The Poisson probability distribution‚ named after the French mathematician Siméon-Denis. Poisson is another important probability distribution of a discrete random variable that has a large number of applications. Suppose a washing machine in a Laundromat breaks down an average of three times a month. We may want to find the probability of exactly two breakdowns during the next month. This is an example of a Poisson probability distribution problem. Each breakdown is called an occurrence in Poisson
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respondent is selected at random‚ what is the probability that he or she a. prefers to order at the drive-through? b. is a male and prefers to order at the drive-through? c. is a male or prefers to order at the drive-through? d. Explain the difference in the results in (b) and (c). e. Given that a respondent is a male‚ what is the probability that he prefers to order at the drive-through? f. Given that a respondent is a female‚ what is the probability that she prefers to order at the drive-through
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more than 1000‚ we cannot always use the normal approximation to binomial. Solution: If a sample is n>30‚ we can say that sample size is sufficiently large to assume normal approximation to binomial curve. Hence the statement is false. #2 A salesperson goes door-to-door in a residential area to demonstrate the use of a new Household appliance to potential customers. She has found from her years of experience that after demonstration‚ the probability of purchase (long run average) is
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Ans.1: Non-Probability Sampling: When the units of a sample are chosen so that each unit in the population does not have a calculable non-zero probability of being selected in the sample‚ this is called Non-Probability Sampling. Also‚ Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. In contrast with probability sampling‚ non-probability sample is not a product
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14. If x has the probability distribution f(x) = 12x for x = 1‚2‚3‚…‚ show that E(2X) does not exist. This is famous Petersburg paradox‚ according to which a player’s expectation is infinite (does not exist) if he is to receive 2x dollars when‚ in a series of flips of a balanced coin‚ the first head appears on the xth flip. 17. The manager of a bakery knows that the number of chocolate cakes he can sell on any given day is a random variable having the probability distribution f(x) = 16 for x =
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Problem 2.22: a. Formulate an LP model for this problem X1 = quantity of ornate‚ decorative wood frame doors produced X2 = quantity of windows produced Max Total Profit: 500X1+400X2 X1+0.5*X2≤40 0.5*X1+0.75*X2≤40 0.5 X1+X2≤60 X1‚ X2≥0 b. Sketch the feasible region Please refer to the graph below c. What is the optimal solution After calculations using solver in excel‚ we figure out that the max total profit available is $26‚000.00‚ when produce a mix of 20 units of doors and 40 units of windows
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Probability Distribution Memo To: Howard Gray‚ CEO; Jean Dubois‚ VP Mechanical Watch Division; Uma Gardner‚ VP Production; Amanda Hamilton‚ VP Marketing After identifying the business problem of falling sales and an increase in rejections by the Swiss Official Chronometer Control‚ conducting a study for research will prove to identify a solution. Researchers performed a study of a sample population of 500 people. The study reveals 60% of the watches purchased are certified and the average
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history 138w-38 | Why did Truman use the atomic bomb against Japan? What did his decision say about priorities of American foreign policy? | Truman decision and reasons behind deployment of the atomic bomb | | Min Yong Jung | 11/2/2010 | | Harry S. Truman‚ the 33rd President of the United States‚ deployed the atomic bomb on Japan to ensure the end of the Pacific War with minimal US casualties. Upon rejection of the Potsdam Declaration and calls for unconditional surrender by the Japanese
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Lysias was able to present a persuasive but well concealed argument drawn from probability to influence the minds of a jury. This utilization of an argument from probability
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A FICO score is the credit score mathematically developed by the company FICO. It shows potential lenders the risk that they are taking lending to you. The score is on a range starting at 300 and ending at 850; the higher your credit score‚ the probability that you will be lent to‚ increases. The score itself is determined from credit reports that disclose the individual’s history of financial activity. Their gender‚ race‚ religion‚ nor age play a role. From most influential to least‚ it is composed
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