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Confidence Intervals for the Population Mean Alternatives to the Student-T Confidence Interval
Journal of Modern Applied Statistical Methods Volume 18 Issue 1 Article 15 4-6-2020 Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval Moustafa Omar Ahmed Abu-Shawiesh The Hashemite University, Zarqa, Jordan, [email protected] Aamir Saghir Mirpur University of Science and Technology, Mirpur, Pakistan, [email protected] Follow this and additional works at: https://digitalcommons.wayne.edu/jmasm Part of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and the Statistical Theory Commons Recommended Citation Abu-Shawiesh, M. O. A., & Saghir, A. (2019). Robust confidence intervals for the population mean alternatives to the Student-t confidence interval. Journal of Modern Applied Statistical Methods, 18(1), eP2721. doi: 10.22237/jmasm/1556669160 This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized editor of DigitalCommons@WayneState. Robust Confidence Intervals for the Population Mean Alternatives to the Student-t Confidence Interval Cover Page Footnote The authors are grateful to the Editor and anonymous three reviewers for their excellent and constructive comments/suggestions that greatly improved the presentation and quality of the article. This article was partially completed while the first author was on sabbatical leave (2014–2015) in Nizwa University, Sultanate of Oman. He is grateful to the Hashemite University for awarding him the sabbatical leave which gave him excellent research facilities. This regular article is available in Journal of Modern Applied Statistical Methods: https://digitalcommons.wayne.edu/ jmasm/vol18/iss1/15 Journal of Modern Applied Statistical Methods May 2019, Vol. -
Statistical Fallacy: a Menace to the Field of Science
International Journal of Scientific and Research Publications, Volume 9, Issue 6, June 2019 297 ISSN 2250-3153 Statistical Fallacy: A Menace to the Field of Science Kalu Emmanuel Ogbonnaya*, Benard Chibuike Okechi**, Benedict Chimezie Nwankwo*** * Department of Ind. Maths and Applied Statistics, Ebonyi State University, Abakaliki ** Department of Psychology, University of Nigeria, Nsukka. *** Department of Psychology and Sociological Studies, Ebonyi State University, Abakaliki DOI: 10.29322/IJSRP.9.06.2019.p9048 http://dx.doi.org/10.29322/IJSRP.9.06.2019.p9048 Abstract- Statistical fallacy has been a menace in the field of easier to understand but when used in a fallacious approach can sciences. This is mostly contributed by the misconception of trick the casual observer into believing something other than analysts and thereby led to the distrust in statistics. This research what the data shows. In some cases, statistical fallacy may be investigated the conception of students from selected accidental or unintentional. In others, it is purposeful and for the departments on statistical concepts as it relates statistical fallacy. benefits of the analyst. Students in Statistics, Economics, Psychology, and The fallacies committed intentionally refer to abuse of Banking/Finance department were randomly sampled with a statistics and the fallacy committed unintentionally refers to sample size of 36, 43, 41 and 38 respectively. A Statistical test misuse of statistics. A misuse occurs when the data or the results was conducted to obtain their conception score about statistical of analysis are unintentionally misinterpreted due to lack of concepts. A null hypothesis which states that there will be no comprehension. The fault cannot be ascribed to statistics; it lies significant difference between the students’ conception of with the user (Indrayan, 2007). -
Misuse of Statistics in Surgical Literature
Statistics Corner Misuse of statistics in surgical literature Matthew S. Thiese1, Brenden Ronna1, Riann B. Robbins2 1Rocky Mountain Center for Occupational & Environment Health, Department of Family and Preventive Medicine, 2Department of Surgery, School of Medicine, University of Utah, Salt Lake City, Utah, USA Correspondence to: Matthew S. Thiese, PhD, MSPH. Rocky Mountain Center for Occupational & Environment Health, Department of Family and Preventive Medicine, School of Medicine, University of Utah, 391 Chipeta Way, Suite C, Salt Lake City, UT 84108, USA. Email: [email protected]. Abstract: Statistical analyses are a key part of biomedical research. Traditionally surgical research has relied upon a few statistical methods for evaluation and interpretation of data to improve clinical practice. As research methods have increased in both rigor and complexity, statistical analyses and interpretation have fallen behind. Some evidence suggests that surgical research studies are being designed and analyzed improperly given the specific study question. The goal of this article is to discuss the complexities of surgical research analyses and interpretation, and provide some resources to aid in these processes. Keywords: Statistical analysis; bias; error; study design Submitted May 03, 2016. Accepted for publication May 19, 2016. doi: 10.21037/jtd.2016.06.46 View this article at: http://dx.doi.org/10.21037/jtd.2016.06.46 Introduction the most commonly used statistical tests of the time (6,7). Statistical methods have since become more complex Research in surgical literature is essential for furthering with a variety of tests and sub-analyses that can be used to knowledge, understanding new clinical questions, as well as interpret, understand and analyze data. -
