(Prerequisite: MAT 104) COURSE DESCRIPTION This course examines the principles of probability and of descriptive and inferential statistics. Topics include probability concepts, measures of central tendency, normal distributions, and sampling techniques. The application of these principles to simple hypothesis testing methods and to confidence intervals is also covered. The application of these topics in solving problems encountered in personal and professional settings is also discussed. INSTRUCTIONAL
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الدرس 1 Ch:- 5…… Probability P=The number of Possible cassesTotal number of casses ============================== EX1:- BLUE=12 RED=3 GREEN=5 What is the Probability:- P(B) OR P(R) P(B) OR P(G) P(B) OR P(R) OR P(G) SOLVE:- P=PB+ PR P= 1220+ 320= 1520 P=PB+ P(G) P=1220+ 520= 1720 P=PB+ PR+ P(G) P= 1220+ 320+520 = 2020=1 بأختصار (( الاحتمالات )) بيعطيك بالسؤال الاحتمالات المطلوبه فقط ضع الاحتمال المطلوب في البسط ومجموع الأحتمالات في المقام
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Jet Copies Breakdown Cumulative Prob Time Between Breakdown Probability Cumulative Probability Repair Time (days) Probability (Uniform) Cumulative Probability Sales Vol. F(x) = .0275x2 x = 20*sqrt(r/11) 0.20 0 1 0.143 0 2000 0.45 0.20 2 0.143 0.143 3000 0.25 0.65 3 0.143 0.286 4000 0.10 0.90 4 0.143 0.429 5000 0.143 0.571 6000 0.143 0.714 7000 0.143 0.857 8000 Breakdowns "Random
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University of Illinois at Springfield PAD 503 Analytical Tools Spring 2016 Homework #5 Student Name:____________________ Worked with:_____________________________________________________________ 1. MBB Problem 24.2 (modified): Tex Anderson has just been appointed director of the Roads Department in Gak, Texas. He knows that the last director was “urged to explore other career opportunities” due to performance problems in the department. The former director never collected data on performance
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making. Topics include descriptive statistics, probability distributions, confidence intervals and hypothesis testing and the use of computer software for statistical applications. Learning Outcomes: The successful Business Analytics I student should be able to: 1) Organize and summarize data using appropriate descriptive statistics and graphical methods. 2) Understand the concept of probability and to be able to calculate probabilities required in order to perform statistical inferences
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likely, optimistic etc) ABFGJKM ABFGJLM ABHGJKM ABHGJLM 2b.)Use Excel to determine the probability that each of the four critical paths would be completed within 30 months +The path completed within 30 months is BFGJLM +The path(s) completed within 30-40 months are paths BFGJKM,BHGJKM,BHGJLM 3.) Given the following German autobahn repair project, find the probability of completion by 17 weeks and 24 weeks. ( not sure where to find this
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1 CATEGORICAL DATA ANALYSIS, 3rd edition Solutions to Selected Exercises Alan Agresti Version August 3, 2012, c Alan Agresti 2012 This file contains solutions and hints to solutions for some of the exercises in Categorical Data Analysis, third edition, by Alan Agresti (John Wiley, & Sons, 2012). The solutions given are partly those that are also available at the website www.stat.ufl.edu/~ aa/ cda2/cda.html for many of the odd-numbered exercises in the second edition of the book (some
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taken. The challenge that practitioners face in utilizing expected return is not being able to precisely know what the future holds. Therefore, methods to estimate the expected return are created. * Distinguished-level: Explain the role of probability distribution in determining expected return. * Question 2: * Proficient-level: "Describe how different allocations between the risk-free security and the market portfolio can achieve any level of market risk desired" (Cornett, Adair,
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Bar Graphs & Pie Charts: follow area principle, (pie has to add up to 100%) Histograms: rectangles class intervals based on frequency Boxplots: less informative than histogram, (can’t tell shape unless unimodal) Statistics: Descriptive- how we cope w/ numbers [graphical methods (histograms, boxplots) numerical methods (mean, median)] Variable Types: QUANTITATIVE (numerical): Discrete- numbers are a certain gap apart, can’t have decimals (# kids in a house) Continuous- numbers can be arbitrarily
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Graphical model From Wikipedia, the free encyclopedia A graphical model is a probabilistic model for which a graph denotes the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. An example of a graphical model. Each arrow indicates a dependency. In this example: D depends on A, D depends on B, D depends on C, C depends on B, and C depends on D. Contents [hide] 1 Types
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