DOCSLIB.ORG

III. Famous Discrete Distributions: the Binomial and Poisson Distributions

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
III. Famous Discrete Distributions: the Binomial and Poisson Distributions Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 III. Famous Discrete Distributions: The Binomial and Poisson Distributions Up to this point, we have concerned ourselves with the general properties of categorical and continuous distributions, illustrated with somewhat arbitrary examples. However, there are particular distributions that are well understood and have wide applicability. We will first cover two specific types of categorical variables: those that follow the binomial and Poisson distributions. Both of these distributions model the probability of observing a certain number of events over a period of time or within a physical space. They are used widely in all branches of science and engineering. Later, we will spend some time discussing the most famous continuous distribution: the normal distribution or “bell curve”. Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 The Bernoulli Distribution As a way of understanding the binomial distribution, it helps to consider the simplest categorical random variable: a binary variable, which can take only one of two values, such as “heads” or “tails”, “yes” or “no”, male or female, dead or alive, etc. We typically code a binary random variable X as 1 or 0. We refer arbitrarily to X = 1 as a “success”, and X = 0 as a “failure”. How you choose to define “success” and “failure” is entirely up to you. If X is binary with probability of success p, then we say X ~ Bernoulli(p). That is, the pmf for X is P(X = 1) = p and P(X = 0) = 1 – p. Note also that E(X) = (1)p + (0)(1 – p) = p, and Var(X) = (1)2p + (0)2(1 – p) – p2 = p – p2 = p(1 – p). Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 The Binomial Distribution Suppose that we have n independent Bernoulli trials, each with probability of success p. The binomial distribution determines the probability of observing a given number of “successes” out of the n trials. Example III.A We flip a fair coin 10 times, and observe the number of tosses that result in “heads”. The number of heads out of 10 flips follows the binomial distribution. Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 Example III.B We survey 500 randomly selected students at USU, and ask them whether they think that President Bush is doing a good job (“yes” or “no”). The number who respond “yes” is a binomially distributed random variable. The Binomial PMF If X follows the binomial distribution, with number of independent trials given by n and probability of “success” given by p, we say that X ~ Binomial(n, p). The pmf of X is given by ⎛n⎞ x n−x P(X = x) = ⎜ ⎟ p (1− p) , for x = 0,K,n. ⎝ x⎠ Note also that E(X) = np, and Var(X) = np(1– p). Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 Example III.C A study of lizards reveals that at midday the probability of finding a given specimen in the sun is 0.077. Suppose that we randomly sample 60 lizards in a particular area. What is the probability that all 60 are in the shade? What is the expected number of lizards out of this sample found in the sun? What is the probability that we find 10 or more in the sun? Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 The Poisson Distribution Like the binomial distribution, the Poisson distribution is used to model count data. The distinction between the two distributions, however, is that we use the binomial distribution to model the probability of some count out of a fixed, finite number of trials. The Poisson distribution does not depend on a fixed number of trials – the range of a Poisson random variable is 0, 1, 2,… The Poisson distribution is especially useful for modeling the occurrence of relatively rare events. Example III.D An engineer observes traffic flow through an intersection during the period of an hour. The number of vehicles that will pass through is a Poisson random variable. Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 Example III.E A physicist is interested in the per minute intensity of particle emissions from a radioactive substance. The number of emitted particles can be considered a Poisson random variable. The Poisson PMF There is a single parameter that determines the distribution of a Poisson random variable X: the “rate” parameter µ. We often refer to µ as the rate because it turns out that E(X) = µ. In other words, µ represents the average count per unit of time or space. Given µ, the Poisson pmf is µ xe−µ P(X = x) = , for x = 0, 1, 2,... x! Note also that Var(X) = µ. The mean and variance of a Poisson random variable are the same. Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 Example III.F According to data from Lloyd’s Register of Shipping, the damage rate for a given ship is 0.004 incidents for each month of service. What is the probability that a given ship is damaged once during the next month? What is the probability that a given ship is damaged no more than once during the next month? What is the probability that a given ship is damaged at least once during its next 5 years of service? Stat 3000 – Statistics for Scientists and Engineers Dr. Corcoran, Fall 2005 The Poisson Approximation to the Binomial Distribution It turns out the Poisson distribution can provide a good approximation of binomial probabilities. This is especially true for a binomial random variable with relatively large n and small p. Can you think of a heuristic explanation for this? Suppose that you have a random variable X ~ Binomial(n,p), and you wish to use a Poisson approximation for the pmf of X. What mean and variance would you use for this Poisson distribution? Use the Poisson approximation to compute the probabilities in Example II.C. Does the approximation work well? Why or why not?.