STAT 22000 Lecture Slides Overview of Confidence Intervals
STAT 22000 Lecture Slides Overview of Confidence Intervals Yibi Huang Department of Statistics University of Chicago Outline This set of slides covers section 4.2 in the text • Overview of Confidence Intervals 1 Confidence intervals • A plausible range of values for the population parameter is called a confidence interval. • Using only a sample statistic to estimate a parameter is like fishing in a murky lake with a spear, and using a confidence interval is like fishing with a net. We can throw a spear where we saw a fish but we will probably miss. If we toss a net in that area, we have a good chance of catching the fish. • If we report a point estimate, we probably won’t hit the exact population parameter. If we report a range of plausible values we have a good shot at capturing the parameter. 2 Photos by Mark Fischer (http://www.flickr.com/photos/fischerfotos/7439791462) and Chris Penny (http://www.flickr.com/photos/clearlydived/7029109617) on Flickr. Recall that CLT says, for large n, X ∼ N(µ, pσ ): For a normal n curve, 95% of its area is within 1.96 SDs from the center. That means, for 95% of the time, X will be within 1:96 pσ from µ. n 95% σ σ µ − 1.96 µ µ + 1.96 n n Alternatively, we can also say, for 95% of the time, µ will be within 1:96 pσ from X: n Hence, we call the interval ! σ σ σ X ± 1:96 p = X − 1:96 p ; X + 1:96 p n n n a 95% confidence interval for µ. -
Tolerance-Package
Package ‘tolerance’ February 5, 2020 Type Package Title Statistical Tolerance Intervals and Regions Version 2.0.0 Date 2020-02-04 Depends R (>= 3.5.0) Imports MASS, rgl, stats4 Description Statistical tolerance limits provide the limits between which we can expect to find a speci- fied proportion of a sampled population with a given level of confidence. This package pro- vides functions for estimating tolerance limits (intervals) for various univariate distributions (bi- nomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hyper- geometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Pois- son-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multi- variate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regres- sion settings (linear regression, nonlinear regression, nonparametric regression, and multivari- ate regression), and analysis of variance tolerance intervals. Visualizations are also avail- able for most of these settings. License GPL (>= 2) NeedsCompilation no Author Derek S. Young [aut, cre] Maintainer Derek S. Young <[email protected]> Repository CRAN Date/Publication 2020-02-05 13:10:05 UTC R topics documented: tolerance-package . .3 acc.samp . .4 anovatol.int . .5 bayesnormtol.int . .7 bintol.int . .9 bonftol.int . 12 cautol.int . 13 1 2 R topics documented: diffnormtol.int . 14 DiffProp . 16 DiscretePareto . 18 distfree.est . 19 dpareto.ll . 20 dparetotol.int . 21 exp2tol.int . 23 exptol.int . 25 exttol.int . 26 F1.............................................. 28 fidbintol.int . 29 fidnegbintol.int . 31 fidpoistol.int . 33 gamtol.int . 35 hypertol.int . 37 K.factor . 38 K.factor.sim . 40 K.table . 42 laptol.int . 44 logistol.int . -
United Nations Fundamental Principles of Official Statistics
UNITED NATIONS United Nations Fundamental Principles of Official Statistics Implementation Guidelines United Nations Fundamental Principles of Official Statistics Implementation guidelines (Final draft, subject to editing) (January 2015) Table of contents Foreword 3 Introduction 4 PART I: Implementation guidelines for the Fundamental Principles 8 RELEVANCE, IMPARTIALITY AND EQUAL ACCESS 9 PROFESSIONAL STANDARDS, SCIENTIFIC PRINCIPLES, AND PROFESSIONAL ETHICS 22 ACCOUNTABILITY AND TRANSPARENCY 31 PREVENTION OF MISUSE 38 SOURCES OF OFFICIAL STATISTICS 43 CONFIDENTIALITY 51 LEGISLATION 62 NATIONAL COORDINATION 68 USE OF INTERNATIONAL STANDARDS 80 INTERNATIONAL COOPERATION 91 ANNEX 98 Part II: Implementation guidelines on how to ensure independence 99 HOW TO ENSURE INDEPENDENCE 100 UN Fundamental Principles of Official Statistics – Implementation guidelines, 2015 2 Foreword The Fundamental Principles of Official Statistics (FPOS) are a pillar of the Global Statistical System. By enshrining our profound conviction and commitment that offi- cial statistics have to adhere to well-defined professional and scientific standards, they define us as a professional community, reaching across political, economic and cultural borders. They have stood the test of time and remain as relevant today as they were when they were first adopted over twenty years ago. In an appropriate recognition of their significance for all societies, who aspire to shape their own fates in an informed manner, the Fundamental Principles of Official Statistics were adopted on 29 January 2014 at the highest political level as a General Assembly resolution (A/RES/68/261). This is, for us, a moment of great pride, but also of great responsibility and opportunity. In order for the Principles to be more than just a statement of noble intentions, we need to renew our efforts, individually and collectively, to make them the basis of our day-to-day statistical work. -
