Experimental Design and Analysis
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Introduction to Biostatistics
Introduction to Biostatistics Jie Yang, Ph.D. Associate Professor Department of Family, Population and Preventive Medicine Director Biostatistical Consulting Core Director Biostatistics and Bioinformatics Shared Resource, Stony Brook Cancer Center In collaboration with Clinical Translational Science Center (CTSC) and the Biostatistics and Bioinformatics Shared Resource (BB-SR), Stony Brook Cancer Center (SBCC). OUTLINE What is Biostatistics What does a biostatistician do • Experiment design, clinical trial design • Descriptive and Inferential analysis • Result interpretation What you should bring while consulting with a biostatistician WHAT IS BIOSTATISTICS • The science of (bio)statistics encompasses the design of biological/clinical experiments the collection, summarization, and analysis of data from those experiments the interpretation of, and inference from, the results How to Lie with Statistics (1954) by Darrell Huff. http://www.youtube.com/watch?v=PbODigCZqL8 GOAL OF STATISTICS Sampling POPULATION Probability SAMPLE Theory Descriptive Descriptive Statistics Statistics Inference Population Sample Parameters: Inferential Statistics Statistics: 흁, 흈, 흅… 푿ഥ , 풔, 풑ෝ,… PROPERTIES OF A “GOOD” SAMPLE • Adequate sample size (statistical power) • Random selection (representative) Sampling Techniques: 1.Simple random sampling 2.Stratified sampling 3.Systematic sampling 4.Cluster sampling 5.Convenience sampling STUDY DESIGN EXPERIEMENT DESIGN Completely Randomized Design (CRD) - Randomly assign the experiment units to the treatments -
An Ethnographic Account of Disability and Globalization in Contemporary Russia
INACCESSIBLE ACCESSIBILITY: AN ETHNOGRAPHIC ACCOUNT OF DISABILITY AND GLOBALIZATION IN CONTEMPORARY RUSSIA Cassandra Hartblay A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Anthropology. Chapel Hill 2015 ! ! ! ! Approved by: Michele Rivkin-Fish Arturo Escobar Sue Estroff Jocelyn Chua Robert McRuer © 2015 Cassandra Hartblay ALL RIGHTS RESERVED ! ii! ABSTRACT Cassandra Hartblay: Inaccessible Accessibility: An Ethnographic Account Of Disability And Globalization In Contemporary Russia (Under the Direction of Michele Rivkin-Fish) Based on over twelve months of fieldwork in Russia, this dissertation explores what an ethnographic approach offers disability studies as a global, interdisciplinary, justice-oriented field. Focused on the personal, embodied narratives and experiences of five adults with mobility impairments in the regional capital city of Petrozavodsk, the dissertation draws on methods including participant observation, ethnographic interviews, performance ethnography, and analysis of public documents and popular media to trace the ways in which the category of disability is reproduced, stigmatized, and made meaningful in a contemporary postsoviet urban context. In tracing the ways in which concepts of disability and accessibility move transnational and transculturally as part of global expert cultures, I argue that Russian adults with disabilities expertly negotiate multiple modes of understanding disability, including historically and culturally rooted social stigma; psychosocial, therapeutic, or medicalized approaches; and democratic minority group citizenship. Considering the array of colloquial Russian terms that my interlocutors used to discuss issues of access and inaccess in informal settings, and their cultural antecedents, I suggest that the postsoviet infrastructural milieu is frequently posited as always opposed to development and European modernity. -
Design of Experiments Application, Concepts, Examples: State of the Art
Periodicals of Engineering and Natural Scinces ISSN 2303-4521 Vol 5, No 3, December 2017, pp. 421‒439 Available online at: http://pen.ius.edu.ba Design of Experiments Application, Concepts, Examples: State of the Art Benjamin Durakovic Industrial Engineering, International University of Sarajevo Article Info ABSTRACT Article history: Design of Experiments (DOE) is statistical tool deployed in various types of th system, process and product design, development and optimization. It is Received Aug 3 , 2018 multipurpose tool that can be used in various situations such as design for th Revised Oct 20 , 2018 comparisons, variable screening, transfer function identification, optimization th Accepted Dec 1 , 2018 and robust design. This paper explores historical