Attribute Gage R&R An Overview
|
|
- Darlene Heath
- 7 years ago
- Views:
Transcription
1 Attribute Gage R&R An Overview Presented to ASQ Section 1302 August 18, 2011 by Jon Ridgway
2 Overview What is an Attribute Gage R&R? Why is it worth the work? What are the caveats? How do I perform it? How do I understand the results?
3 My Attribute Gage R&R Experience First Data Credit Card mailings ( packages ) Airlite Plastics: Mold & Print Quality Print Quality, Cup & Lid Decoration
4 WHY conduct a Gage R&R???? Some processes require subjective decision making: Inspection Validation Subjectivity creates the potential for variation Measurement System variation impacts process capability: Type I Errors Type II Errors
5 So, Why Conduct a Gage R&R? To understand: How likely Appraiser will agree with himself / herself: WITHIN / Repeatability How likely all Appraisers will agree with each other: BETWEEN / Reproducibility Understanding R&R allows you to: Predict probability (%) of agreement / disagreement Implement training to improve that probability Reduce Type I and Type II Errors = $$$
6 MSA The Foundation of everything in Quality is measurement Measure for two primary reasons: To make a decision As the basis for process improvement Can we trust our measurement system to give us reliable data? CONFIDENCE
7 Ultimate purpose of the Attribute Agreement Analysis To determine if your measurement system can distinguish between a good & bad part Accuracy & Precision: Accuracy: Absence of bias, or agreeing with the standard. Precision: Ability of different Appraisers to reach the same conclusion several times.
8 Accurate, But Not Precise
9 Precise, But Not Accurate
10 Which is Easier to Remedy? Accurate, but not Precise Precise, but not Accurate
11 Gage R&R Review Measurement System Analysis (MSA) 1 st R: Repeatability 2 nd R: Reproducibility Data in General: Continuous / Variables Attribute / Discrete
12 Attribute vs. Continuous Attribute Data: Categorical, named only, arbitrary scales Also known as Discrete Data Continuous Data: Allows for infinitely finer sub-divisions Also known as Variables Data
13 Nominal: Literally, name Represents categories Ordinal: Ordered or ranked data Not scaled Basic Data Types Interval: Measured / scaled data: Each position equidistant 0 can be relevant (temperature) Ratio: Numbers compared as multiples of one another
14 Hierarchy of Data Types Nominal Ordinal Interval Ratio Classified Data Quantified Data DISCRETE / ATTRIBUTE Non-parametric CONTINUOUS / VARIABLE Parametric
15 2 Main Attribute Gage R&R Types 1) Binary / Nominal GO / NO GO Data are Categorical and mutually exclusive Kappa statistic is relevant 2) Ordinal Rank, not categorical Data are not mutually exclusive Kendall s statistic more relevant than Kappa
16 Kappa Statistic Proportion of agreement between evaluators after chance agreement has been removed: Kappa = P observed P chance / P chance Expressed as a number: From 0 (expected by chance) Up to +1 (complete agreement)
17 Kendall s Statistics Two different Kendall s for different tests: Kendall s Coefficient of Concordance: Rankings without a known Standard Kendall s Correlation Coefficient: Rankings with a known Standard Expressed as 0 (weaker agreement) to +1 (stronger agreement)
18 Kappa: Kappa & Kendall s Summary Nominal / Binary Only Match or No Match Kendall s Coefficient of Concordance: Ordinal but not using a known Standard Kendall s Correlation Coefficient: Ordinal and using a known Standard
19 Attribute Gage R&R Considerations Study Purpose Destructiveness Precision vs. Time Binomial / Nominal vs. Ordinal
20 MSA Factors Impacting Variation Gage Appraiser Method Part Environment
21 Ideally: Controlling MSA Factors 1. Use the same Assessment Method 2. Require all Appraisers to assess the same dimension / feature / sample 3. Conduct the study under the normal assessment conditions
22 Controlling MSA Factors, Cont. 1) Appraisers: Select from group that normally appraises the part. 2) Number of parts should cover the entire range of variation. 3) More than one appraisal per Appraiser should be done. 4) The presentation of the samples within the Trial should be randomized.
