Probability – a numerical value that represents the chance, likelihood, possibility that an event will occur (always between 0 and 1) Event – Each possible outcome of a variable Source: 3748154 5 Copyright © 2013 Pearson Australia (a division of Pearson Australia Group Pty Ltd) – 9781442548473/Berenson/Basic Business Statistics/3e 9781442549272/Berenson/Business Statistics /2e Events Simple
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Question 1: Sample space: a list of all possible outcomes of the random experiment Event: a collection or set of one or more simple events in a sample space Probability of an event: the sum of the probabilities of the simple events Random experiment: an action or process that leads to one of several possible outcomes The first step in the process of assigning probabilities is to produce a list of the outcomes. The list of outcomes must be exhaustive, which means all possible outcomes must be
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that a random person has the condition, which is 1 just 20 .) This is an example of a conditional probability: we are interested in the probability that a person has the condition (event A) given that he/she tests positive (event B). Let’s write this as Pr[A|B]. How should we define Pr[A|B]? Well, since event B is guaranteed to happen, we should look not at the whole sample space Ω , but at the smaller sample space consisting only of the sample points in B. What should the conditional probabilities
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Person A is going to get a tattoo. He thought of the idea half an hour ago at a party with his friends and has no idea what he is going to get. He will most likely do the first thing that pops into his head. A shoe. Person B is also going to get a tattoo and has thought of getting one for over two years now. He knows exactly what he will get. He has finally perfected his design and decided today was the day. His tattoo is very meaningful and he will not regret it later. Both are going to get a
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A Decision of Uncertainty A Decision of Uncertainty Paper There are decisions that people make where the outcome is presumably known and, there are the decisions that people make where the results are unknown. The latter part of the aforementioned statement is also known as decisions of uncertainty. To make these choices with more confidence, we will explore concepts that will formulate these judgments. We also have to include appropriate probability concepts that will help limit
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Module code: BSS002-6 Module Name: Business Data Analysis Submission: 30 November, 2012 I my class I learn how to simulate data to solve a problem. I learn to use excel and some function in it these are =SUM() = RAND() =AVERAGE() =STDV() =RANDINV() =VLOOKUP() and few more . These are widely used function when I simulated data in excel. In task 1 I find out how to calculate or forecast how much card should we print. I use a random variable with =RAND( function and use =VLOOKUP()
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United Arab Emirates University College Of Engineering Electrical Engineering Department ECOM320 - Random Signals
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subjects to assess their abilities - indeed, a vast majority of people say they are above the average. * Hindsight bias Hindsight bias, also known as the knew-it-all-along effect or creeping determinism, is the inclination, after an event has occurred, to see the event as having been predictable, despite there having been little or no objective basis for predicting it, prior to its occurrence. It is a multifaceted phenomenon that can affect different stages of designs, processes, contexts, and situations
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theory. Probability also plays a key role in fields such as Physics, Biology, Economics and Medicine. There is a close relationship between Counting/Combinatorics and Probability. In many cases, the probability of an event is simply the fraction of possible outcomes that make up the event. So many of the rules we developed for finding the cardinality of finite sets carry over to Probability Theory. For example, we’ll apply an Inclusion-Exclusion principle for probabilities in some examples below. In principle
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heart attack. If the patient survives the surgery, he has a 50% chance that the heart damage will heal. Find the probability that the patient survives and the heart damage heals. Let BS be the event that the patient survives bypass surgery. Let H be the event that the heart damage will heal. Then P(BS) = 0.60, and also we have a conditional probability: given the patient survives the probability that the heart damage will heal is 0.5, that is P(H|BS) = 0.5
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