and Continuous Probability Distributions Business Statistics With Canadian Applications Hummelbrunner Rak Gray Third Edition Week6 Pages 261-263 chapter 8 Pages 288-314, 320-325 chapter 9 Arranged by: Neiloufar Aminneia Probability distribution A probability distribution is a list of all events
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information communicated by Terri speculating that it will take 1 to 4 days to repair the original copier and the probability of each occurrence, based on a 20% probability for 1 day, 45% probability for 2 days, 25% probability for 3 days, and 10 % probability for 4 days, which equals 1, signifying the entire experiment or simulation. With this information, I was able to compute the cumulative probability, which also totals 1, (e.g., 0%, 20%, 65%, and 90%,
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a. f(x|θ) = θx-1(1-θ) x=1,2,……. f(θ)=1, 0< θ<1 Assuming that a series of observation x=1, 2 ... are sample from f(x|θ) and that each observation is independent and is identically distributed (iid), the joint probability density function for x1,….., xn is the product of the individual pdfs: fx1,….,xnθ=i=1nfxiθ =i=1nθxi-1(1-θ) =(1-θ)nθι=1nxi - n The maximum likelihood estimate of θ can be found by first finding
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that the requirements of binomial probability distribution are satisfied. a. A fixed number of trials – 24. b. Two possible outcomes – No trouble found, yes or no c. The probability of success is the same on each trail. d. The trails are independent. e. The random variable X is represented. 1. What is the probability that exactly 18 of the 24 customers made a return for “no trouble found”? binompdf (24, 0.68, 18) = 0.1397 2. What is the probability that no more than half of the customers
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month We assume that M obeys a Normal distribution with µ = 3,000 and σ = 1,000 = price of the prix fixe meal We assume that P obeys the following discrete probability distribution Scenario Very healthy market Healthy market Not so healthy market Unhealthy market Price of Prix Fixe Meal $20.00 $18.50 $16.50 $15.00 Probability 0.25 0.35 0.30 0.10 L = labor costs per month We assume that L obeys a uniform distribution with a minimum of $5,040 and maximum of $6,860 4 The Behavior
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Heading: Types of Probability Key words: deterministic techniques 2) Probabilistic techniques assume that no uncertainty exists in model parameters. Answer: FALSE Diff: 1 Page Ref: 489 Main Heading: Types of Probability Key words: probabilistic techniques 3) Objective probabilities that can be stated prior to the occurrence of an event are classical or a priori. Answer: TRUE Diff: 2 Page Ref: 489 Main Heading: Types of Probability Key words: objective probabilities, classical probabilities
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informs Vol. 36, No. 3, May–June 2006, pp. 248–258 issn 0092-2102 eissn 1526-551X 06 3603 0248 ® doi 10.1287/inte.1060.0211 © 2006 INFORMS Estimating Air-Cargo Overbooking Based on a Discrete Show-Up-Rate Distribution School of Industrial and Systems Engineering, Georgia Institute of Technology, 765 Ferst Drive, Atlanta, Georgia 30332 {andreeap@isye.gatech.edu, pinar@isye.gatech.edu, ellis.johnson@isye.gatech.edu} Sabre Airline Solutions, 1 East Kirkwood Boulevard, Southlake, Texas 76092
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= 0,005 = 2,576 95% confident interval ∝/2 = 0,025 = 1.96 90% confident interval ∝/2 = 0,05 = 1.645 We can be ...% confident that μ is not less than.... but no more than..... * Both and are random variables * Both and have probability distribution called the sampling distribution of * Scetch sampling distribution is mean and standard deviation and shape. [-μ] or │- p│.= sampling error In order to solve a question you should know 3 things; mean of ; E() which is μ, the
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is simulating the time between breakdowns. Without explicit information on frequency of breakdowns, some assumptions have to be made. In this case, the assumption is between 0 and 6 weeks with the probability of a breakdown increasing as time between breakdowns increases. I was given a probability distribution graph that showed time in weeks on the x axis from 0 to 6 with a y value of 0.33 where x = 6. The graph looked something like this: From that information, I calculated that area under
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as X. When there is only one predictor variable, the prediction method is called simple regression. In simple linear regression, the topic of this section, the predictions of Y when plotted as a function of X form a straight line. The study of probability helps us figure out the likelihood of something happening. For instance, when you roll a pair of dice, you might ask how likely you are to roll a seven. In
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