‘remaining steady’. Write the table for the classification. III) Calculate the probability of price i) going up ii) going down and iii) remaining steady. Is this conditional probability or a simple probability? IV) It is observed that the status of closing market price of any day depends on the status of the previous day. If yesterday’s status of the price is ‘up’ then using this sample what is the probability that a) Today the price goes up. b) Today the price goes down. c) Today the
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1.M/G/ Queue a. Show that Let A(t) : Number of arrivals between time (0, t] “ n should be equal to or great than k” since if n is less than k (n<k), Pk(t)=0 Let’s think some customer C, Let’s find P{C arrived at time x and in service at time t | x=(0,t)] } P{C arrives in (x, x+dx] | C arrives in (0, t] }P{C is in service | C arrives at x, and x = (0,t] } Since theorem of Poisson Process, The theorem is that Given that N(t) =n, the n arrival times S1, S2
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modeled by probability-ofdetection: 4 2 01.06.2012 Probabilistic Updating of Flaw Information Tang (1973) 5 Updating models and reliability computations with (indirect) information • Bayes‘ rule: ∝ 6 3 01.06.2012 How to compute the reliability of a geotechnical site conditional on deformation monitoring outcomes? -> Integrate Bayesian updating in structural reliability methods 7 Prior model in structural reliability • Failure domain: Ω 0 • Probability of failure:
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Combinations without repetition is used to explain how lotteries work. The numbers are drawn one at a time and if one has the lucky numbers, no matter in what order, they win. This can be understood by assuming the order does matter, such as permutation and consequently alter it so the order no longer matters. An example of this is when we go to back to trying to find which 3 options of soccer players were chosen and not the order. We already know when 3 players are chosen out of 10 options of
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will be applying appropriate probability concepts to find resulting data to limit the uncertainty in this decision with rationale, identifying each discrete outcome from the statistical analysis, identifying tradeoffs between accuracy and precision, and will include the recommendation for the decision to be made. a. Include appropriate probability concepts and your application of them to find resulting data to limit the uncertainty in this decision. The probability concept that will be used to
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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() function to find out demand from discreet variable called cumulative probabilities. This is discrete because we find out the range by frequency distribution. Then I use if function to calculate disposable cost with =IF (Demand>Production,(Demand-Production)*Disposable cost, 0). This function works with logical criteria. It
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In my first portion of this assignment I decided to use the method like the dice. Using the repair time and probability given to us in the book. I computed the cumulative Probability by adding cells A & B; lines 5- 8 which gave me the Cum. Prob. for each day it would take for repair of the copiers. Next, I took the Cumulative Probability and the Repair Time in Days, named it Repairs. This will assist me later in finding the random numbers for the number of days the possibility of the copier breaking
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the given table, there are three columns of probability, repair time and cumulative. I used the figures given in case problem for probability and repair time days but for the cumulative column we started at 0.00 cumulative, then added the 0.20 to that to get the cumulative for day 2. For each probability subsequent to the previous we added, so for the 0.45 probability we added the 0.20 to get 0.65. Then repeating the same for the rest of probabilities, after 0.65 the cumulative resulted in 0.90.
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Chapter 1 Discrete Probability Distributions 1.1 Simulation of Discrete Probabilities Probability In this chapter, we shall first consider chance experiments with a finite number of possible outcomes ω1 , ω2 , . . . , ωn . For example, we roll a die and the possible outcomes are 1, 2, 3, 4, 5, 6 corresponding to the side that turns up. We toss a coin with possible outcomes H (heads) and T (tails). It is frequently useful to be able to refer to an outcome of an experiment. For example, we might
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The Hoylake Rescue Squad receives an emergency call every 1, 2, 3, 4, 5, or 6 hours, according to the following probability distribution: Time Between emergency Probabilities Calls (hours) 1 ……………………………………..0.05 2 ……………………………………..0.10 3 ……………………………………..0.30 4 ……………………………………..0.30 5 ……………………………………..0.20 6 ……………………………………..0.05 1.00 The squad is on duty 24 hours per day, 7 days per week. a. Simulate the emergency calls for three days (note that this will
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