Basic Probability Notes Probability— the relative frequency or likelihood that a specific event will occur. If the event is A‚ then the probability that A will occur is denoted P(A). Example: Flip a coin. What is the probability of heads? This is denoted P(heads). Properties of Probability 1. The probability of an event E always lies in the range of 0 to 1; i.e.‚ 0 ≤ P( E ) ≤ 1. Impossible event—an event that absolutely cannot occur; probability is zero. Example: Suppose you roll a normal die
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Conditional Probability How to handle Dependent Events Life is full of random events! You need to get a "feel" for them to be a smart and successful person. Independent Events Events can be "Independent"‚ meaning each event is not affected by any other events. Example: Tossing a coin. Each toss of a coin is a perfect isolated thing. What it did in the past will not affect the current toss. The chance is simply 1-in-2‚ or 50%‚ just like ANY toss of the coin. So each toss is an Independent
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Bayes’ theorem describes the relationships that exist within an array of simple and conditional probabilities. For example: Suppose there is a certain disease randomly found in one-half of one percent (.005) of the general population. A certain clinical blood test is 99 percent (.99) effective in detecting the presence of this disease; that is‚ it will yield an accurate positive result in 99 percent of the cases where the disease is actually present. But it also yields false-positive results in 5
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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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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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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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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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1/08/13 Probability Primer Principles of Econometrics‚ 4th Edition Probability Primer Page 1 ! Announcement: ! Please make sure you know who your tutor is and remember their names. This will save confusion and embarrassment later. ! Kai Du (David) ! Ngoc Thien Anh Pham (Anh) ! Zara Bomi Shroff Principles of Econometrics‚ 4th Edition Probability Primer Page 2 Chapter Contents ¡ P.1 Random Variables ¡ P.2 Probability Distributions ¡ P.3 Joint
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Probability theory Probability: A numerical measure of the chance that an event will occur. Experiment: A process that generates well defined outcomes. Sample space: The set of all experimental outcomes. Sample point: An element of the sample space. A sample point represents an experimental outcome. Tree diagram: A graphical representation that helps in visualizing a multiple step experiment. Classical method: A method of assigning probabilities that is appropriate when all the experimental
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