Probability Concepts 1. Fundamental Concepts of Probability 2. Mutually Exclusive and Collectively Exhaustive 3. Statistically Independent and Dependent Events 4. Bayes’Theorem Learning Objectives • Understand the basic foundations of probability analysis • Learn the probability rules for conditional probability and joint probability • Use Bayes’ theorem to establish posterior probabilities Reference: Text Chapter 2 Introduction • Life is uncertain; we are note sure what the
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: SQQP 5023 COURSE NAME : DECISION ANALYSIS LECTURER : DR. SYARIZA ABDUL RAHMAN email: syariza@uum.edu.my tel: 04 – 9286975/ 016-4127923 1. COURSE SYNOPSIS Mathematical tools have been applied for thousands of years; however‚ the formal study and application of quantitative techniques to practical decision making is largely a product of the twentieth century. Decision analysis refers to a body of techniques that allows a decision-maker to evaluate uncertainty‚ risk
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FREELANCER MANUAL GET TO WORK‚ GROW YOUR CAREER‚ AND BE AN ODESK SUPERSTAR! Copyright © 2013‚ oDesk Corp. All rights reserved. 1 Freelancer Manual What’s Inside Part 1: Welcome to oDesk What does oDesk do? ....................3 Why should I work on oDesk? ....................3 How does oDesk make money? ....................3 What is online work? ....................4 How is online work different from traditional work? ...................
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Decision Analysis Course Outline‚ Quarter I‚ 2006 Class Materials Topic Hardcopy in Packet Other* Introduction 1 Freemark Abbey Winery Structuring Decisions Framework for Analyzing Risk 2 The North Star Concert North Star.xls Best Guess‚ Worst Case‚ Best Case; and Continuous Uncertainties 3 Engine Services‚ Inc. Quick Start Guide to Crystal Ball Analyzing Uncertainty‚ Probability Distributions‚ and Simulation Learning Module: Crystal Ball Litigate Demo Engine Services.xls Language
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Concepts of Decision Making MMPBL/500 Foundations of Problem Based Learning Abstract Key concepts of decision making are a vital part of keeping a company successful and happy employees. There are many areas to take into consideration when applying these decisions. To begin managers must apply their critical thinking skills. As a manager you must choose a decision making concept. Then analyze the concept that is going to be used. Also take a close look at the symptoms and problems
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PROBABILITY DISTRIBUTION In the world of statistics‚ we are introduced to the concept of probability. On page 146 of our text‚ it defines probability as "a value between zero and one‚ inclusive‚ describing the relative possibility (chance or likelihood) an event will occur" (Lind‚ 2012). When we think about how much this concept pops up within our daily lives‚ we might be shocked to find the results. Oftentimes‚ we do not think in these terms‚ but imagine what the probability of us getting behind
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Probability 1.) AE-2 List the enduring understandings for a content-area unit to be implemented over a three- to five- week time period. Explain how the enduring understandings serve to contextualize (add context or way of thinking to) the content-area standards. Unit: Data and Probability Time: 3 weeks max Enduring Understanding: “Student Will Be Able To: - Know what probability is (chance‚ fairness‚ a way to observe our random world‚ the different representations) - Know what the
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QMT200 CHAPTER 3: PROBABILITY DISTRIBUTION 3.1 RANDOM VARIABLES AND PROBABILITY DISTRIBUTION Random variables is a quantity resulting from an experiment that‚ by chance‚ can assume different values. Examples of random variables are the number of defective light bulbs produced during the week and the heights of the students is a class. Two types of random variables are discrete random variables and continuous random variable. 3.2 DISCRETE RANDOM VARIABLE A random variable is called
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Probability 2 Theory Probability theory is the branch of mathematics concerned with probability‚ the analysis of random phenomena. (Feller‚ 1966) One object of probability theory is random variables. An individual coin toss would be considered to be a random variable. I predict if the coin is tossed repeatedly many times the sequence of it landing on either heads or tails will be about even. Experiment The Experiment we conducted was for ten students to flip a coin one hundred times
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Introduction Objectives PROBABILITY 2.2 Some Elementary Theorems 2.3 General Addition Rule 2.4 Conditional Probability and Independence 2.4.1 Conditional Probability 2.4.2 Independent Events and MultiplicationRule 2.4.3 Theorem of Total Probability and Bayes Theorem 2.5 Summary 2.1 INTRODUCTION You have already learnt about probability axioms and ways to evaluate probability of events in some simple cases. In this unit‚ we discuss ways to evaluate the probability of combination of events
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