# 'Random variables' presentation slideshows

## Random Variable

Random Variable. A random variable X is a function that assign a real number, X ( ζ ), to each outcome ζ in the sample space of a random experiment. Domain of the random variable -- S Range of the random variable -- S x

By albert
(560 views)

## Outline: Independence. Odds ratios. Random variables. Distribution function, pmf, density. Expected value .

Outline: Independence. Odds ratios. Random variables. Distribution function, pmf, density. Expected value . Independence: P(B | A) = P(B) (and vice versa) [so, when independent, P(A&B) = P(A)P(B|A) = P(A)P(B).] Reasonable to assume the following are independent:

By jacob
(337 views)

## Chapter 6

Chapter 6 Introduction to Formal Statistical Inference Inferential Statistics Two areas of statistics: Descriptive Statistics Inferential Statistics Some Terminology Quantities of a population are called parameters and are typically denoted by Greek letters

By niveditha
(965 views)

## SLIDES PREPARED By Lloyd R. Jaisingh Ph.D. Morehead State University Morehead KY

STATISTICS for the Utterly Confused , 2 nd ed. SLIDES PREPARED By Lloyd R. Jaisingh Ph.D. Morehead State University Morehead KY Part 1 DESCRIPTIVE STATISTICS Chapter 1 Graphical Displays of Univariate Data Outline Do I Need to Read This Chapter?

By sandra_john
(343 views)

By HarrisCezar
(230 views)

## RANDOM VARIABLES, EXPECTATIONS, VARIANCES ETC.

RANDOM VARIABLES, EXPECTATIONS, VARIANCES ETC. Variable. Recall: Variable: A characteristic of population or sample that is of interest for us. Random variable: A function defined on the sample space S that associates a real number with each outcome in S. DISCRETE RANDOM VARIABLES.

(356 views)

## Appendix B

ECON 4550 Econometrics Memorial University of Newfoundland. Review of Probability Concepts. Appendix B. Adapted from Vera Tabakova’s notes . Appendix B: Review of Probability Concepts. B.1 Random Variables B.2 Probability Distributions

By KeelyKia
(252 views)

## SIMULATION MODELING AND ANALYSIS WITH ARENA T. Altiok and B. Melamed Chapter 7 Input Analysis

SIMULATION MODELING AND ANALYSIS WITH ARENA T. Altiok and B. Melamed Chapter 7 Input Analysis. Input Analysis Activities. Input Analysis activities consist of the following stages: Stage 1: data collection Stage 2: data analysis Stage 3: modeling time series data

By Jims
(671 views)

## Ch. 6 The Normal Distribution

Ch. 6 The Normal Distribution. A continuous random variable is a variable that can assume any value on a continuum (can assume an uncountable number of values) thickness of an item time required to complete a task temperature of a solution height, in inches

By jana
(223 views)

## 2806 Neural Computation Self-Organizing Maps Lecture 9

2806 Neural Computation Self-Organizing Maps Lecture 9. 2005 Ari Visa. Agenda. Some historical notes Some theory Self-Organizing Map Learning Vector Quantization C onclusions . Some Historical Notes . Local ordering (von der Malsbyrg, 1973)

By Samuel
(314 views)

## Option Pricing under ARMA Processes Theoretical and Empirical prospective

Option Pricing under ARMA Processes Theoretical and Empirical prospective. Chou-Wen Wang. Astract.

By Rita
(161 views)

## Chi-Square Test

Chi-Square Test. A fundamental problem is genetics is determining whether the experimentally determined data fits the results expected from theory (i.e. Mendel’s laws as expressed in the Punnett square).

By MikeCarlo
(619 views)

## Probability Review

Probability Review. (many slides from Octavia Camps). Intuitive Development. Intuitively, the probability of an event a could be defined as:. Where N(a) is the number that event a happens in n trials. More Formal:. W is the Sample Space: Contains all possible outcomes of an experiment

By liam
(188 views)

## Random Variables & Entropy: Extension and Examples

Random Variables & Entropy: Extension and Examples. Brooks Zurn EE 270 / STAT 270 FALL 2007. Overview. Density Functions and Random Variables Distribution Types Entropy. Density Functions. PDF vs. CDF PDF shows probability of each size bin

By betty_james
(379 views)

## Chapter 6 Continuous Random Variables

Chapter 6 Continuous Random Variables. Continuous Probability Distributions The Uniform Distribution The Normal Probability Distribution. Continuous Probability Distributions. A continuous random variable can assume any value in an interval on the real line or in a collection of intervals.

(445 views)

## Random-Packing Dynamics in Granular Flow

Random-Packing Dynamics in Granular Flow. Martin Z. Bazant Department of Mathematics, MIT. The Dry Fluids Laboratory @ MIT Students: Chris Rycroft, Ken Karmin, Jeremie Palacci, Jaehyuk Choi (PhD ‘05) Collaborators: Arshad Kudrolli (Clark University, Physics)

By lynley
(426 views)

## Lecture 7 Multiple Regression & Matrix Notation

Lecture 7 Multiple Regression & Matrix Notation. Quantitative Methods 2 Edmund Malesky, Ph.D., UCSD. Order of Presentation. 1. Review of Variance of Beta Hat 2. Review of T-Tests 3. Review of Quadratic Equations 4. Introduction to Multiple Regression 5. The Role of Control Variables

By alia
(522 views)

## Incorporating Language Modeling into the Inference Network Retrieval Framework

Incorporating Language Modeling into the Inference Network Retrieval Framework. Don Metzler. Motivation. Great deal of information lost when forming queries Example: “ stemming information retrieval ” InQuery informal ( tf.idf observation estimates)

By aldis
(133 views)

## Dealing with Spatial Autocorrelation

Dealing with Spatial Autocorrelation. Spatial Analysis Seminar Spring 2009. Spatial Autocorrelation Defined.

By lynde
(342 views)

## CHAPTER 4 EXPECTATION

CHAPTER 4 EXPECTATION. CHAPTER 4. Overview. ● The Expectation of a R. V. ● Properties of Expectation ● Variance ● Moments ● The Mean and the Median ● Covariance and Correlation ● Conditional Expectation ● The Sample Mean. Section 4.1 The Expectation of a Random Variable.

By damisi
(253 views)

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