PPT-Normal, Binomial, Poisson,

and Exponential Distributions 5 Introduction Several specific distributions commonly occur in a variety of business situations N ormal distributiona continuous distribution characterized .  .  .  . Summary from last time. Discrete Random Variables. Binomial distribution . . – number . of successes from . independent Bernoulli (YES/NO) trials.  .  . Standard deviation . – measure spread of distribution. Probability Theorem. A binomial is a polynomial with two terms such as . x. + . a. . Often we need to raise a binomial to a power. In this section we'll explore a way to do just that without lengthy multiplication.. Zhang . Zhuozhuo. . Calum. Johnson . Waldemar. . Pietraszkiewicz. . The binomial model is a very useful and popular technique for pricing an option.. The binomial option pricing formula is based on assumption that the stock price follows a multiplicative binomial process over discrete intervals. . Distributions, link functions, diagnostics (linearity, homoscedasticity, leverage). Dichotomous key: picking a distribution for your data. Discrete or continuous?. Possible values: . 0/1 or 0,1,2,… etc.. Objectives:. By the end of this section, I will be. able to…. Explain what constitutes a binomial experiment. . Compute probabilities using the binomial probability formula.. Find probabilities using the binomial tables.. Shane G. Henderson. http://people.orie.cornell.edu/~shane. A Traditional Definition. Shane G. Henderson. 2. /15. What . A. re . T. hey For?. Shane G. Henderson. 3. /15. Times of customer arrivals (no scheduling and no groups). Distribution. If the probability that James is late home from work on any. day is 0.4, what is the probability that he is late home. t. hree times in a five-day working week?. L . L. . L. . L. ’ L’. The Poisson random variable was first introduced by the French mathematician Simeon-Denis Poisson (1781-1840). He discovered it as a limit to the binomial distribution as the number of trials . n. approaches infinity.. http://www.answers.com/topic/binomial-distribution. Chapter 18: Poisson Random Variables. http://. www.boost.org/doc/libs/1_35_0/libs/math/doc/sf_and_dist/html. /. math_toolkit. /. dist. /. dist_ref. Quantitative Analysis for Business Decisions. 2. 4.6 Standard Discrete Distributions continued. Further Examples on Use. Example 5. : . The probability of a . good. component in inspecting assembly line output is known to be 0.8 ; probability of a . Background. Historically. , the Binomial Distribution evaluation has been subject to approximation due to the laborious math involved. When the product np is less than five, a Poisson approximation is used. When np is equal or greater than five a normal approximation is used.. 1. Discrete Data. All data comes in Discrete form.. For Measurement data, in principle, it is on a continuous scale, but in reality it is truncated. . As long as Sigma(X)>measurement unit, there is no problem with using charts.. Ch. 3 . Myung Song, Ph.D.. © 2009 W.H. Freeman and Company. 1.. 2. Random Variables. Definition. For a given sample space S of some experiment, a . random variable (. rv. ) . is any . rule that associates a number with each outcome in S . . Binomial Distribution 1. Binomial Distribution 2. Additional Problems on Binomial Distribution. More Visual Representation. Binomial Distribution Tends to Normal for Large n. Binomial Distribution Tends to Normal for p Close to 0.5.