23 Nominal / Binary Study Two Appraisers 50 Parts 2 Trials
24
25
26 Output: Within
27 Output: Within vs. Standard
28 Assessment Agreement Date of study: Reported by: Name of product: Misc: Within Appraisers Appraiser vs Standard % C I Percent % C I Percent Percent 90 Percent Lee Fred 80 Lee Fred Appraiser Appraiser
29 Output: Between
30 Output: Between vs. Standard
31 Ordinal Case Study: Print Quality
32 What did we want to know? Do all Appraisers of Print Quality: Agree consistently with Themselves? Agree consistently with Each Other? Given our world, we have an ordinal system: Accept Accept but Adjust Reject
33 How was it Done? 10 samples, Good & Bad Random Order, Same for All 2 Trials per person All people in the study Environment
34 Ensure Gage R&R Consistency
35 Spanish Version
36 Vietnamese Version
37 Trial Order My Checklist
38
39 Results Sample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard
40 False Alarms & Misses Assess Fail when Standard = Pass: False Alarm Type I Error Assess Pass when Standard = Fail: Miss Type II Error
41 Results False Alarms Sample QA1-1 QA1-2 QA2-1 QA2-2 QA4-1 QA4-2 Standard MISSES
42 Minitab 15 Four Results: 1. Within 2. Within vs. Standard 3. Between 4. Between vs. Standard
43 Check Here
44
45
46 Assessment Agreement Date of study: Reported by: Name of product: Misc: Within Appraisers Appraiser vs Standard % C I Percent % C I Percent Percent Percent QA-1 QA-2 Appraiser QA-4 0 QA-1 QA-2 Appraiser QA-4
47
48
49 Two Big Lessons You can t trust your data until it is proven to be trustworthy. A single, one-time Gage R&R study is not enough
50 Questions? Thank You!
Statistics I for QBIC. Contents and Objectives. Chapters 1 7. Revised: August 2013
Statistics I for QBIC Text Book: Biostatistics, 10 th edition, by Daniel & Cross Contents and Objectives Chapters 1 7 Revised: August 2013 Chapter 1: Nature of Statistics (sections 1.1-1.6) Objectives
More informationResearch Methods & Experimental Design
Research Methods & Experimental Design 16.422 Human Supervisory Control April 2004 Research Methods Qualitative vs. quantitative Understanding the relationship between objectives (research question) and
More informationSample Size and Power in Clinical Trials
Sample Size and Power in Clinical Trials Version 1.0 May 011 1. Power of a Test. Factors affecting Power 3. Required Sample Size RELATED ISSUES 1. Effect Size. Test Statistics 3. Variation 4. Significance
More informationDescriptive Statistics
Descriptive Statistics Primer Descriptive statistics Central tendency Variation Relative position Relationships Calculating descriptive statistics Descriptive Statistics Purpose to describe or summarize
More informationLean Six Sigma Black Belt-EngineRoom
Lean Six Sigma Black Belt-EngineRoom Course Content and Outline Total Estimated Hours: 140.65 *Course includes choice of software: EngineRoom (included for free), Minitab (must purchase separately) or
More informationNONPARAMETRIC STATISTICS 1. depend on assumptions about the underlying distribution of the data (or on the Central Limit Theorem)
NONPARAMETRIC STATISTICS 1 PREVIOUSLY parametric statistics in estimation and hypothesis testing... construction of confidence intervals computing of p-values classical significance testing depend on assumptions
More informationAnalysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk
Analysing Questionnaires using Minitab (for SPSS queries contact -) Graham.Currell@uwe.ac.uk Structure As a starting point it is useful to consider a basic questionnaire as containing three main sections:
More informationStatistics in Medicine Research Lecture Series CSMC Fall 2014
Catherine Bresee, MS Senior Biostatistician Biostatistics & Bioinformatics Research Institute Statistics in Medicine Research Lecture Series CSMC Fall 2014 Overview Review concept of statistical power
More informationX X X a) perfect linear correlation b) no correlation c) positive correlation (r = 1) (r = 0) (0 < r < 1)
CORRELATION AND REGRESSION / 47 CHAPTER EIGHT CORRELATION AND REGRESSION Correlation and regression are statistical methods that are commonly used in the medical literature to compare two or more variables.
