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5. the Student T Distribution
Virtual Laboratories > 4. Special Distributions > 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 5. The Student t Distribution In this section we will study a distribution that has special importance in statistics. In particular, this distribution will arise in the study of a standardized version of the sample mean when the underlying distribution is normal. The Probability Density Function Suppose that Z has the standard normal distribution, V has the chi-squared distribution with n degrees of freedom, and that Z and V are independent. Let Z T= √V/n In the following exercise, you will show that T has probability density function given by −(n +1) /2 Γ((n + 1) / 2) t2 f(t)= 1 + , t∈ℝ ( n ) √n π Γ(n / 2) 1. Show that T has the given probability density function by using the following steps. n a. Show first that the conditional distribution of T given V=v is normal with mean 0 a nd variance v . b. Use (a) to find the joint probability density function of (T,V). c. Integrate the joint probability density function in (b) with respect to v to find the probability density function of T. The distribution of T is known as the Student t distribution with n degree of freedom. The distribution is well defined for any n > 0, but in practice, only positive integer values of n are of interest. This distribution was first studied by William Gosset, who published under the pseudonym Student. In addition to supplying the proof, Exercise 1 provides a good way of thinking of the t distribution: the t distribution arises when the variance of a mean 0 normal distribution is randomized in a certain way. -
Chapter 8 Fundamental Sampling Distributions And
CHAPTER 8 FUNDAMENTAL SAMPLING DISTRIBUTIONS AND DATA DESCRIPTIONS 8.1 Random Sampling pling procedure, it is desirable to choose a random sample in the sense that the observations are made The basic idea of the statistical inference is that we independently and at random. are allowed to draw inferences or conclusions about a Random Sample population based on the statistics computed from the sample data so that we could infer something about Let X1;X2;:::;Xn be n independent random variables, the parameters and obtain more information about the each having the same probability distribution f (x). population. Thus we must make sure that the samples Define X1;X2;:::;Xn to be a random sample of size must be good representatives of the population and n from the population f (x) and write its joint proba- pay attention on the sampling bias and variability to bility distribution as ensure the validity of statistical inference. f (x1;x2;:::;xn) = f (x1) f (x2) f (xn): ··· 8.2 Some Important Statistics It is important to measure the center and the variabil- ity of the population. For the purpose of the inference, we study the following measures regarding to the cen- ter and the variability. 8.2.1 Location Measures of a Sample The most commonly used statistics for measuring the center of a set of data, arranged in order of mag- nitude, are the sample mean, sample median, and sample mode. Let X1;X2;:::;Xn represent n random variables. Sample Mean To calculate the average, or mean, add all values, then Bias divide by the number of individuals. -
Chapter 4: Central Tendency and Dispersion
Central Tendency and Dispersion 4 In this chapter, you can learn • how the values of the cases on a single variable can be summarized using measures of central tendency and measures of dispersion; • how the central tendency can be described using statistics such as the mode, median, and mean; • how the dispersion of scores on a variable can be described using statistics such as a percent distribution, minimum, maximum, range, and standard deviation along with a few others; and • how a variable’s level of measurement determines what measures of central tendency and dispersion to use. Schooling, Politics, and Life After Death Once again, we will use some questions about 1980 GSS young adults as opportunities to explain and demonstrate the statistics introduced in the chapter: • Among the 1980 GSS young adults, are there both believers and nonbelievers in a life after death? Which is the more common view? • On a seven-attribute political party allegiance variable anchored at one end by “strong Democrat” and at the other by “strong Republican,” what was the most Democratic attribute used by any of the 1980 GSS young adults? The most Republican attribute? If we put all 1980 95 96 PART II :: DESCRIPTIVE STATISTICS GSS young adults in order from the strongest Democrat to the strongest Republican, what is the political party affiliation of the person in the middle? • What was the average number of years of schooling completed at the time of the survey by 1980 GSS young adults? Were most of these twentysomethings pretty close to the average on -
Notes Mean, Median, Mode & Range
Notes Mean, Median, Mode & Range How Do You Use Mode, Median, Mean, and Range to Describe Data? There are many ways to describe the characteristics of a set of data. The mode, median, and mean are all called measures of central tendency. These measures of central tendency and range are described in the table below. The mode of a set of data Use the mode to show which describes which value occurs value in a set of data occurs most frequently. If two or more most often. For the set numbers occur the same number {1, 1, 2, 3, 5, 6, 10}, of times and occur more often the mode is 1 because it occurs Mode than all the other numbers in the most frequently. set, those numbers are all modes for the data set. If each number in the set occurs the same number of times, the set of data has no mode. The median of a set of data Use the median to show which describes what value is in the number in a set of data is in the middle if the set is ordered from middle when the numbers are greatest to least or from least to listed in order. greatest. If there are an even For the set {1, 1, 2, 3, 5, 6, 10}, number of values, the median is the median is 3 because it is in the average of the two middle the middle when the numbers are Median values. Half of the values are listed in order. greater than the median, and half of the values are less than the median. -