aspects of DOE and provides state of the art of its application, guides researchers how to conceptualize, plan and conduct experiments, and how to analyze and interpret data including Keyword: examples. Also, this paper reveals that in past 20 years application of DOE have Design of Experiments, been grooving rapidly in manufacturing as well as non-manufacturing full factorial design; industries. It was most popular tool in scientific areas of medicine, engineering, fractional factorial design; biochemistry, physics, computer science and counts about 50% of its product design; applications compared to all other scientific areas. quality improvement; Corresponding Author: Benjamin Durakovic International University of Sarajevo Hrasnicka cesta 15 7100 Sarajevo, Bosnia Email: [email protected] 1. Introduction Design of Experiments (DOE) mathematical methodology used for planning and conducting experiments as well as analyzing and interpreting data obtained from the experiments. It is a branch of applied statistics that is used for conducting scientific studies of a system, process or product in which input variables (Xs) were manipulated to investigate its effects on measured response variable (Y). -
Experimentation Science: a Process Approach for the Complete Design of an Experiment
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 1996 - 8th Annual Conference Proceedings EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT D. D. Kratzer K. A. Ash Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Kratzer, D. D. and Ash, K. A. (1996). "EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT," Conference on Applied Statistics in Agriculture. https://doi.org/ 10.4148/2475-7772.1322 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 109 EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT. D. D. Kratzer Ph.D., Pharmacia and Upjohn Inc., Kalamazoo MI, and K. A. Ash D.V.M., Ph.D., Town and Country Animal Hospital, Charlotte MI ABSTRACT Experimentation Science is introduced as a process through which the necessary steps of experimental design are all sufficiently addressed. Experimentation Science is defined as a nearly linear process of objective formulation, selection of experimentation unit and decision variable(s), deciding treatment, design and error structure, defining the randomization, statistical analyses and decision procedures, outlining quality control procedures for data collection, and finally analysis, presentation and interpretation of results. -
Songs by Artist
Reil Entertainment Songs by Artist Karaoke by Artist Title Title &, Caitlin Will 12 Gauge Address In The Stars Dunkie Butt 10 Cc 12 Stones Donna We Are One Dreadlock Holiday 19 Somethin' Im Mandy Fly Me Mark Wills I'm Not In Love 1910 Fruitgum Co Rubber Bullets 1, 2, 3 Redlight Things We Do For Love Simon Says Wall Street Shuffle 1910 Fruitgum Co. 10 Years 1,2,3 Redlight Through The Iris Simon Says Wasteland 1975 10, 000 Maniacs Chocolate These Are The Days City 10,000 Maniacs Love Me Because Of The Night Sex... Because The Night Sex.... More Than This Sound These Are The Days The Sound Trouble Me UGH! 10,000 Maniacs Wvocal 1975, The Because The Night Chocolate 100 Proof Aged In Soul Sex Somebody's Been Sleeping The City 10Cc 1Barenaked Ladies Dreadlock Holiday Be My Yoko Ono I'm Not In Love Brian Wilson (2000 Version) We Do For Love Call And Answer 11) Enid OS Get In Line (Duet Version) 112 Get In Line (Solo Version) Come See Me It's All Been Done Cupid Jane Dance With Me Never Is Enough It's Over Now Old Apartment, The Only You One Week Peaches & Cream Shoe Box Peaches And Cream Straw Hat U Already Know What A Good Boy Song List Generator® Printed 11/21/2017 Page 1 of 486 Licensed to Greg Reil Reil Entertainment Songs by Artist Karaoke by Artist Title Title 1Barenaked Ladies 20 Fingers When I Fall Short Dick Man 1Beatles, The 2AM Club Come Together Not Your Boyfriend Day Tripper 2Pac Good Day Sunshine California Love (Original Version) Help! 3 Degrees I Saw Her Standing There When Will I See You Again Love Me Do Woman In Love Nowhere Man 3 Dog Night P.S. -
An Introduction to Psychometric Theory with Applications in R