More informationTest Reliability Indicates More than Just Consistency
Assessment Brief 015.03 Test Indicates More than Just Consistency by Dr. Timothy Vansickle April 015 Introduction is the extent to which an experiment, test, or measuring procedure yields the same results
More informationStudy Guide for the Final Exam
Study Guide for the Final Exam When studying, remember that the computational portion of the exam will only involve new material (covered after the second midterm), that material from Exam 1 will make
More informationAssessing Measurement System Variation
Assessing Measurement System Variation Example 1: Fuel Injector Nozzle Diameters Problem A manufacturer of fuel injector nozzles installs a new digital measuring system. Investigators want to determine
More informationTHIS PAGE WAS LEFT BLANK INTENTIONALLY
SAMPLE EXAMINATION The purpose of the following sample examination is to present an example of what is provided on exam day by ASQ, complete with the same instructions that are given on exam day. The test
More informationCorrelational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots
Correlational Research Stephen E. Brock, Ph.D., NCSP California State University, Sacramento 1 Correlational Research A quantitative methodology used to determine whether, and to what degree, a relationship
More informationQuantitative Methods for Finance
Quantitative Methods for Finance Module 1: The Time Value of Money 1 Learning how to interpret interest rates as required rates of return, discount rates, or opportunity costs. 2 Learning how to explain
More informationCorrelation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables 2
Lesson 4 Part 1 Relationships between two numerical variables 1 Correlation Coefficient The correlation coefficient is a summary statistic that describes the linear relationship between two numerical variables
More informationElementary Statistics
Elementary Statistics Chapter 1 Dr. Ghamsary Page 1 Elementary Statistics M. Ghamsary, Ph.D. Chap 01 1 Elementary Statistics Chapter 1 Dr. Ghamsary Page 2 Statistics: Statistics is the science of collecting,
More informationII. DISTRIBUTIONS distribution normal distribution. standard scores
Appendix D Basic Measurement And Statistics The following information was developed by Steven Rothke, PhD, Department of Psychology, Rehabilitation Institute of Chicago (RIC) and expanded by Mary F. Schmidt,
More informationGage Studies for Continuous Data
1 Gage Studies for Continuous Data Objectives Determine the adequacy of measurement systems. Calculate statistics to assess the linearity and bias of a measurement system. 1-1 Contents Contents Examples
More informationSection 3 Part 1. Relationships between two numerical variables
Section 3 Part 1 Relationships between two numerical variables 1 Relationship between two variables The summary statistics covered in the previous lessons are appropriate for describing a single variable.
More informationIntroduction to Statistics and Quantitative Research Methods
Introduction to Statistics and Quantitative Research Methods Purpose of Presentation To aid in the understanding of basic statistics, including terminology, common terms, and common statistical methods.
More informationStatistics Review PSY379
Statistics Review PSY379 Basic concepts Measurement scales Populations vs. samples Continuous vs. discrete variable Independent vs. dependent variable Descriptive vs. inferential stats Common analyses
More informationSCHOOL OF HEALTH AND HUMAN SCIENCES DON T FORGET TO RECODE YOUR MISSING VALUES
SCHOOL OF HEALTH AND HUMAN SCIENCES Using SPSS Topics addressed today: 1. Differences between groups 2. Graphing Use the s4data.sav file for the first part of this session. DON T FORGET TO RECODE YOUR
More informationChapter G08 Nonparametric Statistics
G08 Nonparametric Statistics Chapter G08 Nonparametric Statistics Contents 1 Scope of the Chapter 2 2 Background to the Problems 2 2.1 Parametric and Nonparametric Hypothesis Testing......................
