University of Perpetual Help System Dalta Molino Campus Molino III‚ Bacoor City Probability and Statistics LAGERA‚ Einar John A. Table of Contents Simple Correlation Analysis ................................................................................................. 1 Introduction .................................................................................................................................................................. 1 What is Correlation? ...........
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Richard C. Carrier‚ Ph.D. “Bayes’ Theorem for Beginners: Formal Logic and Its Relevance to Historical Method — Adjunct Materials and Tutorial” The Jesus Project Inaugural Conference “Sources of the Jesus Tradition: An Inquiry” 5-7 December 2008 (Amherst‚ NY) Table of Contents for Enclosed Document Handout Accompanying Oral Presentation of December 5...................................pp. 2-5 Adjunct Document Expanding on Oral Presentation.............................................pp. 6-26
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The linear probability model‚ ctd. When Y is binary‚ the linear regression model Yi = β0 + β1Xi + ui is called the linear probability model. • The predicted value is a probability: • E(Y|X=x) = Pr(Y=1|X=x) = prob. that Y = 1 given x • Yˆ = the predicted probability that Yi = 1‚ given X • β1 = change in probability that Y = 1 for a given ∆x: Pr(Y = 1 | X = x + ∆x ) − Pr(Y = 1 | X = x ) β1 = ∆x 5 Example: linear probability model‚ HMDA data Mortgage denial v. ratio of debt payments to income (P/I
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A JOURNAL OF LTEX CLASS FILES‚ VOL. 6‚ NO. 1‚ JANUARY 2007 1 Enhancing Performance of Random Testing Through Markov Chain Monte Carlo Methods Bo Zhou‚ Hiroyuki Okamura‚ Member‚ IEEE‚ and Tadashi Dohi‚ Member‚ IEEE Abstract—In this paper‚ we propose a probabilistic approach to finding failure-causing inputs based on Bayesian estimation. According to our probabilistic insights of software testing‚ the test case generation algorithms are developed by Markov chain Mote Carlo (MCMC) methods. Dissimilar
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Conditional sentences (type 1‚2) I/ Complete the sentences with the verbs in parentheses. 1. If she (read)…………….. in bad light‚ she’ll ruin her eyes. 2. He’ll be late for the bus if he (not start)……………………. at once. 3. Jim isn’t at hone right now. If he (be) …………………at home‚ I (visit) him. 4. If he (study) for the test‚ he (pass)…………………… it. 5. If you have enough time‚ please (paint)……………….. the chair before you leave. 6. I (accept)………………………. if they invite me to the party. 7. If you come here in Spring
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Concept and basics of probability sampling methods One of the most important issues in researches is selecting an appropriate sample. Among sampling methods‚ probability sample are of much importance since most statistical tests fit on to this type of sampling method. Representativeness and generalize-ability will be achieved well with probable samples from a population‚ although the matter of low feasibility of a probable sampling method or high cost‚ don’t allow us to use it and shift us to the
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Work Probability from Blake Internal P(Successful venture) = 0.6 P(Unsuccessful venture) = 0.4 MAI Probabilities P(Successful venture | Fav survey) = 0.7 P(Unsuccessful venture | Fav survey) = 0.3 P(Successful venture | Unfav survey) = 0.4 P(Unsuccessful venture | Unfav survey) = 0.6 Iverstine & Walker Probabilities P(Fav survey | Succes) = 0.9 P(Unfav survey | Success) = 0.1 P(Fav survey | Failure) = 0.2 P(Unfav survey | Failure) = 0.8 Calculations for Posterior Probabilities (Bayes Theorem)
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while statement will continue to run while a certain condition is still true. You can tell the number of times a loop statement will repeat itself‚ while you are unable to tell for the while statement. 6. Conditional expressions used in a While statement have the same components as conditional expression used in what other control construct? boolean 7. Although in some programs a While statement should be infinite‚ in most programs a While statement should eventually end. How can you make sure
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sensitivity analysis and analysis of other alternatives faced by Bio-Imaging and Medtech (a related company). The Need for a Systematic Theory of Probability • Discussion on the "Suds-Away" dishwashing detergent example‚ which introduces the need for probability theory to properly assign probabilities to branches at event nodes (conditional probabilities). • The solution to this example is postponed to Chapter 2. Further Issues and Concluding Remarks On Decision Analysis • Discussion on the following
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a prior probability? Prior probability is the probability that is initially calculated based on the information or data that is available in that moment. 2. Explain the purpose behind using Bayes Theorem The purpose of the Bayes’ theorem is to revise previously calculated probabilities based on new information. 19.36 Again consider the oil company case that was described in Example 19.1. Recall that the oil company wishes to decide whether to drill and that the prior probabilities of no oil
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