necessary to discuss joint probabilities and marginal probabilities.

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STAT3600

STAT3600. Lecture 5 Chapter III Discrete Random Variables and Probability Distributions. Discrete Random Variables.

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Defining Probabilities: Random Variables

Defining Probabilities: Random Variables. Examples: Out of 100 heart catheterization procedures performed at a local hospital each year, the probability that more than five of them will result in complications is __________

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Discrete Random Variables 3

Discrete Random Variables 3. To be able to calculate the expected value and variance of a discrete random variable To investigate the effect of multipliers and constants on the expected value and the variance of a discrete random variable

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Section 5-23

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5.3 Random Variables

5.3 Random Variables. Random Variable Discrete Random Variables Continuous Random Variables Normal Distributions as Probability Distributions. Random Variables.

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Discrete Random Variables

Discrete Random Variables. Presentation 6.1 Overview and Examples. Random Variables. A random variable X is simply a variable whose value is a numerical outcome from a random event. Examples Number of videos rented by a random customer at a video store. Height of a randomly selected person.

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Discrete Random Variables

Discrete Random Variables. To understand what we mean by a discrete random variable To understand that the total sample space adds up to 1 To understand the P(X=x) notation To use the P(X=x) notation to solve problems. Discrete Random Variables. Value is from an experiment in the real world.

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Financial Optimization and Risk Management

Financial Optimization and Risk Management. Professor Alexei A. Gaivoronski. Department of Industrial Economics and Technology Management. Norwegian University of Science and Technology. Alexei.Gaivoronski@iot.ntnu.no. Topics of the course.

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