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Unformatted text preview: 1 CHAPTER 6: PROBABILITY Stats & Prob. for Bus. Mgmt (Stat1100) Jochem b Overall Goals b 1. Getting familiar with basic probability theory so that we can compute probabilities of events. s Chance of winning the lottery? Having 3 out of 5 correct? 2 Probability Theory s If IQ scores are normally distributed, whats the probability that someone will have an IQ above 130? s If trucks crossing a bridge follow a Poisson distribution, whats the prob. of 5 trucks crossing in the next 10 min? b 2. Understanding discrete and continuous probability distributions that are underlying inference estimation. Probability Theory b Road map: Chap 68 b Chapter 6: Probability s Random Experiments , Events, Sample Space s Joint, marginal & conditional probabilities 3 s Probability Operators & Rules s Trees, Bayess Law & Counting b Chapter 7: Discrete Probability Distributions s Random Variables; Bivariate, Binomial & Poisson distribution b Chapter 8: Continuous Probability Distributions s Uniform, Normal, Exponential distribution s Students t , ChiSquared, Fdistribution Thereafter, chapter 9 on sampling distributions makes the transition towards estimation and inference. Probability b Chapter 6: Overview b 1. Basic Terminology of Probability Theory b 2. Joint, Marginal and Conditional Probability . Probability Operators & Rules 4 b 3. Probability Operators & Rules b 4. Trees & Bayess Law b 5. Counting b 6. Exercises b 1.1 Random Experiment b A random experiment is any process that leads to one or several basic outcomes (or a list thereof).* b Examples: 5 Probability s 1. Flipping coins s 2. PA Lottery Treasury Hunt s 3. Writing an exam s 4. Playing football s 5. Batting baseballs * Basic outcomes need to be mutually exclusive. (If we consider the weather as a random experiment, then for example sunny and rainy could not be basic outcomes as both can occur simultaneously.) An experiment is called random if its result cannot be predicted with certainty. b 1.2 Sample Space b The sample space of a random experiment is the list of all possible basic outcomes : 6 Probability b Examples: s 1. Flipping coins { Heads, Tails } s 2. PA Lottery Treasure Hunt { (1,2,3,4,5), (26,27,28,2930) } s 3. Writing an exam { 0,1,2, ,125 points} s 4. Football scores { , (24:14), } s 5. Batting averages { , 0.25, , 0.39, } countable uncountable b 1.3 Probability b Chance b Critical component of statistical inference. sed extensively in decision aking under uncertainty. 7 Probability b Used extensively in decisionmaking under uncertainty. b 1.3 Probability b A probability is the chance of one (or more) specific outcome(s) of the sample space occurring....
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 Spring '08
 Chiappetta
 Probability

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