More informationNonparametric Statistics
Nonparametric Statistics J. Lozano University of Goettingen Department of Genetic Epidemiology Interdisciplinary PhD Program in Applied Statistics & Empirical Methods Graduate Seminar in Applied Statistics
More informationWhy Is EngineRoom the Right Choice? 1. Cuts the Cost of Calculation
What is EngineRoom? - A Web based data analysis application with an intuitive, drag-and-drop graphical interface. - A suite of powerful, simple-to-use Lean and Six Sigma data analysis tools that you can
More informationWe are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries?
Statistics: Correlation Richard Buxton. 2008. 1 Introduction We are often interested in the relationship between two variables. Do people with more years of full-time education earn higher salaries? Do
More informationBasic research methods. Basic research methods. Question: BRM.2. Question: BRM.1
BRM.1 The proportion of individuals with a particular disease who die from that condition is called... BRM.2 This study design examines factors that may contribute to a condition by comparing subjects
More informationLean Six Sigma Black Belt Body of Knowledge
General Lean Six Sigma Defined UN Describe Nature and purpose of Lean Six Sigma Integration of Lean and Six Sigma UN Compare and contrast focus and approaches (Process Velocity and Quality) Y=f(X) Input
More informationUNIVERSITY OF NAIROBI
UNIVERSITY OF NAIROBI MASTERS IN PROJECT PLANNING AND MANAGEMENT NAME: SARU CAROLYNN ELIZABETH REGISTRATION NO: L50/61646/2013 COURSE CODE: LDP 603 COURSE TITLE: RESEARCH METHODS LECTURER: GAKUU CHRISTOPHER
More informationStudents' Opinion about Universities: The Faculty of Economics and Political Science (Case Study)
Cairo University Faculty of Economics and Political Science Statistics Department English Section Students' Opinion about Universities: The Faculty of Economics and Political Science (Case Study) Prepared
More informationMeasurement in ediscovery
Measurement in ediscovery A Technical White Paper Herbert Roitblat, Ph.D. CTO, Chief Scientist Measurement in ediscovery From an information-science perspective, ediscovery is about separating the responsive
More informationMeasurement and Metrics Fundamentals. SE 350 Software Process & Product Quality
Measurement and Metrics Fundamentals Lecture Objectives Provide some basic concepts of metrics Quality attribute metrics and measurements Reliability, validity, error Correlation and causation Discuss
More informationSIMULATION STUDIES IN STATISTICS WHAT IS A SIMULATION STUDY, AND WHY DO ONE? What is a (Monte Carlo) simulation study, and why do one?
SIMULATION STUDIES IN STATISTICS WHAT IS A SIMULATION STUDY, AND WHY DO ONE? What is a (Monte Carlo) simulation study, and why do one? Simulations for properties of estimators Simulations for properties
More informationBasic Concepts in Research and Data Analysis
Basic Concepts in Research and Data Analysis Introduction: A Common Language for Researchers...2 Steps to Follow When Conducting Research...3 The Research Question... 3 The Hypothesis... 4 Defining the
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationDescriptive Statistics and Measurement Scales
Descriptive Statistics 1 Descriptive Statistics and Measurement Scales Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample
More informationAccurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios
Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are
More informationBusiness Statistics: Intorduction
Business Statistics: Intorduction Donglei Du (ddu@unb.edu) Faculty of Business Administration, University of New Brunswick, NB Canada Fredericton E3B 9Y2 September 23, 2015 Donglei Du (UNB) AlgoTrading
More informationEvaluation & Validation: Credibility: Evaluating what has been learned
Evaluation & Validation: Credibility: Evaluating what has been learned How predictive is a learned model? How can we evaluate a model Test the model Statistical tests Considerations in evaluating a Model
More information11. Analysis of Case-control Studies Logistic Regression
Research methods II 113 11. Analysis of Case-control Studies Logistic Regression This chapter builds upon and further develops the concepts and strategies described in Ch.6 of Mother and Child Health:
More informationSix Sigma Acronyms. 2-1 Do Not Reprint without permission of
Six Sigma Acronyms $k Thousands of dollars $M Millions of dollars % R & R Gauge % Repeatability and Reproducibility ANOVA Analysis of Variance AOP Annual Operating Plan BB Black Belt C & E Cause and Effects
More informationWhat Are the Differences?
