arrive in a five-minute period? The expected number is of 0.4*5 = 2 customer is a five-minute period. b) Assume that the Poisson probability distribution can be used to describe the arrival process. Use the arrival rate in part (a) and compute the probabilities that exactly 0, 1, 2, and 3 customers will arrive during a five-minute period. The probabilities of the described scenarios are as follows: P0=20e-20! P0=0.135335283 P1=21e-21! P1=0.270670566 P2=22e-22! P2=0.270670566 P3=23e-23
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Asymmetric Shocks, Long-term Bonds and Sovereign Default1 Junjun Zhu, Shiyu Xie School of Economics, Fudan University January 2011 Abstract: We present a sovereign default model with asymmetric shocks and long-term bonds, and solve the model using discrete state dynamic programming. As result, our model matches the Argentinean economy over period 1993Q1-2001Q4 quite well. We show that our model can match high default frequency, high debt/output ratio and other cyclical features, such as countercyclical
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n is fixed. 2. The n observations (or trials) are independent. 3. There are only 2 possible outcomes for each observation. They are labeled “Success” and “Failure” 4. The probability of success is the same for each trial. Let p = success probability and 1 – p = failure probability 5. The binomial random variable is . . . X = the number of successes out of n observations. Binomial distributions are identified specifically by two parameters: n and p
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Lecture 0 Statistics Review Yexiao Xu School of Management The University of Texas at Dallas 1 Outline Probability Random variables Distributions Characterizing a random variable (ex-ante) • Expected returns • variances, covariances • Correlation Statistics (ex-post) 2 Random Events Many events occurs with uncertainty • Working condition of hard drive in your laptop ─ Working properly versus hard drive crash • Driving home ─ Safely get to home versus
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504 IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 5, NO. 3, JULY 2008 Stochastic Modeling of an Automated Guided Vehicle System With One Vehicle and a Closed-Loop Path Aykut F. Kahraman, Abhijit Gosavi, Member, IEEE, and Karla J. Oty Abstract—The use of automated guided vehicles (AGVs) in material-handling processes of manufacturing facilities and warehouses is becoming increasingly common. A critical drawback of an AGV is its prohibitively high cost. Cost considerations
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applies to concepts of chance, probability, and information entropy. In these situations, randomness implies a measure of uncertainty, and notions of haphazardness are irrelevant. The fields of mathematics, probability, and statistics use formal definitions of randomness. In statistics, a random variable is an assignment of a numerical value to each possible outcome of an event space. This association facilitates the identification and the calculation of probabilities of the events. A random process is
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there is an approximate 80% probability that the present value of Uptown Plaza will be greater than the current offer if you wait until the lease renewals for the tenants in question are in place (see Appendix A). The below assessment summarizes the downside, but more importantly in this case, upside risk of waiting to sell rather than accepting the current offer. There are 3 key factors that drive the present commercial value of the shopping centre – the probability of renewal, the renewed rental
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they did not have a back-up copy machine. The first step in creating the simulation was to determine the time between repairs. The probability function for time between repairs is x = 6*square root (sqrt) of r, where r is the generated random number. First, a random number was generated. The next step to determine the time between repairs was to use the probability function of x=6*sqrt of r. The results of this calculation were placed in the second column of the excel worksheet. A third column was
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TABLE OF CONTENTS Question 1: ……………………………………………..…………………..………......1 - 3 1.1 1.2 1.2.1 1.2.2 1.2.3 1.3 Question 2: ……………………………………………………………..…………...….4 - 5 2.1 2.2 2.3 2.4 Question 3: ………………………………………………………………..………………..6 Question 4: …………………………………………………………..................…..…7 - 8 Question 5: ……………………………..……………………………………………..9 - 11 Bibliography: ………………………………………………………...…….……………..12 1 QUESTION 1 1.1 Class Interval 20 up to 50 50 up to 80 80 up to 110 110 up to 140 140 up to 170
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150000+40+14000+0=710000 2. Should the probability of defects change if we produce 18,000 units as opposed to 10,000 units? In my personal opinion, the probability of defects should change. In real life, it is reasonable to believe that more experienced worker and better manager can reduce defects probability to some degree. 3. Would your answer to question 1 change if Teloxy management believes that follow on contracts will be forthcoming? What would happen if the probability of defects changes to 15 percent
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