PPT-Some Continuous Probability

Author : sherrill-nordquist | Published Date : 2017-04-12

Distributions 61 Continuous Uniform Distribution One of the simplest continuous distributions in all of statistics is the continuous uniform distribution This

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Some Continuous Probability: Transcript


Distributions 61 Continuous Uniform Distribution One of the simplest continuous distributions in all of statistics is the continuous uniform distribution This distribution is characterized by a density function. QSCI 381 – Lecture 12. (Larson and Farber, Sect 4.1). Learning objectives. Become comfortable with variable definitions. Create and use probability distributions. Random Variables-I. A . Continuous distributions. Sample size 24. Guess the mean and standard deviation. Dot plot sample size 49. Draw the population distribution you expect. Sample size 93. Sample size 476. Sample size 948. A Brief Introduction. Random Variables. Random Variable (RV): A numeric outcome that results from an experiment. For each element of an experiment’s sample space, the random variable can take on exactly one value. 1. Normal Distribution. Log Normal Distribution. Gamma Distribution. Chi Square Distribution. F Distribution. t Distribution. Weibull Distribution. Extreme Value Distribution (Type I and II. ). Exponential. http://. rchsbowman.wordpress.com/2009/11/29. /. statistics-notes-%E2%80%93-properties-of-normal-distribution-2/. Chapter 23: Probability Density Functions. http://. divisbyzero.com/2009/12/02. /. an-applet-illustrating-a-continuous-nowhere-differentiable-function//. 3.1 . The Concept of Probability. 3.2 . Sample Spaces and Events. 3.3 . Some Elementary Probability Rules. 3.4 . Conditional Probability and Independence. 3.5 . Bayes’ Theorem. 3-. 2. Probability Concepts. 4. Introduction. (slide 1 of 3). A key . aspect of solving real business problems is dealing appropriately with uncertainty.. This involves recognizing explicitly that uncertainty exists and using quantitative methods to model uncertainty.. What we learned last class…. We are not good at recognizing/dealing with randomness. Our “random” coin flip results weren’t streaky enough.. If B/G results behave like independent coin flips, we know how many families to EXPECT with 0,1,2,3,4 girls.. . Integration. in Agile . environment. What is continuous integration ?. “Continuous Integration is a software development practice where members of a team integrate their work frequently, usually each person integrates at least daily - leading to multiple integrations per day. Each integration is verified by an automated build (including test) to detect integration errors as quickly as possible. Many teams find that this approach leads to significantly reduced integration problems and allows a team to develop cohesive software more rapidly.” Martin Fowler. March 4, 2015. First things first. The Exam. Due to Monday’s class cancellation. Today’s lecture on the Normal Distribution . will not. be covered on the Midterm. However, the previous lecture, on the Binomial Distribution, . 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.. CHAPTER 12 : Introducing Probability Basic Practice of Statistics 7th Edition Lecture PowerPoint Slides In Chapter 12, we cover … The idea of probability The search for randomness Probability models Probability Space of Two Die. σ-. Algebra (. ℱ. ). Sample Space (Ω). [...]. E5={(1,4),(2,3),(3,2),(4,1)}. [...]. Probability Measure Function (P). P. E5. 0.11. Probability Measure Function (P). . Section 6.1. Discrete & Continuous Random Variables. After this section, you should be able to…. APPLY the concept of discrete random variables to a variety of statistical settings. CALCULATE and INTERPRET the mean (expected value) of a discrete random variable.

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