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    Monthly Earnings (X) = Monthly Revenue (P*Q) – Monthly Expense (F+V+L) =P*Q – (3995+L+11*Q) There are three variables in this equation‚ with the assumption; this model is realistic enough‚ if I was Sanjay‚ I will consider what the shape of the probability distribution of X is‚ and the measurement of this distribution to make risk analysis. a). Without considering the partnership opportunity‚ to solve the case‚ we run a Crystal Ball simulation with 1000 trials. The assumption variables are P‚ Q‚

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    Reliability analysis in ship’s critical machinery Objectives 1. Implement Markov process to identify availability of the engines of a vessel. 2. Using Monte Carlo simulation technique to model the Markov process using non-continuous transition rates. 3. Using the simulation model‚ calculate different reliability cost and worth‚ using numerous what-if scenarios. Objectives #1: Implement Markov process to identify availability of the engines of a vessel. Markov Process A Markov

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    ASSIGNMENT

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    100 but less than or equal to Rs 150. (iii) monthly income greater than Rs 250. 2. The demand of a product is approximately normally distributed with an average demand of 300 units per month. The probability of demand being less than 280 units is 0.025. What is the probability that demand is more than 315 units? 3. Approximately 30% of the time demand of a product is more than 250 units‚ and 20% of the time demand is less than 200 units. What is the average demand?

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    Performance of Safety Incidents Statistical Analysis of Safety Incident Rates Table of Contents Introduction 3 Part I. Graphical Descriptive Statistics 3 Part II. Binomial Probability Distribution 4 Part III. Inferential Statistics 5 Part IV. One Sample Hypothesis T-test 5 Part V. Two Sample Hypothesis T-test 6 Part VI. Paired (matched) Observation – Two Populations Hypothesis 6 Part VII. Linear Regression and Correlation Study 7 Part VIII. ANOVA – One-Way Test of Variance 7 Part

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    standardized Z value for P(Delivery Time>29) can be calculated as: (29-25.9) / 4.08 = 0.76 P(Z>29) = 0.5 – 0.2764 = 0.2236 = %22 The probability of delivery time being more than 29 minutes is higher than the targeted probability of 5 percent. Further Recommendations for Improvement In order to catch up the targeted probability of exceeding 29 minutes‚ there are two things that Mr. Scapelli can work on. First‚ he can take actions to decrease the expected time of the total delivery

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    Math 116

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    the z-score for which the area under the standard normal curve to its left is 0.04 9) Determine the two z-scores that divide the area under the standard normal curve into a middle 0.874 area and two outside 0.063 areas. Find the indicated probability or percentage for the normally distributed variable. 10) The variable X is normally distributed. The mean is μ = 15.2 and the standard deviation is σ = 0.9.

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    number of successes of A in n trials‚ a) Show that P {550 ≤ k ≤ 650} = 0.999‚ for n=1000. b) Find n such that P {0.59n ≤ k ≤ 0.61n} = 0.95 Q 4-26‚ A system has 100 components. The probability that a specific component will fail in the interval (a‚ b) equals e-a/T – e-b/T. Find the probability that in the interval (0‚ T/4)‚ no more than 100 components will

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    26.5 28.0 28.2 32.6 32.9 70.1 76.1 84.5 The position of the 60th percentile is Conditional probability is the probability that an event will occur given that another has already occurred. If A and B are two events‚ then the conditional probability A given B is written as P ( A | B ) and read as “the probability of A given that B has already occurred.” We are to calculate the probability of the intersection of the events F and G. P(F and G) = P(F) P(G |F) P(F) = 13/40 P(G |F)

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    BMA 140 WINTER 2011 PRACTICE (Midterm) 1. Monthly rent data in dollars for a sample of 10 one-bedroom apartments in a small town in Iowa are given below: 220 216 220 205 210 240 195 235 204 250 a. Compute the sample monthly average rent b. Compute the sample median c. What is the mode? d. Describe briefly what each statistic in parts a. to c. tells you about the data. 2. Suppose that a firm’s sales were $2‚500‚000 four years ago

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    | |Changed |262 |82 |8 |352 | |Total |272 |96 |32 |400 |   What is the probability that a consumer selected at random purchased fewer products than before? 0.6800

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