Load more

Recommended publications
  • Effect of Probability Distribution of the Response Variable in Optimal Experimental Design with Applications in Medicine †
    mathematics Article Effect of Probability Distribution of the Response Variable in Optimal Experimental Design with Applications in Medicine † Sergio Pozuelo-Campos *,‡ , Víctor Casero-Alonso ‡ and Mariano Amo-Salas ‡ Department of Mathematics, University of Castilla-La Mancha, 13071 Ciudad Real, Spain; [email protected] (V.C.-A.); [email protected] (M.A.-S.) * Correspondence: [email protected] † This paper is an extended version of a published conference paper as a part of the proceedings of the 35th International Workshop on Statistical Modeling (IWSM), Bilbao, Spain, 19–24 July 2020. ‡ These authors contributed equally to this work. Abstract: In optimal experimental design theory it is usually assumed that the response variable follows a normal distribution with constant variance. However, some works assume other probability distributions based on additional information or practitioner’s prior experience. The main goal of this paper is to study the effect, in terms of efficiency, when misspecification in the probability distribution of the response variable occurs. The elemental information matrix, which includes information on the probability distribution of the response variable, provides a generalized Fisher information matrix. This study is performed from a practical perspective, comparing a normal distribution with the Poisson or gamma distribution. First, analytical results are obtained, including results for the linear quadratic model, and these are applied to some real illustrative examples. The nonlinear 4-parameter Hill model is next considered to study the influence of misspecification in a Citation: Pozuelo-Campos, S.; dose-response model. This analysis shows the behavior of the efficiency of the designs obtained in Casero-Alonso, V.; Amo-Salas, M.
    [Show full text]
  • The Beta-Binomial Distribution Introduction Bayesian Derivation
    In Danish: 2005-09-19 / SLB Translated: 2008-05-28 / SLB Bayesian Statistics, Simulation and Software The beta-binomial distribution I have translated this document, written for another course in Danish, almost as is. I have kept the references to Lee, the textbook used for that course. Introduction In Lee: Bayesian Statistics, the beta-binomial distribution is very shortly mentioned as the predictive distribution for the binomial distribution, given the conjugate prior distribution, the beta distribution. (In Lee, see pp. 78, 214, 156.) Here we shall treat it slightly more in depth, partly because it emerges in the WinBUGS example in Lee x 9.7, and partly because it possibly can be useful for your project work. Bayesian Derivation We make n independent Bernoulli trials (0-1 trials) with probability parameter π. It is well known that the number of successes x has the binomial distribution. Considering the probability parameter π unknown (but of course the sample-size parameter n is known), we have xjπ ∼ bin(n; π); where in generic1 notation n p(xjπ) = πx(1 − π)1−x; x = 0; 1; : : : ; n: x We assume as prior distribution for π a beta distribution, i.e. π ∼ beta(α; β); 1By this we mean that p is not a fixed function, but denotes the density function (in the discrete case also called the probability function) for the random variable the value of which is argument for p. 1 with density function 1 p(π) = πα−1(1 − π)β−1; 0 < π < 1: B(α; β) I remind you that the beta function can be expressed by the gamma function: Γ(α)Γ(β) B(α; β) = : (1) Γ(α + β) In Lee, x 3.1 is shown that the posterior distribution is a beta distribution as well, πjx ∼ beta(α + x; β + n − x): (Because of this result we say that the beta distribution is conjugate distribution to the binomial distribution.) We shall now derive the predictive distribution, that is finding p(x).