Comparing Interval Estimates What Is a Tolerance Interval? Other Interval
Evaluating Tolerance Interval Estimates Michelle Quinlan, University of Nebraska-Lincoln James Schwenke, Boehringer Ingelheim Pharmaceuticals, Inc. University of Nebraska Walt Stroup, University of Nebraska-Lincoln Department of Statistics PQRI Stability Shelf Life Working Group Comparing Interval Estimates 22 . Computed using be / (ratio of between to within batch variance) and The mean of each interval estimate across iterations is computed Statistical interval estimates are constructed to central t-distribution Comparisons are made among the interval estimates . Estimate parameters . Variance is a linear combination of independent mean squares, df calculated . Quantify characteristics of population using Satterthwaite approximation To correctly interpret estimates, it must be clearly defined what each interval β-content tolerance interval: is estimating Prμ,σˆˆ {Pr X [μˆ - kσ ˆ x < X < μ ˆ + kσ ˆ x |μ,σ ˆ ˆ x ] β} = γ . Confidence/prediction intervals are well understood x γ = confidence coefficient . Definition of a tolerance interval varies among literature sources . Interval that contains at least 100β% of population with given confidence What is a Tolerance Interval? level γ (Mee) Tolerance intervals are being used with more frequency, thus a consistent o Computed using factors from Normal and Chi-squared distributions definition needs to be established o β-content interval in models with only 1 source of variation are computed using noncentral t-distribution Definitions found in literature: . Wald and Wolfowitz use same definition to define tolerance intervals but instead use the formula: . A bound that covers at least (100-α)% of the measurements with (100- γ)% n confidence (Walpole and Myers) X 2 rs χ n,β o Focuses on where individual observations fall o Equivalent to a (100-γ)% CI on middle (100-α)% of Normal distribution SAS® Proc Capabilities Method 3 computes an approximate statistical . -
1 Introduction: Statistical Intervals
Avd. Matematisk statistik Sf2955 COMPUTER INTENSIVE METHODS TOLERANCE INTERVALS with A COMPUTER ASSIGNMENT 2011 Timo Koski 1 Introduction: Statistical Intervals Many practical problems are phrased in terms of individual measurements rather than parameters of distributions. We take two such examples. The first one will require, what is to be called a prediction interval, instead of a confidence interval. A consumer is considering buying a car. Then this person should be far more interested in knowing whether a full tank on a particu- lar automobile will suffice to carry her/him the 500 kms to her/his destination than in learning that there is a 95% confidence inter- val for the mean mileage of the model, which is possible to use to project the average or total gasoline consumption for the ma- nufactured fleet of such cars over their first 5000 kilometers of use. A different situation appears in the following. This will require, what is to be called a tolerance interval, instead of a confidence interval. A design engineer is charged with the problem of determining how large a tank the car model really needs to guarantee that 99% of the cars produced will have a cruising range of 500 kilometers. 1 What the engineer really needs is a tolerance interval for a fraction of 100 β = 99% mileages of such automobiles. × Prediction and tolerance intervals address problems of inference for (future) measurements. 2 Definitions of Other Intervals than Confi- dence Intervals We must distinguish between two different questions (I - II) concerning in- ference for future values. Let X1,...,Xn be I.I.D. -
The Numbers Game - the Use and Misuse of Statistics in Civil Rights Litigation
Volume 23 Issue 1 Article 2 1977 The Numbers Game - The Use and Misuse of Statistics in Civil Rights Litigation Marcy M. Hallock Follow this and additional works at: https://digitalcommons.law.villanova.edu/vlr Part of the Civil Procedure Commons, Civil Rights and Discrimination Commons, Evidence Commons, and the Labor and Employment Law Commons Recommended Citation Marcy M. Hallock, The Numbers Game - The Use and Misuse of Statistics in Civil Rights Litigation, 23 Vill. L. Rev. 5 (1977). Available at: https://digitalcommons.law.villanova.edu/vlr/vol23/iss1/2 This Article is brought to you for free and open access by Villanova University Charles Widger School of Law Digital Repository. It has been accepted for inclusion in Villanova Law Review by an authorized editor of Villanova University Charles Widger School of Law Digital Repository. Hallock: The Numbers Game - The Use and Misuse of Statistics in Civil Righ 1977-19781 THE NUMBERS GAME - THE USE AND MISUSE OF STATISTICS IN CIVIL RIGHTS LITIGATION MARCY M. HALLOCKt I. INTRODUCTION "In the problem of racial discrimination, statistics often tell much, and Courts listen."' "We believe it evident that if the statistics in the instant matter represent less than a shout, they certainly constitute '2 far more than a mere whisper." T HE PARTIES TO ACTIONS BROUGHT UNDER THE CIVIL RIGHTS LAWS3 have relied increasingly upon statistical 4 analyses to establish or rebut cases of unlawful discrimination. Although statistical evidence has been considered significant in actions brought to redress racial discrimination in jury selection,5 it has been used most frequently in cases of allegedly discriminatory t B.A., University of Pennsylvania, 1972; J.D., Georgetown University Law Center, 1975. -