What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD An introduction to Psychometric Theory with applications in R William Revelle Department of Psychology Northwestern University Evanston, Illinois USA February, 2013 1 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Overview 1 Overview Psychometrics and R What is Psychometrics What is R 2 Part I: an introduction to R What is R A brief example Basic steps and graphics 3 Day 1: Theory of Data, Issues in Scaling 4 Day 2: More than you ever wanted to know about correlation 5 Day 3: Dimension reduction through factor analysis, principal components analyze and cluster analysis 6 Day 4: Classical Test Theory and Item Response Theory 7 Day 5: Structural Equation Modeling and applied scale construction 2 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD Outline of Day 1/part 1 1 What is psychometrics? Conceptual overview Theory: the organization of Observed and Latent variables A latent variable approach to measurement Data and scaling Structural Equation Models 2 What is R? Where did it come from, why use it? Installing R on your computer and adding packages Installing and using packages Implementations of R Basic R capabilities: Calculation, Statistical tables, Graphics Data sets 3 Basic statistics and graphics 4 steps: read, explore, test, graph Basic descriptive and inferential statistics 4 TOD 3 / 71 What is psychometrics? What is R? Where did it come from, why use it? Basic statistics and graphics TOD What is psychometrics? In physical science a first essential step in the direction of learning any subject is to find principles of numerical reckoning and methods for practicably measuring some quality connected with it. -
Design of Experiments (DOE) Using JMP Charles E
PharmaSUG 2014 - Paper PO08 Design of Experiments (DOE) Using JMP Charles E. Shipp, Consider Consulting, Los Angeles, CA ABSTRACT JMP has provided some of the best design of experiment software for years. The JMP team continues the tradition of providing state-of-the-art DOE support. In addition to the full range of classical and modern design of experiment approaches, JMP provides a template for Custom Design for specific requirements. The other choices include: Screening Design; Response Surface Design; Choice Design; Accelerated Life Test Design; Nonlinear Design; Space Filling Design; Full Factorial Design; Taguchi Arrays; Mixture Design; and Augmented Design. Further, sample size and power plots are available. We give an introduction to these methods followed by a few examples with factors. BRIEF HISTORY AND BACKGROUND From early times, there has been a desire to improve processes with controlled experimentation. A famous early experiment cured scurvy with seamen taking citrus fruit. Lives were saved. Thomas Edison invented the light bulb with many experiments. These experiments depended on trial and error with no software to guide, measure, and refine experimentation. Japan led the way with improving manufacturing—Genichi Taguchi wanted to create repeatable and robust quality in manufacturing. He had been taught management and statistical methods by William Edwards Deming. Deming taught “Plan, Do, Check, and Act”: PLAN (design) the experiment; DO the experiment by performing the steps; CHECK the results by testing information; and ACT on the decisions based on those results. In America, other pioneers brought us “Total Quality” and “Six Sigma” with “Design of Experiments” being an advanced methodology. -
Cluster Analysis for Gene Expression Data: a Survey
Cluster Analysis for Gene Expression Data: A Survey Daxin Jiang Chun Tang Aidong Zhang Department of Computer Science and Engineering State University of New York at Buffalo Email: djiang3, chuntang, azhang @cse.buffalo.edu Abstract DNA microarray technology has now made it possible to simultaneously monitor the expres- sion levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremen- dous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. Cluster analysis seeks to partition a given data set into groups based on specified features so that the data points within a group are more similar to each other than the points in different groups. A very rich literature on cluster analysis has developed over the past three decades. Many conventional clustering algorithms have been adapted or directly applied to gene expres- sion data, and also new algorithms have recently been proposed specifically aiming at gene ex- pression data. These clustering algorithms have been proven useful for identifying biologically relevant groups of genes and samples. In this paper, we first briefly introduce the concepts of microarray technology and discuss the basic elements of clustering on gene expression data. -
Chapter 5 Contrasts for One-Way ANOVA Page 1. What Is a Contrast?