    [Show full text]
  • 9. Binomial Distribution
    J. K. SHAH CLASSES Binomial Distribution 9. Binomial Distribution THEORETICAL DISTRIBUTION (Exists only in theory) 1. Theoretical Distribution is a distribution where the variables are distributed according to some definite mathematical laws. 2. In other words, Theoretical Distributions are mathematical models; where the frequencies / probabilities are calculated by mathematical computation. 3. Theoretical Distribution are also called as Expected Variance Distribution or Frequency Distribution 4. THEORETICAL DISTRIBUTION DISCRETE CONTINUOUS BINOMIAL POISSON Distribution Distribution NORMAL OR Student’s ‘t’ Chi-square Snedecor’s GAUSSIAN Distribution Distribution F-Distribution Distribution A. Binomial Distribution (Bernoulli Distribution) 1. This distribution is a discrete probability Distribution where the variable ‘x’ can assume only discrete values i.e. x = 0, 1, 2, 3,....... n 2. This distribution is derived from a special type of random experiment known as Bernoulli Experiment or Bernoulli Trials , which has the following characteristics (i) Each trial must be associated with two mutually exclusive & exhaustive outcomes – SUCCESS and FAILURE . Usually the probability of success is denoted by ‘p’ and that of the failure by ‘q’ where q = 1-p and therefore p + q = 1. (ii) The trials must be independent under identical conditions. (iii) The number of trial must be finite (countably finite). (iv) Probability of success and failure remains unchanged throughout the process. : 442 : J. K. SHAH CLASSES Binomial Distribution Note 1 : A ‘trial’ is an attempt to produce outcomes which is neither sure nor impossible in nature. Note 2 : The conditions mentioned may also be treated as the conditions for Binomial Distributions. 3. Characteristics or Properties of Binomial Distribution (i) It is a bi parametric distribution i.e.
    [Show full text]
  • Chapter 6 Continuous Random Variables and Probability
    EF 507 QUANTITATIVE METHODS FOR ECONOMICS AND FINANCE FALL 2019 Chapter 6 Continuous Random Variables and Probability Distributions Chap 6-1 Probability Distributions Probability Distributions Ch. 5 Discrete Continuous Ch. 6 Probability Probability Distributions Distributions Binomial Uniform Hypergeometric Normal Poisson Exponential Chap 6-2/62 Continuous Probability Distributions § A continuous random variable is a variable that can assume any value in an interval § thickness of an item § time required to complete a task § temperature of a solution § height in inches § These can potentially take on any value, depending only on the ability to measure accurately. Chap 6-3/62 Cumulative Distribution Function § The cumulative distribution function, F(x), for a continuous random variable X expresses the probability that X does not exceed the value of x F(x) = P(X £ x) § Let a and b be two possible values of X, with a < b. The probability that X lies between a and b is P(a < X < b) = F(b) -F(a) Chap 6-4/62 Probability Density Function The probability density function, f(x), of random variable X has the following properties: 1. f(x) > 0 for all values of x 2. The area under the probability density function f(x) over all values of the random variable X is equal to 1.0 3. The probability that X lies between two values is the area under the density function graph between the two values 4. The cumulative density function F(x0) is the area under the probability density function f(x) from the minimum x value up to x0 x0 f(x ) = f(x)dx 0 ò xm where
    [Show full text]
  • Lecture.7 Poisson Distributions - Properties, Normal Distributions- Properties
    Lecture.7 Poisson Distributions - properties, Normal Distributions- properties Theoretical Distributions Theoretical distributions are 1. Binomial distribution Discrete distribution 2. Poisson distribution 3. Normal distribution Continuous distribution Discrete Probability distribution Bernoulli distribution A random variable x takes two values 0 and 1, with probabilities q and p ie., p(x=1) = p and p(x=0)=q, q-1-p is called a Bernoulli variate and is said to be Bernoulli distribution where p and q are probability of success and failure. It was given by Swiss mathematician James Bernoulli (1654-1705) Example • Tossing a coin(head or tail) • Germination of seed(germinate or not) Binomial distribution Binomial distribution was discovered by James Bernoulli (1654-1705). Let a random experiment be performed repeatedly and the occurrence of an event in a trial be called as success and its non-occurrence is failure. Consider a set of n independent trails (n being finite), in which the probability p of success in any trail is constant for each trial. Then q=1-p is the probability of failure in any trail. 1 The probability of x success and consequently n-x failures in n independent trails. But x successes in n trails can occur in ncx ways. Probability for each of these ways is pxqn-x. P(sss…ff…fsf…f)=p(s)p(s)….p(f)p(f)…. = p,p…q,q… = (p,p…p)(q,q…q) (x times) (n-x times) Hence the probability of x success in n trials is given by x n-x ncx p q Definition A random variable x is said to follow binomial distribution if it assumes non- negative values and its probability mass function is given by P(X=x) =p(x) = x n-x ncx p q , x=0,1,2…n q=1-p 0, otherwise The two independent constants n and p in the distribution are known as the parameters of the distribution.