Meta-Analysis of Standardised Mean Differences from Randomised Trials with Treatment-Related Clustering Associated with Care Providers
This is a repository copy of Meta-analysis of standardised mean differences from randomised trials with treatment-related clustering associated with care providers. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/109207/ Version: Accepted Version Article: Walwyn, R and Roberts, C (2017) Meta-analysis of standardised mean differences from randomised trials with treatment-related clustering associated with care providers. Statistics in Medicine, 36 (7). pp. 1043-1067. ISSN 0277-6715 https://doi.org/10.1002/sim.7186 © 2016 John Wiley & Sons, Ltd. This is the peer reviewed version of the following article: "Walwyn, R., and Roberts, C. (2017) Meta-analysis of standardised mean differences from randomised trials with treatment-related clustering associated with care providers. Statist. Med., 36 (7): 1043–1067. doi: 10.1002/sim.7186." which has been published in final form at https://doi.org/10.1002/sim.7186. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving. Reuse Unless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version - refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher’s website. -
Statistics for Dummies Cheat Sheet from Statistics for Dummies, 2Nd Edition by Deborah Rumsey
Statistics For Dummies Cheat Sheet From Statistics For Dummies, 2nd Edition by Deborah Rumsey Copyright © 2013 & Trademark by John Wiley & Sons, Inc. All rights reserved. Whether you’re studying for an exam or just want to make sense of data around you every day, knowing how and when to use data analysis techniques and formulas of statistics will help. Being able to make the connections between those statistical techniques and formulas is perhaps even more important. It builds confidence when attacking statistical problems and solidifies your strategies for completing statistical projects. Understanding Formulas for Common Statistics After data has been collected, the first step in analyzing it is to crunch out some descriptive statistics to get a feeling for the data. For example: Where is the center of the data located? How spread out is the data? How correlated are the data from two variables? The most common descriptive statistics are in the following table, along with their formulas and a short description of what each one measures. Statistically Figuring Sample Size When designing a study, the sample size is an important consideration because the larger the sample size, the more data you have, and the more precise your results will be (assuming high- quality data). If you know the level of precision you want (that is, your desired margin of error), you can calculate the sample size needed to achieve it. To find the sample size needed to estimate a population mean (µ), use the following formula: In this formula, MOE represents the desired margin of error (which you set ahead of time), and σ represents the population standard deviation. -
Misuse of Statistics
MISUSE OF STATISTICS Author: Rahul Dodhia Posted: May 25, 2007 Last Modified: October 15, 2007 This article is continuously updated. For the latest version, please go to www.RavenAnalytics.com/articles.php INTRODUCTION Percent Return on Investment 40 Did you know that 54% of all statistics are made up on the 30 spot? 20 Okay, you may not have fallen for that one, but there are 10 plenty of real-life examples that bait the mind. For 0 example, data from a 1988 census suggest that there is a high correlation between the number of churches and the year1 year2 number of violent crimes in US counties. The implied year3 Group B year4 Group A message from this correlation is that religion and crime are linked, and some would even use this to support the preposterous sounding hypothesis that religion causes FIGURE 1 crimes, or there is something in the nature of people that makes the two go together. That would be quite shocking, Here is the same data in more conventional but less pretty but alert statisticians would immediately point out that it format. Now it is clear that Fund A outperformed Fund B is a spurious correlation. Counties with a large number of in 3 out of 4 years, not the other way around. churches are likely to have large populations. And the larger the population, the larger the number of crimes.1 40 Percent Return on Investment Statistical literacy is not a skill that is widely accepted as Group A Group B necessary in education. Therefore a lot of misuse of 30 statistics is not intentional, just uninformed.