'Cumulative distribution function' presentation slideshows

Continuous Random Variables and Reliability Analysis

In the Name of the Most High . Continuous Random Variables and Reliability Analysis. Behzad Akbari Spring 2009 Tarbiat Modares University. These slides are based on the slides of Prof. K.S. Trivedi (Duke University). Definitions. Distribution function:

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Random Variables

Random Variables. an important concept in probability. A random variable , X, is a numerical quantity whose value is determined be a random experiment. Examples Two dice are rolled and X is the sum of the two upward faces.

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Chapter 2. Random Variables

Chapter 2. Random Variables. 2.1 Discrete Random Variables 2.2 Continuous Random Variables 2.3 The Expectation of a Random Variable 2.4 The Variance of a Random Variable 2.5 Jointly Distributed Random Variables 2.6 Combinations and Functions of Random Variables. -3. -2. -1. 1. 0. 2. 3.

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Continuous Random Variables

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The Monte Carlo Method: an Introduction

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Introduction to R

Introduction to R. Summer session: Lecture 3 Brian Healy. Outline. Discussion of R Importing and changing data Creating your own data Summary statistics / graphs Tests for normality. What is R?. Statistical computer language similar to S-plus Has many built-in statistical functions

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Chapter 3. Discrete Probability Distributions

Chapter 3. Discrete Probability Distributions. 3.1 The Binomial Distribution 3.2 The Geometric and Negative Binomial Distributions 3.3 The Hypergeometric Distribution 3.4 The Poisson Distribution 3.5 The Multinomial Distribution.

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Cellular COMMUNICATIONS

Cellular COMMUNICATIONS. LTE. Data Rate. Requirements And Targets to LTE. reduced delays, in terms of both connection establishment and transmission latency; increased user data rates; increased cell-edge bit-rate, for uniformity of service provision;

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Primer on Statistics for Interventional Cardiologists Giuseppe Sangiorgi, MD Pierfrancesco Agostoni, MD Giuseppe Biondi-

Primer on Statistics for Interventional Cardiologists Giuseppe Sangiorgi, MD Pierfrancesco Agostoni, MD Giuseppe Biondi-Zoccai, MD. What you will learn. Introduction Basics Descriptive statistics Probability distributions Inferential statistics Finding differences in mean between two groups

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Use of moment generating functions

Use of moment generating functions . Definition. Let X denote a random variable with probability density function f ( x ) if continuous (probability mass function p ( x ) if discrete) Then m X ( t ) = the moment generating function of X.

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Operational vulnerability indicators

Operational vulnerability indicators. Anand Patwardhan IIT-Bombay. Context and objectives matter. Vulnerability. A composite measure of the sensitivity of the system and its adaptive (coping) capacity Combine hazard, exposure and response layers

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Diversity techniques for flat fading channels

Diversity techniques for flat fading channels. BER vs. SNR in a flat fading channel Different kinds of diversity techniques Selection diversity performance Maximum Ratio Combining performance. BER vs. SNR in a flat fading channel. Proakis, 3rd Ed. 14-3.

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Exploratory Analysis of Survey Data

Exploratory Analysis of Survey Data. Lisa Cannon Luke Peterson. Presentation Outline. Density Estimation Nonparametric kernel density estimates Properties of kernel density estimators Other methods Graphical Displays NHANES data. Three features that distinguish survey data:.

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Review of Basic Probability and Statistics

Review of Basic Probability and Statistics. ISE525: Spring 10. Random Variables and Their Properties. Experiment : a process whose outcome is not known with certainty. Set of all possible outcomes of an experiment is the sample space. Outcomes are sample points in the sample space.

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Simulating Normal Random Variables

Simulating Normal Random Variables. Simulation can provide a great deal of information about the behavior of a random variable. Simulating Normal Random Variables. Two types of simulations (1) Generating fixed values - Uses Random Number Generation (2) Generating changeable values

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STAT3600

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

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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:

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Math 10 Chapter 6 Notes: The Normal Distribution

Math 10 Chapter 6 Notes: The Normal Distribution. Notation: X is a continuous random variable X ~ N( ? , ? ) Parameters: ? is the mean and ? is the standard deviation Graph is bell-shaped and symmetrical The mean, median, and mode are the same (in theory).

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