Impact of Bacteria Motility in the Encounter Rates with Bacteriophage in Mucus Kevin L
www.nature.com/scientificreports OPEN Impact of bacteria motility in the encounter rates with bacteriophage in mucus Kevin L. Joiner1,2*, Arlette Baljon3,7, Jeremy Barr 4, Forest Rohwer5,7 & Antoni Luque 1,6,7* Bacteriophages—or phages—are viruses that infect bacteria and are present in large concentrations in the mucosa that cover the internal organs of animals. Immunoglobulin (Ig) domains on the phage surface interact with mucin molecules, and this has been attributed to an increase in the encounter rates of phage with bacteria in mucus. However, the physical mechanism behind this phenomenon remains unclear. A continuous time random walk (CTRW) model simulating the difusion due to mucin-T4 phage interactions was developed and calibrated to empirical data. A Langevin stochastic method for Escherichia coli (E. coli) run-and-tumble motility was combined with the phage CTRW model to describe phage-bacteria encounter rates in mucus for diferent mucus concentrations. Contrary to previous theoretical analyses, the emergent subdifusion of T4 in mucus did not enhance the encounter rate of T4 against bacteria. Instead, for static E. coli, the difusive T4 mutant lacking Ig domains outperformed the subdifusive T4 wild type. E. coli’s motility dominated the encounter rates with both phage types in mucus. It is proposed, that the local fuid-fow generated by E. coli’s motility combined with T4 interacting with mucins may be the mechanism for increasing the encounter rates between the T4 phage and E. coli bacteria. Phages—short for bacteriophages—are viruses that infect bacteria and are the most abundant replicative biolog- ical entities on the planet1,2, helping to regulate ecosystems and participating in the shunt of nutrients and the control of bacteria populations3. -
Volatility Modeling Using the Student's T Distribution
Volatility Modeling Using the Student’s t Distribution Maria S. Heracleous Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics Aris Spanos, Chair Richard Ashley Raman Kumar Anya McGuirk Dennis Yang August 29, 2003 Blacksburg, Virginia Keywords: Student’s t Distribution, Multivariate GARCH, VAR, Exchange Rates Copyright 2003, Maria S. Heracleous Volatility Modeling Using the Student’s t Distribution Maria S. Heracleous (ABSTRACT) Over the last twenty years or so the Dynamic Volatility literature has produced a wealth of uni- variateandmultivariateGARCHtypemodels.Whiletheunivariatemodelshavebeenrelatively successful in empirical studies, they suffer from a number of weaknesses, such as unverifiable param- eter restrictions, existence of moment conditions and the retention of Normality. These problems are naturally more acute in the multivariate GARCH type models, which in addition have the problem of overparameterization. This dissertation uses the Student’s t distribution and follows the Probabilistic Reduction (PR) methodology to modify and extend the univariate and multivariate volatility models viewed as alternative to the GARCH models. Its most important advantage is that it gives rise to internally consistent statistical models that do not require ad hoc parameter restrictions unlike the GARCH formulations. Chapters 1 and 2 provide an overview of my dissertation and recent developments in the volatil- ity literature. In Chapter 3 we provide an empirical illustration of the PR approach for modeling univariate volatility. Estimation results suggest that the Student’s t AR model is a parsimonious and statistically adequate representation of exchange rate returns and Dow Jones returns data. -
Non-Gaussian Behavior of Reflected Fractional Brownian Motion
Non-Gaussian behavior of reflected fractional Brownian motion Alexander H O Wada1,2, Alex Warhover1 and Thomas Vojta1 1 Department of Physics, Missouri University of Science and Technology, Rolla, MO 65409, USA 2 Instituto de F´ısica, Universidade de S˜aoPaulo, Rua do Mat˜ao,1371, 05508-090 S˜aoPaulo, S˜aoPaulo, Brazil January 2019 Abstract. A possible mechanism leading to anomalous diffusion is the presence of long-range correlations in time between the displacements of the particles. Fractional Brownian motion, a non-Markovian self-similar Gaussian process with stationary increments, is a prototypical model for this situation. Here, we extend the previous results found for unbiased reflected fractional Brownian motion [Phys. Rev. E 97, 020102(R) (2018)] to the biased case by means of Monte Carlo simulations and scaling arguments. We demonstrate that the interplay between the reflecting wall and the correlations leads to highly non-Gaussian probability densities of the particle position x close to the reflecting wall. Specifically, the probability density P (x) develops a power-law singularity P xκ with κ < 0 if the correlations are positive (persistent) and κ > 0 ∼ if the correlations are negative (antipersistent). We also analyze the behavior of the large-x tail of the stationary probability density reached for bias towards the wall, the average displacements of the walker, and the first-passage time, i.e., the time it takes for the walker reach position x for the first time. Keywords: Anomalous diffusion, fractional Brownian motion Contents arXiv:1811.06130v2 [cond-mat.stat-mech] 29 Mar 2019 1 Introduction 2 2 Normal diffusion 3 3 Fractional Brownian motion 5 4 Unbiased reflected fractional Brownian motion 6 CONTENTS 2 5 Biased reflected fractional Brownian motion 7 6 Monte Carlo simulations 10 6.1 Method ................................... -
The Value at the Mode in Multivariate T Distributions: a Curiosity Or Not?