    [Show full text]
  • 3.2.3 Binomial Distribution
    3.2.3 Binomial Distribution The binomial distribution is based on the idea of a Bernoulli trial. A Bernoulli trail is an experiment with two, and only two, possible outcomes. A random variable X has a Bernoulli(p) distribution if 8 > <1 with probability p X = > :0 with probability 1 − p, where 0 ≤ p ≤ 1. The value X = 1 is often termed a “success” and X = 0 is termed a “failure”. The mean and variance of a Bernoulli(p) random variable are easily seen to be EX = (1)(p) + (0)(1 − p) = p and VarX = (1 − p)2p + (0 − p)2(1 − p) = p(1 − p). In a sequence of n identical, independent Bernoulli trials, each with success probability p, define the random variables X1,...,Xn by 8 > <1 with probability p X = i > :0 with probability 1 − p. The random variable Xn Y = Xi i=1 has the binomial distribution and it the number of sucesses among n independent trials. The probability mass function of Y is µ ¶ ¡ ¢ n ¡ ¢ P Y = y = py 1 − p n−y. y For this distribution, t n EX = np, Var(X) = np(1 − p),MX (t) = [pe + (1 − p)] . 1 Theorem 3.2.2 (Binomial theorem) For any real numbers x and y and integer n ≥ 0, µ ¶ Xn n (x + y)n = xiyn−i. i i=0 If we take x = p and y = 1 − p, we get µ ¶ Xn n 1 = (p + (1 − p))n = pi(1 − p)n−i. i i=0 Example 3.2.2 (Dice probabilities) Suppose we are interested in finding the probability of obtaining at least one 6 in four rolls of a fair die.
    [Show full text]
  • 1 One Parameter Exponential Families
    1 One parameter exponential families The world of exponential families bridges the gap between the Gaussian family and general dis- tributions. Many properties of Gaussians carry through to exponential families in a fairly precise sense. • In the Gaussian world, there exact small sample distributional results (i.e. t, F , χ2). • In the exponential family world, there are approximate distributional results (i.e. deviance tests). • In the general setting, we can only appeal to asymptotics. A one-parameter exponential family, F is a one-parameter family of distributions of the form Pη(dx) = exp (η · t(x) − Λ(η)) P0(dx) for some probability measure P0. The parameter η is called the natural or canonical parameter and the function Λ is called the cumulant generating function, and is simply the normalization needed to make dPη fη(x) = (x) = exp (η · t(x) − Λ(η)) dP0 a proper probability density. The random variable t(X) is the sufficient statistic of the exponential family. Note that P0 does not have to be a distribution on R, but these are of course the simplest examples. 1.0.1 A first example: Gaussian with linear sufficient statistic Consider the standard normal distribution Z e−z2=2 P0(A) = p dz A 2π and let t(x) = x. Then, the exponential family is eη·x−x2=2 Pη(dx) / p 2π and we see that Λ(η) = η2=2: eta= np.linspace(-2,2,101) CGF= eta**2/2. plt.plot(eta, CGF) A= plt.gca() A.set_xlabel(r'$\eta$', size=20) A.set_ylabel(r'$\Lambda(\eta)$', size=20) f= plt.gcf() 1 Thus, the exponential family in this setting is the collection F = fN(η; 1) : η 2 Rg : d 1.0.2 Normal with quadratic sufficient statistic on R d As a second example, take P0 = N(0;Id×d), i.e.
