Chapter 6 Continuous Probability Distributions Case Problem: Specialty Toys 1. Information provided by the forecaster At x = 30‚000‚ [pic] [pic] Normal distribution [pic] [pic] 2. @ 15‚000 [pic] P(stockout) = 1 - .1635 = .8365 @ 18‚000 [pic] P(stockout) = 1 - .3483 = .6517 @ 24‚000 [pic] P(stockout) = 1 - .7823 = .2177 @ 28‚000 [pic]
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Sean Worang Sustainability 04/19/12 Runa Supply Chain I. Farmers Runa has worked with about 3000 to 4500 Guayusa farmers in the Amazon in regards to the supply and distribution of Guayusa leafs. The result of surveys (done by Runatarpuna) shows that by incorporating above 3000 to 4500 farmers Runa can generate about 16000 pounds/ month and can potentially generate about $17.2 per year projections. II. Runatarpuna Runatarpuna is Runa LLC’s fully owned export subsidiary‚ to manage
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F-distribution: A continuous right-skewed statistical distribution also Known as Snedecor’s F distribution or the Fisher - Snedecor distribution ( After R.A. Fisher and George W. Snedecor)(2) which arises in the testing of whether two observed samples have the same variance. (1) Note that three of the most important distributions (namely the normal distribution‚ the t distribution‚ and the chi-square distribution) may be seen as special cases of the F distribution: (3) Example: We want to
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LocatingMiddlemen The search for prospective middlemen should begin with study of the market and determination of criteria for evaluating middlemen servicing that market. The company ’s broad policy guidelines should be followed‚ but expect expediency to override policy at times. The checklist of criteria differs according to the type of middlemen being used and the nature of their relationship with the company. Basically‚ such lists are built around four subject areas: (1) productivity or
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Chapter 13: Chi-Square Applications SHORT ANSWER 1. When samples of size n are drawn from a normal population‚ the chi-square distribution is the sampling distribution of = ____________________‚ where s2 and are the sample and population variances‚ respectively. ANS: PTS: 1 OBJ: Section 13.2 2. Find the chi-square value for each of the right-tail areas below‚ given that the degrees of freedom are 7: A) 0.95 ____________________ B) 0.01 ____________________ C) 0.025 ____________________
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2.3. The Chi-Square Distribution One of the most important special cases of the gamma distribution is the chi-square distribution because the sum of the squares of independent normal random variables with mean zero and standard deviation one has a chi-square distribution. This section collects some basic properties of chi-square random variables‚ all of which are well known; see Hogg and Tanis [6]. A random variable X has a chi-square distribution with n degrees of freedom if it is a gamma
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The Normal and Lognormal Distributions John Norstad j-norstad@northwestern.edu http://www.norstad.org February 2‚ 1999 Updated: November 3‚ 2011 Abstract The basic properties of the normal and lognormal distributions‚ with full proofs. We assume familiarity with elementary probability theory and with college-level calculus. 1 1 DEFINITIONS AND SUMMARY OF THE PROPOSITIONS 1 Definitions and Summary of the Propositions ∞ √ Proposition 1: −∞ 2 2 1 e−(x−µ) /2σ
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Weston Materials‚ Inc.‚ a national manufacturer of unattached garages‚ reports that it takes two construction workers a mean of 32 hours and a standard deviation of 2 hours to erect the Red Barn model. Assume the assembly times follow the normal distribution. a. Determine the z values for 29 and 34 hours. What percent of the garages take between 32 hours and 34 hours to erect? z(29) = (29-32)/2 = -3/2 z(34) = (34-32)/2 = 1 z(32) = 0 P(32 < x < 34) = P(0< z < 1) = 0.34 b. What percent of the
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X-bar Definition 1 x xi n i 1 Probability and statistics - Karol Flisikowski n Sampling Distribution of x-bar How does x-bar behave? To study the behavior‚ imagine taking many random samples of size n‚ and computing an x-bar for each of the samples. Then we plot this set of x-bars with a histogram. Probability and statistics - Karol Flisikowski Sampling Distribution of x-bar Probability and statistics - Karol Flisikowski Central Limit Theorem The key to the
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Probability Distribution Essay Example Suppose you flip a coin two times. This simple statistical experiment can have four possible outcomes: HH‚ HT‚ TH‚ and TT. Now‚ let the random variable X represent the number of Heads that result from this experiment. The random variable X can only take on the values 0‚ 1‚ or 2‚ so it is a discrete random variable Binomial Probability Function: it is a discrete distribution. The distribution is done when the results are not ranged along a wide range‚ but are
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