PPT-Stat 35b: Introduction to Probability with Applications to

Outline for the day EXY EX EY HarmanNegreanu Running a hand multiple times expected value and variance Geometric random variables Negative binomial random variables Midterm is Feb 21 in class 50 min . Stat 150 Spring 2015 Syllabus http://www.stat.berkeley.edu/~sly/Stat 150Spring 2015Syllabus.pdf Instructor : Allan Sly GSI: Jonathan Hermon Course Webpage : http://www.stat.berkeley.edu/~sly/STAT1 2bal.stat bal.stat Description bal.statcomparesthetreatmentandcontrolsubjectsbymeans,standarddeviations,effectsize,andKSstatisticsUsage bal.stat(data,vars=NULL,treat.var,w.all,get.means=TRUE,get.ks=TR T.Jagannadha. . Swamy. Dept of . ECE,Griet. Random Variable. A random variable . x. takes on a defined set of values with different probabilities.. For example, if you roll a die, the outcome is random (not fixed) and there are 6 possible outcomes, each of which occur with probability one-sixth. . Jake Blanchard. Spring 2010. Uncertainty Analysis for Engineers. 1. Introduction. Interpretations of Probability. Classical – If an event can occur in N equally likely and different ways, and if n of these have an attribute A, then the probability of the occurrence of A, denoted Pr(A), is defined as n/N. Web Site Administration. Introduction to Web Applications. Instructor: Enoch E. Damson. Information System. A collection of components that work together to process . data. into accurate . information. Section Week 5 - Probability. Review. To best prepare for the exam, pay close attention to your homework and the solutions and take the practice midterms. -Also, if . you have questions about the grading of your assignment, feel free to contact your grader directly.. . Chapter 1 - Overview and Descriptive Statistics. . Chapter 2 - Probability. . Chapter 3 - Discrete Random Variables and Probability Distributions. Chapter 4 - Continuous Random Variables and Probability Distributions. A Year 2 Joint Hurricane . Testbed. Project Update. . Mark DeMaria. 1. , Robert DeMaria. 2. , Andrea Schumacher. 2. , . Daniel Brown. 3. , Michael Brennan. 3. , Richard Knabb. 4. , Pablo Santos. 5. By :. Wayne W. Daniel. -Elementary Biostatistics with Applications from Saudi Arabia. By : Nancy . Hasabelnaby. . 1434 / . 1435 H. 2. Chapter 1: Organizing and Displaying Data. 1.1: Introduction. Here we will consider some basic definitions and terminologies (. A Year 2 Joint Hurricane . Testbed. Project Update. . Mark DeMaria. 1. , Robert DeMaria. 2. , Andrea Schumacher. 2. , . Daniel Brown. 3. , Michael Brennan. 3. , Richard Knabb. 4. , Pablo Santos. 5. Introduction to Probability and Statistics Chapter 5 Discrete Distributions Discrete Random Variables Discrete random variables take on only a finite or countable many of values . Number of heads in 1000 trials of coin tossing What is probability?. Classical definition:. the . ratio. of “favorable” to equally probable . cases. .. “. favorable”. :. . the kind you’re interested . in. .. Probability of getting heads on flipping a fair coin: 1/2 (heads is 1 of 2 possibilities). A Year 2 Joint Hurricane . Testbed. Project Update. . Mark DeMaria. 1. , Robert DeMaria. 2. , Andrea Schumacher. 2. , . Daniel Brown. 3. , Michael Brennan. 3. , Richard Knabb. 4. , Pablo Santos. 5. Nisheeth. Random Variables. 2. Informally, a random variable (. r.v.. ) . denotes possible outcomes of an event. Can be discrete (i.e., finite many possible outcomes) or continuous. Some examples of discrete .