The Order Up-To Model Slide ‹#› Medtronic’s InSync pacemaker supply chain and objectives Supply chain: One distribution center (DC) in Mounds View, MN. About 500 sales territories throughout the country. Consider Susan Magnotto’s territory in Madison, Wisconsin. Objective: Because the gross margins are high, develop a system to minimize inventory investment while maintaining a very high service target, e.g., a 99.9% in-stock probability or a 99.9% fill rate. Slide
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contain less than one liter of soft drink. The company has decided to investigate the problem. According to the company records, when there is no malfunctioning in the beverage dispensing unit, the bottles contain liters of beverage on average, with a standard deviation of liters. A sample of bottles has been taken to be measured from the beverage dispensing lot. The mean amount of beverage in these bottles was liters. Find the probability of observing a sample mean of liters or less in a sample of bottles
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Pareto distribution of IV kind. The cumulative distribution function of sample range from their distribution is derived. The percentiles of the distribution of range are worked out and are used to construct the control limits. The performance of the control chart is compared with that of gamma based control chart. Interval estimation for the scale parameter is also worked out. Received Nov 11, 2012 Revised Mar 17, 2013 Accepted Mar 28, 2013 Keyword: Order Statistics Distribution of Range
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| |(Points: 1) | | |Table 7-1. The following table lists the probability distribution of the number of cell phones owned by families in small city of 3000 | | |residents. | | |number of cell phones
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) −1 < x < 1 0 otherwise ∞ f (x) = (a) What is the value of c? We know that for f (x) to be a probability distribution −∞ f (x)dx = 1. We integrate f (x) with respect to x, set the result equal to 1 and solve for c. 1 1 = −1 c(1 − x2 )dx cx − c x3 3 1 −1 = = = = c = Thus, c = 3 4 c c − −c + c− 3 3 2c −2c − 3 3 4c 3 3 4 . (b) What is the cumulative distribution function of X? We want to find F (x). To do that, integrate f (x) from the lower bound of the domain on which f (x)
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Undergraduate Research Opportunity Programme in Science Value at Risk Dai Bo Supervisor: Dr. Arie Harel Department of Mathematics National University of Singapore Academic year (2000/2001) I Summary Value at Risk (VaR) is one of the most popular tools used to estimate exposure to market risks, and it measures the worst expected loss at a given confidence level. In this report, we explain the concept of VaR, and then describe in detail some methods of VaR computation. We
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if its thickness is below 997 mm or above 1003 mm. The production process, when under control, produces the products with thicknesses following a normal distribution with a mean = 1000 mm and a standard deviation of 1 mm. However, due to wearing and tearing, the process mean thickness may change and deviate from the target 1000 mm while the standard deviation stays the same. If the process mean thickness drifts to a position at which the process produces more than 5% defective products, it is
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8 | 8 | 8 | 10 | 10 | 8 | 10 | 8 | 6 | a. | Construct a frequency distribution and a bar chart.Solution : Data Given 1. Weight of can of different size (a) 6 ounces (b) 8 ounces (c) 10 ounces So we have 3 cans (2) Total shopper is 40 Thus we can calculate the individual frequency of three different can . we got No of can of 6 ounces : 14No of can of 8 ounces : 17No of can of 10 ounces 9Frequency distribution table 6 ounces | 14 | 8 ounces | 17 | 10 ounces | 9 | A Bar Graph (also
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appropriate choice Standard statistical models and statistical inference methods. First of all, the Standard Model described. These models, in order to apply interval estimation and hypothesis testing parameters Also described, including the next two sample cases, when the purpose of comparing two or more of the population For their means and variances. Secondly, non-parametric inference tests are also described in the case where the data Sample distribution is not compatible with standard parameter distribution
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Exercises 8/27/14 1. Binomial distribution. a. Throw a fair dice once. What is the sample space? What is the probability to get “six”? What is the probability to get “six” or “five”? Define variable X to be { 1,2,……6} Probability to get a 6 is P(six)=P(X=6) = 1/6 Probability to get a 5 or 6 is 1/3 The sample space is the set of all possible outcomes. Sample space = {(one)(two)…(six)} b. Throw a fair dice 10 times. What is the probability to get “six” twice? What is the probability
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