'Possible values' diaporamas de présentation

Why Statistics?

Why Statistics?. Two Purposes Descriptive Finding ways to summarize the important characteristics of a dataset Inferential How (and when) to generalize from a sample dataset to the larger population. Descriptive Statistics. Firsthand Impression. Secondhand Impression. 3.88 3.38 1.88 .63

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Inverse Kinematics (part 2)

Inverse Kinematics (part 2). CSE169: Computer Animation Instructor: Steve Rotenberg UCSD, Winter 2004. Forward Kinematics. We will use the vector: to represent the array of M joint DOF values We will also use the vector:

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Lecture 3 Part 1.Important Database Concepts Part 2. Queries

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Statistical Methods in Clinical Research

Statistical Methods in Clinical Research. James B. Spies M.D., MPH Professor of Radiology Georgetown University School of Medicine Washington, DC. Overview. Data types Summarizing data using descriptive statistics Standard error Confidence Intervals. Overview. P values

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Chapter 2: Descriptive Statistics

Chapter 2: Descriptive Statistics 2.1 Organizing Qualitative Data 2.2 Organizing Quantitative Data 2.3 Additional Displays 2.4 Misrepresentations of Data September 10, 2008 Categorical Variables

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Decision Tree Classification

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Introduction to Pharmacoeconomics

Introduction to Pharmacoeconomics. Ellen Campbell, Ph.D. Division of Economic, Social & Administrative Pharmacy Florida A&M University Prepared for ISPOR Student Educational Teleconference Wednesday, September 19, 2007. What is Pharmacoeconomics? Types of Analyses Steps for PE Analysis

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Constraint Reasoning for Differential Models

Constraint Reasoning for Differential Models Jorge Cruz CENTRIA - Centre for Artificial Intelligence DI/FCT/UNL June 2009 PRESENTATION OUTLINE Constraint Reasoning Constraint Reasoning for Differential Models Examples: Drug Design / Epidemic Study Conclusions and Future Work

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Mathematical Induction

Mathematical Induction. CS/APMA 202 Rosen section 3.3 Aaron Bloomfield. What is induction?. A method of proof It does not generate answers: it only can prove them Three parts: Base case(s): show it is true for one element Inductive hypothesis: assume it is true for any given element

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Computer System Components Presentation Instructions

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4.4 Sampling Distribution Models and the Central Limit Theorem

4.4 Sampling Distribution Models and the Central Limit Theorem. Transition from Data Analysis and Probability to Statistics. Sampling Distributions. Population parameter : a numerical descriptive measure of a population.

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Digital Image Characteristics

Digital Image Characteristics. Thanks to the work of Dr. Perry Sprawls of Emory University and the Sprawls Educational Foundation, this material is available on-line. Image Types. There are two types of images, analog and digital , using in radiology.

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Statistics with Economics and Business Applications

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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

690 views • 35 slides

Random Variable

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

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Discrete Probability Distributions

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Analog-to-Digital Conversion

Analog-to-Digital Conversion. Terminology analog: continuously valued signal, such as temperature or speed, with infinite possible values in between digital: discretely valued signal, such as integers, encoded in binary

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Chapter 1 Introduction to Statistics

Chapter 1 Introduction to Statistics. 1-1 Overview 1-2 Types of Data 1-3 Critical Thinking 1-4 Design of Experiments. Section 1-1 Overview. Overview.

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Greedy Approximation Algorithms for finding Dense Components in a Graph

Greedy Approximation Algorithms for finding Dense Components in a Graph. Paper by Moses Charikar. Presentation by Paul Horn. Overview. Differing definitions of density The problem Undirected Case Linear Programming Network Flows Approximation Directed Case Linear Programming

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Digital to Analogue Conversion

Digital to Analogue Conversion. Analogue ? Digital Conversion . Analog and digital data were briefly mentioned at the start A digital signal is an approximation of an analog one Levels of signal are sampled and converted to a discrete bit pattern.

818 views • 18 slides