    [Show full text]
  • Solutions to the Exercises
    Solutions to the Exercises Chapter 1 Solution 1.1 (a) Your computer may be programmed to allocate borderline cases to the next group down, or the next group up; and it may or may not manage to follow this rule consistently, depending on its handling of the numbers involved. Following a rule which says 'move borderline cases to the next group up', these are the five classifications. (i) 1.0-1.2 1.2-1.4 1.4-1.6 1.6-1.8 1.8-2.0 2.0-2.2 2.2-2.4 6 6 4 8 4 3 4 2.4-2.6 2.6-2.8 2.8-3.0 3.0-3.2 3.2-3.4 3.4-3.6 3.6-3.8 6 3 2 2 0 1 1 (ii) 1.0-1.3 1.3-1.6 1.6-1.9 1.9-2.2 2.2-2.5 10 6 10 5 6 2.5-2.8 2.8-3.1 3.1-3.4 3.4-3.7 7 3 1 2 (iii) 0.8-1.1 1.1-1.4 1.4-1.7 1.7-2.0 2.0-2.3 2 10 6 10 7 2.3-2.6 2.6-2.9 2.9-3.2 3.2-3.5 3.5-3.8 6 4 3 1 1 (iv) 0.85-1.15 1.15-1.45 1.45-1.75 1.75-2.05 2.05-2.35 4 9 8 9 5 2.35-2.65 2.65-2.95 2.95-3.25 3.25-3.55 3.55-3.85 7 3 3 1 1 (V) 0.9-1.2 1.2-1.5 1.5-1.8 1.8-2.1 2.1-2.4 6 7 11 7 4 2.4-2.7 2.7-3.0 3.0-3.3 3.3-3.6 3.6-3.9 7 4 2 1 1 (b) Computer graphics: the diagrams are shown in Figures 1.9 to 1.11.
    [Show full text]
  • ONE SAMPLE TESTS the Following Data Represent the Change
    1 WORKED EXAMPLES 6 INTRODUCTION TO STATISTICAL METHODS EXAMPLE 1: ONE SAMPLE TESTS The following data represent the change (in ml) in the amount of Carbon monoxide transfer (an indicator of improved lung function) in smokers with chickenpox over a one week period: 33, 2, 24, 17, 4, 1, -6 Is there evidence of significant improvement in lung function (a) if the data are normally distributed with σ = 10, (b) if the data are normally distributed with σ unknown? Use a significance level of α = 0.05. SOLUTION: (a) Here we have a sample of size 7 with sample mean x = 10.71. We want to test H0 : μ = 0.0, H1 : μ = 0.0, 6 under the assumption that the data follow a Normal distribution with σ = 10.0 known. Then, we have, in the Z-test, 10.71 0.0 z = − = 2.83, 10.0/√7 which lies in the critical region, as the critical values for this test are 1.96, for significance ± level α = 0.05. Therefore we have evidence to reject H0. The p-value is given by p = 2Φ( 2.83) = 0.004 < α. − (b) The sample variance is s2 = 14.192. In the T-test, we have test statistic t given by x 0.0 10.71 0.0 t = − = − = 2.00. s/√n 14.19/√7 The upper critical value CR is obtained by solving FSt(n 1)(CR) = 0.975, − where FSt(n 1) is the cdf of a Student-t distribution with n 1 degrees of freedom; here n = 7, so − − we can use statistical tables or a computer to find that CR = 2.447, and note that, as Student-t distributions are symmetric the lower critical value is CR.
    [Show full text]
  • Chapter 5 Sections
    Chapter 5 Chapter 5 sections Discrete univariate distributions: 5.2 Bernoulli and Binomial distributions Just skim 5.3 Hypergeometric distributions 5.4 Poisson distributions Just skim 5.5 Negative Binomial distributions Continuous univariate distributions: 5.6 Normal distributions 5.7 Gamma distributions Just skim 5.8 Beta distributions Multivariate distributions Just skim 5.9 Multinomial distributions 5.10 Bivariate normal distributions 1 / 43 Chapter 5 5.1 Introduction Families of distributions How: Parameter and Parameter space pf /pdf and cdf - new notation: f (xj parameters ) Mean, variance and the m.g.f. (t) Features, connections to other distributions, approximation Reasoning behind a distribution Why: Natural justification for certain experiments A model for the uncertainty in an experiment All models are wrong, but some are useful – George Box 2 / 43 Chapter 5 5.2 Bernoulli and Binomial distributions Bernoulli distributions Def: Bernoulli distributions – Bernoulli(p) A r.v. X has the Bernoulli distribution with parameter p if P(X = 1) = p and P(X = 0) = 1 − p. The pf of X is px (1 − p)1−x for x = 0; 1 f (xjp) = 0 otherwise Parameter space: p 2 [0; 1] In an experiment with only two possible outcomes, “success” and “failure”, let X = number successes. Then X ∼ Bernoulli(p) where p is the probability of success. E(X) = p, Var(X) = p(1 − p) and (t) = E(etX ) = pet + (1 − p) 8 < 0 for x < 0 The cdf is F(xjp) = 1 − p for 0 ≤ x < 1 : 1 for x ≥ 1 3 / 43 Chapter 5 5.2 Bernoulli and Binomial distributions Binomial distributions Def: Binomial distributions – Binomial(n; p) A r.v.
