PPT-Chapter 6 Random Variables

Section 61 Discrete and Continuous Random Variables Discrete and Continuous Random Variables USE the probability distribution of a discrete random variable to CALCULATE the probability of an event. And 57375en 57375ere Were None meets the standard for Range of Reading and Level of Text Complexity for grade 8 Its structure pacing and universal appeal make it an appropriate reading choice for reluctant readers 57375e book also o57373ers students Nimantha . Thushan. Baranasuriya. Girisha. . Durrel. De Silva. Rahul . Singhal. Karthik. . Yadati. Ziling. . Zhou. Outline. Random Walks. Markov Chains. Applications. 2SAT. 3SAT. Card Shuffling. Pieter . Abbeel. UC Berkeley EECS. Many slides adapted from . Thrun. , . Burgard. and Fox, Probabilistic Robotics. TexPoint fonts used in EMF. . Read the TexPoint manual before you delete this box.: . Sources of randomness in a computer?. Methods for generating random numbers:. Time of day (Seconds since midnight). 10438901, 98714982747, 87819374327498,1237477,657418,. Gamma ray . counters. Rand Tables. First center (expected value). Now - spread. 4.2 (cont.) Standard Deviation of a Discrete Random Variable. Measures how “spread out” the random variable is. Summarizing data and probability. Data. Overview of Probability. Shannon Quinn. CSCI 6900. Probabilistic and Bayesian Analytics. Andrew W. Moore. School of Computer Science. Carnegie Mellon University. www.cs.cmu.edu/~awm. awm@cs.cmu.edu. 412-268-7599. 1. http://www.landers.co.uk/statistics-cartoons/. 5.1-5.2: Random Variables - Goals. Be able to define what a random variable is.. Be able to differentiate between discrete and continuous random variables.. Designing experiments. OUTLINE of topics. Avoid obvious problems with:. The question. Sampling. Variables. How to do sampling.. Principles of experimental design.. Data collection. Consider the following 3 research questions:. Applied Statistics and Probability for Engineers. Sixth Edition. Douglas C. Montgomery George C. . Runger. Chapter 5 Title and Outline. 2. 5. Joint Probability Distributions. 5-1 Two or More Random Variables. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. In this lesson, we will:. Review the behavior of local variables and parameters. Introduce static variables. See how they work and how they can be applied. Generating unique identifiers. Applying specific algorithms. class is part of the . java.util. package. It provides methods that generate pseudorandom numbers. A . Random. object performs complicated calculations based on a . seed value. to produce a stream of seemingly random values. PX1 P3 1/6 X5 PXLet X your earnings X 100-1 99 X -1 PX99 1/12 3 1/220 PX-1 1-1/220 219/220 EX 1001/220Let X be a random variable assuming the values x1 x2 x3 with corresponding probabi Objective. : . Use experimental and theoretical distributions to make judgments about . the . likelihood of various outcomes in uncertain . situations. CHS Statistics. Decide if the following random variable x is discrete(D) or continuous(C). .