The value at the mode in multivariate t distributions: a curiosity or not? Christophe Ley and Anouk Neven Universit´eLibre de Bruxelles, ECARES and D´epartement de Math´ematique,Campus Plaine, boulevard du Triomphe, CP210, B-1050, Brussels, Belgium Universit´edu Luxembourg, UR en math´ematiques,Campus Kirchberg, 6, rue Richard Coudenhove-Kalergi, L-1359, Luxembourg, Grand-Duchy of Luxembourg e-mail: [email protected]; [email protected] Abstract: It is a well-known fact that multivariate Student t distributions converge to multivariate Gaussian distributions as the number of degrees of freedom ν tends to infinity, irrespective of the dimension k ≥ 1. In particular, the Student's value at the mode (that is, the normalizing ν+k Γ( 2 ) constant obtained by evaluating the density at the center) cν;k = k=2 ν converges towards (πν) Γ( 2 ) the Gaussian value at the mode c = 1 . In this note, we prove a curious fact: c tends k (2π)k=2 ν;k monotonically to ck for each k, but the monotonicity changes from increasing in dimension k = 1 to decreasing in dimensions k ≥ 3 whilst being constant in dimension k = 2. A brief discussion raises the question whether this a priori curious finding is a curiosity, in fine. AMS 2000 subject classifications: Primary 62E10; secondary 60E05. Keywords and phrases: Gamma function, Gaussian distribution, value at the mode, Student t distribution, tail weight. 1. Foreword. One of the first things we learn about Student t distributions is the fact that, when the degrees of freedom ν tend to infinity, we retrieve the Gaussian distribution, which has the lightest tails in the Student family of distributions. -
Arxiv:2011.02444V1 [Cond-Mat.Stat-Mech] 4 Nov 2020 Short in One Important Respect: It Does Not Consider a Jump Over Obstacles, We fix Their Radius As = 10
Scaling study of diffusion in dynamic crowded spaces H. Bendekgey,1, 2 G. Huber,1 and D. Yllanes1, 3, ∗ 1Chan Zuckerberg Biohub, San Francisco, California 94158, USA 2University of California, Irvine, California 92697, USA 3Instituto de Biocomputación y Física de Sistemas Complejos (BIFI), 50009 Zaragoza, Spain (Dated: November 5, 2020) We study Brownian motion in a space with a high density of moving obstacles in 1, 2 and 3 dimensions. Our tracers diffuse anomalously over many decades in time, before reaching a diffusive steady state with an effective diffusion constant Deff that depends on the obstacle density and diffusivity. The scaling of Deff, above and below a critical regime at the percolation point for void space, is characterized by two critical exponents: the conductivity µ, also found in models with frozen obstacles, and , which quantifies the effect of obstacle diffusivity. Introduction. Brownian motion in disordered media times. Most works, however, have concentrated on the has long been the focus of much theoretical and experi- transient subdiffusive regime rather than on the diffusive mental work [1, 2]. A particularly important application steady state. has more recently emerged, thanks to the ever increas- Here we study Brownian motion in such a dynamic, ing quality of microscopy techniques: that of transport crowded space and characterize how the long-time dif- inside the cell (see, e.g., [3–6] for some pioneering stud- fusive regime depends on obstacle density and mobility. ies or [7–10] for more recent overviews). Indeed, it is Using extensive numerical simulations, we compute an ef- now possible to track the movement of single particles fective diffusion constant Deff for a wide range of obstacle inside living cells, of sizes ranging from small proteins concentrations and diffusivities in one, two and three spa- to viruses, RNA molecules or ribosomes. -
Statistics, Measures of Central Tendency I
Statistics, Measures of Central TendencyI We are considering a random variable X with a probability distribution which has some parameters. We want to get an idea what these parameters are. We perfom an experiment n times and record the outcome. This means we have X1;:::; Xn i.i.d. random variables, with probability distribution same as X . We want to use the outcome to infer what the parameters are. Mean The outcomes are x1;:::; xn. The Sample Mean is x1+···+xn x := n . Also sometimes called the average. The expected value of X , EX , is also called the mean of X . Often denoted by µ. Sometimes called population mean. Median The number so that half the values are