    [Show full text]
  • A Note on Skew-Normal Distribution Approximation to the Negative Binomal Distribution
    WSEAS TRANSACTIONS on MATHEMATICS Jyh-Jiuan Lin, Ching-Hui Chang, Rosemary Jou A NOTE ON SKEW-NORMAL DISTRIBUTION APPROXIMATION TO THE NEGATIVE BINOMAL DISTRIBUTION 1 2* 3 JYH-JIUAN LIN , CHING-HUI CHANG AND ROSEMARY JOU 1Department of Statistics Tamkang University 151 Ying-Chuan Road, Tamsui, Taipei County 251, Taiwan [email protected] 2Department of Applied Statistics and Information Science Ming Chuan University 5 De-Ming Road, Gui-Shan District, Taoyuan 333, Taiwan [email protected] 3 Graduate Institute of Management Sciences, Tamkang University 151 Ying-Chuan Road, Tamsui, Taipei County 251, Taiwan [email protected] Abstract: This article revisits the problem of approximating the negative binomial distribution. Its goal is to show that the skew-normal distribution, another alternative methodology, provides a much better approximation to negative binomial probabilities than the usual normal approximation. Key-Words: Central Limit Theorem, cumulative distribution function (cdf), skew-normal distribution, skew parameter. * Corresponding author ISSN: 1109-2769 32 Issue 1, Volume 9, January 2010 WSEAS TRANSACTIONS on MATHEMATICS Jyh-Jiuan Lin, Ching-Hui Chang, Rosemary Jou 1. INTRODUCTION where φ and Φ denote the standard normal pdf and cdf respectively. The This article revisits the problem of the parameter space is: μ ∈( −∞ , ∞), σ > 0 and negative binomial distribution approximation. Though the most commonly used solution of λ ∈(−∞ , ∞). Besides, the distribution approximating the negative binomial SN(0, 1, λ) with pdf distribution is by normal distribution, Peizer f( x | 0, 1, λ) = 2φ (x )Φ (λ x ) is called the and Pratt (1968) has investigated this problem standard skew-normal distribution.
    [Show full text]
  • Poisson Versus Negative Binomial Regression
    Handling Count Data The Negative Binomial Distribution Other Applications and Analysis in R References Poisson versus Negative Binomial Regression Randall Reese Utah State University [email protected] February 29, 2016 Randall Reese Poisson and Neg. Binom Handling Count Data The Negative Binomial Distribution Other Applications and Analysis in R References Overview 1 Handling Count Data ADEM Overdispersion 2 The Negative Binomial Distribution Foundations of Negative Binomial Distribution Basic Properties of the Negative Binomial Distribution Fitting the Negative Binomial Model 3 Other Applications and Analysis in R 4 References Randall Reese Poisson and Neg. Binom Handling Count Data The Negative Binomial Distribution ADEM Other Applications and Analysis in R Overdispersion References Count Data Randall Reese Poisson and Neg. Binom Handling Count Data The Negative Binomial Distribution ADEM Other Applications and Analysis in R Overdispersion References Count Data Data whose values come from Z≥0, the non-negative integers. Classic example is deaths in the Prussian army per year by horse kick (Bortkiewicz) Example 2 of Notes 5. (Number of successful \attempts"). Randall Reese Poisson and Neg. Binom Handling Count Data The Negative Binomial Distribution ADEM Other Applications and Analysis in R Overdispersion References Poisson Distribution Support is the non-negative integers. (Count data). Described by a single parameter λ > 0. When Y ∼ Poisson(λ), then E(Y ) = Var(Y ) = λ Randall Reese Poisson and Neg. Binom Handling Count Data The Negative Binomial Distribution ADEM Other Applications and Analysis in R Overdispersion References Acute Disseminated Encephalomyelitis Acute Disseminated Encephalomyelitis (ADEM) is a neurological, immune disorder in which widespread inflammation of the brain and spinal cord damages tissue known as white matter.
    [Show full text]