Variables Range = Largest number minus smallest number SD = Average Distance from the Mean (Most frequently used) Variance: Fat & Skinny Distributions: Skewness – measure of the lack of symmetry‚ or the lopsidedness of a distribution. One “tail” of the distribution is longer than another. Kurtosis: has to do with how flat or peaked a distribution appears/ Platykurtic: flat Leptokurtic: peake Hypotheses Null Hypothesis: There is NO RELATIONSHIP between our IV and DV Non-Directional
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Optimistic Time ( to ) – the length of time required under optimum conditions 2. Pessimistic Time ( tp ) – the length of time required under the worst conditions 3. Most likely time ( tm* ) –the most probable amount of time required Beta Distribution • used to describe probabilistic time estimates. In special interest in network analysis is the average or expected time for each activity and the variance of each activity time. The expected time of analysis is a weighted average of the three
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validity: degree to which you can generalize the results of your study to some underlying population T-test One sample t-test – A: data should arise from a normal population Paired t-test – A: differences should arise from a normal population Two sample t-test – A: samples must be independent‚ arise from a normal distribution &
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1)Permutation----nPr = n! ---- (n-r)! 2)Combination----nCr = nPr = n! ----- ------- n r! r! (n-r)! 3)Summation-----∑ X i i =1 n 4)Product--------Л Xi i=1 5)Age specific fertility rate(Asfr)=No of
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RISK THEORY - LECTURE NOTES 1. INTRODUCTION The primary subject of Risk Theory is the development and study of mathematical and statistical models to describe and predict the behaviour of insurance portfolios‚ which are simply financial instruments composed of a (possibly quite large) number of individual policies. For the purposes of this course‚ we will define a policy as a random (or stochastic) process generating a deterministic income in the form of periodic premiums‚ and incurring financial
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Information from the American Institute of Insurance indicates the mean amount of life insurance per household in the United States is $110‚000. This distribution follows the normal distribution with a standard deviation of $40‚000. a. If we select a random sample of 50 households‚ what is the standard error of the mean? b. What is the expected shape of the distribution of the sample mean? c. What is the likelihood of selecting a sample with a mean of at least $112‚000? d. What is the likelihood of selecting
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1. Which of the following is a method of data collection that is used in surveys? a. telephone b. Mail c. the Internet d. all of the above 2. All of the following are examples of an observation study EXCEPT: a. a mystery shopper pretending to be a customer in a McDonald’s outlet b. a cable laid across the street that records the number of cars that pass a certain intersection c. determining how long employees spend taking breaks to smoke cigarettes d. a consumer responding to a questionnaire about
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STA2300 Data Analysis Faculty of Sciences Study Book Ashley Plank with contributions from The LTS Team‚ Christine McDonald‚ Paul Fahey‚ Peter Dunn‚ Shahjahan Khan‚ Taryn Swan & Rachel King ii c University of Southern Queensland‚ 2012.1 Published by University of Southern Queensland Toowoomba Qld 4350 Australia http://www.usq.edu.au Copyrighted materials reproduced herein are used under the provisions of the Copyright Act 1968 as amended‚ or as a result of application to the copyright
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Six Sigma Quality at Flyrock Tires Executive Summary The process of creating tires at Flyrock Tires involves 20 different steps to take the rubber from bales to final curing. Given this complexity and the high production volume (the factory produces about 10‚000 tires per hour)‚ it takes only a small margin of error in each of these steps to begin to compound and result in a high defective rate. For both public safety and their reputation‚ Flyrock strives to minimize the number of defects. The
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Michael Cullimore Math 1040 Skittles Project Part 2 Confidence Interval Estimates - Confidence Interval: A confidence interval is an indicator of a measurement’s precision. It is also an indicator of how stable an estimate is‚ which is the measure of how close a measurement will be to the original estimate if an experiment is repeated. The calculations show that we are 99% confident that the true proportion of yellow candies falls between 0.20125 and 0.21275. This is
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