Chapter 5 Contrasts for one-way ANOVA Page 1. What is a contrast? 5-2 2. Types of contrasts 5-5 3. Significance tests of a single contrast 5-10 4. Brand name contrasts 5-22 5. Relationships between the omnibus F and contrasts 5-24 6. Robust tests for a single contrast 5-29 7. Effect sizes for a single contrast 5-32 8. An example 5-34 Advanced topics in contrast analysis 9. Trend analysis 5-39 10. Simultaneous significance tests on multiple contrasts 5-52 11. Contrasts with unequal cell size 5-62 12. A final example 5-70 5-1 © 2006 A. Karpinski Contrasts for one-way ANOVA 1. What is a contrast? • A focused test of means • A weighted sum of means • Contrasts allow you to test your research hypothesis (as opposed to the statistical hypothesis) • Example: You want to investigate if a college education improves SAT scores. You obtain five groups with n = 25 in each group: o High School Seniors o College Seniors • Mathematics Majors • Chemistry Majors • English Majors • History Majors o All participants take the SAT and scores are recorded o The omnibus F-test examines the following hypotheses: H 0 : µ1 = µ 2 = µ3 = µ 4 = µ5 H1 : Not all µi 's are equal o But you want to know: • Do college seniors score differently than high school seniors? • Do natural science majors score differently than humanities majors? • Do math majors score differently than chemistry majors? • Do English majors score differently than history majors? HS College Students Students Math Chemistry English History µ 1 µ2 µ3 µ4 µ5 5-2 © 2006 A. -
Life of My Terms
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Uses for Census Data in Market Research
Uses for Census Data in Market Research MRS Presentation 4 July 2011 Andrew Zelin, Director, Sampling & RMC, Ipsos MORI Why Census Data is so critical to us Use of Census Data underlies almost all of our quantitative work activities and projects Needed to ensure that: – Samples are balanced and representative – ie Sampling; – Results derived from samples reflect that of target population – ie Weighting; – Understanding how demographic and geographic factors affect the findings from surveys – ie Analysis. How would we do our jobs without the use of Census Data? Our work withoutCensusData Our work Regional Assembly What I will cover Introduction, overview and why Census Data is so critical; Census data for sampling; Census data for survey weighting; Census data for statistical analysis; Use of SARs; The future (eg use of “hypercubes”); Our work without Census Data / use of alternatives; Conclusions Census Data for Sampling For Random (pre-selected) Samples: – Need to define stratum sample sizes and sampling fractions; – Need to define cluster size, esp. if PPS – (Need to define stratum / cluster) membership; For any type of Sample: – To balance samples across demographics that cannot be included as quota controls or stratification variables – To determine number of sample points by geography – To create accurate booster samples (eg young people / ethnic groups) Census Data for Sampling For Quota (non-probability) Samples: – To set appropriate quotas (based on demographics); Data are used here at very localised level – ie Census Outputs Census Data for Sampling Census Data for Survey Weighting To ensure that the sample profile balances the population profile Census Data are needed to tell you what the population profile is ….and hence provide accurate weights ….and hence provide an accurate measure of the design effect and the true level of statistical reliability / confidence intervals on your survey measures But also pre-weighting, for interviewer field dept. -
Subjectivity in American Popular Metal: Contemporary Gothic, the Body, the Grotesque, and the Child
CORE Metadata, citation and similar papers at core.ac.uk Provided by Glasgow Theses Service Subjectivity In American Popular Metal: Contemporary Gothic, The Body, The Grotesque, and The Child. Sara Ann Thomas MA (Hons) Submitted in fulfilment for the Degree of Doctor of Philosophy Faculty of Arts University of Glasgow January 2009 © Sara Thomas 2009 Abstract This thesis examines the subject in Popular American Metal music and culture during the period 1994-2004, concentrating on key artists of the period: Korn, Slipknot, Marilyn Manson, Nine Inch Nails, Tura Satana and My Ruin. Starting from the premise that the subject is consistently portrayed as being at a time of crisis, the thesis draws on textual analysis as an under appreciated approach to popular music, supplemented by theories of stardom in order to examine subjectivity. The study is situated in the context of the growing area of the contemporary gothic, and produces a model of subjectivity specific to this period: the contemporary gothic subject. This model is then used throughout to explore recurrent themes and richly symbolic elements of the music and culture: the body, pain and violence, the grotesque and the monstrous, and the figure of the child, representing a usage of the contemporary gothic that has not previously been attempted. Attention is also paid throughout to the specific late capitalist American cultural context in which the work of these artists is situated, and gives attention to the contradictions inherent in a musical form which is couched in commodity culture but which is highly invested in notions of the ‘Alternative’. In the first chapter I propose the model of the contemporary gothic subject for application to the work of Popular Metal artists of the period, drawing on established theories of the contemporary gothic and Michel Foucault’s theory of confession.