Analyzing Waiting Lines Most people find waiting lines irritating – waiting is idle and nonproductive time. From a service system perspective‚ however‚ a line represents a demand for service. Think of a restaurant on a Friday night. As a customer it is an irritation to have to wait 40 plus minutes for a table‚ but from the restaurant’s perspective‚ if there is not a line‚ then that means there are empty tables. Idle services are not good. So management must balance waiting time with the
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18. Compute the following probabilities: i) P (AB)=? [4 points] P (AB)=P[A]+P[B]-P[AB]=0.35+0.40-0.18=0.57 ii) P(AB)=? P[A B] P[ A B] 0.18 0.45 P[ B] 0.40 1 of 6 [4 points] Name: Problem 2 A diagnostic test for a certain disease is said to be 90% accurate in that‚ if a person has the disease‚ the test will detect it with probability 0.9. Also‚ if a person does not have the disease‚ the test will report that he or she does not have it with probability 0.9. Only 1% of the population
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applications: Probability Sampling: Simple Random Sampling‚ Stratified Random Sampling‚ Multi-Stage Sampling * What is each and how is it done? * How do we decide which to use? * How do we analyze the results differently depending on the type of sampling? Non-probability Sampling: Why don’t we use non-probability sampling schemes? Two reasons: * We can’t use the mathematics of probability to analyze the results. * In general‚ we can’t count on a non-probability sampling scheme
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CHAPTER 5 Choosing the type of probability sampling What you will learn in this chapter: •• •• •• •• •• •• The types of probability sampling and how they differ from each other Steps in carrying out the major probability sample designs The strengths and weaknesses of the various types of probability sampling Differences between stratified sampling and quota sampling Differences between stratified sampling and cluster sampling Differences between multistage cluster sampling and multiphase sampling
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Chapter 1 Risk in Our Society 1) Traditionally‚ risk has been defined as A) any situation in which the probability of loss is one. B) any situation in which the probability of loss is zero. C) uncertainty concerning the occurrence of loss. D) the probability of a loss occurring. Answer: C Question Status: Previous Edition 2) Objective risk is defined as A) the probability of loss. B) the relative variation of actual loss from expected loss. C) uncertainty based on a person’s mental
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------------------------------------------------- Top of Form Bottom of Form Wallace State Community College Business Statistics I BUS 271‚ 3 Credit Hours Spring Semester 2013 Step One: Read the syllabus carefully. Step Two: E-mail your instructor‚ in Blackboard through the email/message area; this will be a tab on left side of your homepage in blackboard. Select create message. Browse for recipient‚ Terri McGriff-Waldrop‚ select and fill in the subject line with the topic title‚ Syllabus
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the lawsuit has dropped our expected value by 3.7M. Therefore if we we’re sure that our Environmental report would secure the lease we should be willing to spend up to $3.7M for this report. 3. Sensitivity analysis Changes in the estimated probabilities for our
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Fault Tree Handbook with Aerospace Applications Version 1.1 Fault Tree Handbook with Aerospace Applications Prepared for NASA Office of Safety and Mission Assurance NASA Headquarters Washington‚ DC 20546 August‚ 2002 Fault Tree Handbook with Aerospace Applications Version 1.1 Fault Tree Handbook with Aerospace Applications NASA Project Coordinators: Dr. Michael Stamatelatos‚ NASA Headquarters Office of Safety and Mission Assurance Mr. José Caraballo‚ NASA Langley Research Center
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Chapter 11: 4‚ 7‚ 8‚ 10‚ 11‚ 12‚ 14‚ 15‚ 18‚ 20‚ 21‚ 22‚ 23‚ 24‚ 26‚ 27 Chapter Eleven Credit Risk: Individual Loan Risk Chapter Outline Introduction Credit Quality Problems Types of Loans • Commercial and Industrial Loans • Real Estate Loans • Individual (Consumer) Loans • Other Loans Calculating the Return on a Loan • The Contractually Promised Return on a Loan • The Expected Return on a Loan Retail versus Wholesale Credit Decisions
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Cellular Network Planning and Optimization Part II: Fading Jyri Hämäläinen‚ Communications and Networking Department‚ TKK‚ 17.1.2007 Outline Modeling approaches Path loss models Shadow fading Fast fading 2 Modeling approaches 3 Fading seen by moving terminal Fast fading Power Modeling approach: +20 dB 1. Distance between TX and RX => path loss 2. Shadowing by large obstacles => shadow fading 3. Multi-path effects => fast fading - 20 dB Path loss Lognormal
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