below, half above. If the sample is of even size, you take the average of the middle terms. Mode The number that occurs most frequently. There could be several modes, or no mode. Dan Barbasch Math 1105 Chapter 9 Week of September 25 1 / 24 Statistics, Measures of Central TendencyII Example You have a coin for which you know that P(H) = p and P(T ) = 1 − p: You would like to estimate p. You toss it n times. You count the number of heads. The sample mean should be an estimate of p: EX = p, and E(X1 + ··· + Xn) = np: So X + ··· + X E 1 n = p: n Dan Barbasch Math 1105 Chapter 9 Week of September 25 2 / 24 Descriptive StatisticsI Frequency Distribution Divide into a number of equal disjoint intervals. For each interval count the number of elements in the sample occuring. -
Random Walk Calculations for Bacterial Migration in Porous Media
800 Biophysical Journal Volume 68 March 1995 80Q-806 Random Walk Calculations for Bacterial Migration in Porous Media Kevin J. Duffy,* Peter T. Cummings,** and Roseanne M. Ford** ·Department of Chemical Engineering and *Biophysics Program, University of Virginia, Charlottesville, Virginia 22903-2442 USA ABSTRACT Bacterial migration is important in understanding many practical problems ranging from disease pathogenesis to the bioremediation of hazardous waste in the environment. Our laboratory has been successful in quantifying bacterial migration in fluid media through experiment and the use of population balance equations and cellular level simulations that incorporate parameters based on a fundamental description of the microscopic motion of bacteria. The present work is part of an effort to extend these results to bacterial migration in porous media. Random walk algorithms have been used successfully to date in nonbiological contexts to obtain the diffusion coefficient for disordered continuum problems. This approach has been used here to describe bacterial motility. We have generated model porous media using molecular dynamics simulations applied to a fluid with equal sized spheres. The porosity is varied by allowing different degrees of sphere overlap. A random walk algorithm is applied to simulate bacterial migration, and the Einstein relation is used to calculate the effective bacterial diffusion coefficient. The tortuosity as a function of particle size is calculated and compared with available experimental results of migration of Pseudomonas putida in sand columns. Tortuosity increases with decreasing obstacle diameter, which is in agreement with the experimental results. INTRODUCTION Modem production of synthetic organic compounds has re rotational direction of the flagellar motors of the bacteria. -
Exploring the Mode – the Most Likely Value? Jerzy Letkowski Western New England University
OC13077 Exploring the Mode – the Most Likely Value? Jerzy Letkowski Western New England University Abstract The mean, median and mode are typical summary measures, each representing some focal tendency in random data. Even so the focus of each of the measures is different; they usually are packaged together as measures of central tendency. Among these three measures, the mode is granted the least amount of attention and it is frequently misinterpreted in many introductory textbooks on statistics. Also its centrality is questionable in some situations. The purpose of the paper is to provide a formal definition of the mode and to show appropriate interpretation of this measure with respect to different random data types: qualitative and quantitative (discrete and continuous). Several cases are presented to exemplify this purpose. Detail data processing steps are shown in Google spreadsheets. Keywords: mode, statistics, summary measures, probability distribution, data types, spreadsheet. Exploring the Mode – the Most Likely Value? OC13077 WHAT IS THE MODE? In general, in the context of a population, the concept of the mode of a random variable, X, having domain DX, is quite simple. Formally speaking (Spiegel, 1975, p. 84, McClave, 2011, p. 58, Sharpie, 2010, p. 115), it is a value, x*, of the variable that uniquely maximizes the probability-distribution function, f(x) : x* : f (x* ) = max {f (x)} or None (i) x∈DX One could refer to such a definition of the mode as a probability-distribution driven definition. In a way, it is a universal definition. For example, the maximum point for the Normal distribution (density function) is µ.