Comparison between the MSA manual and VDA Volume 5 What Are the Differences? MSA is short for Measurement Systems Analysis. This document was first published by the Automotive Industry Action Group (AIAG)
More informationThere are three kinds of people in the world those who are good at math and those who are not. PSY 511: Advanced Statistics for Psychological and Behavioral Research 1 Positive Views The record of a month
More informationChapter 4. Probability and Probability Distributions
Chapter 4. robability and robability Distributions Importance of Knowing robability To know whether a sample is not identical to the population from which it was selected, it is necessary to assess the
More informationTABLE OF CONTENTS. About Chi Squares... 1. What is a CHI SQUARE?... 1. Chi Squares... 1. Hypothesis Testing with Chi Squares... 2
About Chi Squares TABLE OF CONTENTS About Chi Squares... 1 What is a CHI SQUARE?... 1 Chi Squares... 1 Goodness of fit test (One-way χ 2 )... 1 Test of Independence (Two-way χ 2 )... 2 Hypothesis Testing
More informationCertified Six Sigma Yellow Belt
Certified Six Sigma Yellow Belt Quality excellence to enhance your career and boost your organization s bottom line asq.org/cert The Global Voice of Quality TM Certification from ASQ is considered a mark
More informationApplying Statistics Recommended by Regulatory Documents
Applying Statistics Recommended by Regulatory Documents Steven Walfish President, Statistical Outsourcing Services steven@statisticaloutsourcingservices.com 301-325 325-31293129 About the Speaker Mr. Steven
More informationSECOND M.B. AND SECOND VETERINARY M.B. EXAMINATIONS INTRODUCTION TO THE SCIENTIFIC BASIS OF MEDICINE EXAMINATION. Friday 14 March 2008 9.00-9.
SECOND M.B. AND SECOND VETERINARY M.B. EXAMINATIONS INTRODUCTION TO THE SCIENTIFIC BASIS OF MEDICINE EXAMINATION Friday 14 March 2008 9.00-9.45 am Attempt all ten questions. For each question, choose the
More informationQUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS This booklet contains lecture notes for the nonparametric work in the QM course. This booklet may be online at http://users.ox.ac.uk/~grafen/qmnotes/index.html.
More informationSampling Strategies for Error Rate Estimation and Quality Control
Project Number: JPA0703 Sampling Strategies for Error Rate Estimation and Quality Control A Major Qualifying Project Report Submitted to the faculty of the Worcester Polytechnic Institute in partial fulfillment
More informationMeasurement Information Model
mcgarry02.qxd 9/7/01 1:27 PM Page 13 2 Information Model This chapter describes one of the fundamental measurement concepts of Practical Software, the Information Model. The Information Model provides
More informationDATA COLLECTION AND ANALYSIS
DATA COLLECTION AND ANALYSIS Quality Education for Minorities (QEM) Network HBCU-UP Fundamentals of Education Research Workshop Gerunda B. Hughes, Ph.D. August 23, 2013 Objectives of the Discussion 2 Discuss
More informationTutorial 5: Hypothesis Testing
Tutorial 5: Hypothesis Testing Rob Nicholls nicholls@mrc-lmb.cam.ac.uk MRC LMB Statistics Course 2014 Contents 1 Introduction................................ 1 2 Testing distributional assumptions....................
More informationPoint Biserial Correlation Tests
Chapter 807 Point Biserial Correlation Tests Introduction The point biserial correlation coefficient (ρ in this chapter) is the product-moment correlation calculated between a continuous random variable
More informationNonparametric tests these test hypotheses that are not statements about population parameters (e.g.,
CHAPTER 13 Nonparametric and Distribution-Free Statistics Nonparametric tests these test hypotheses that are not statements about population parameters (e.g., 2 tests for goodness of fit and independence).
More informationBusiness Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
More informationBODY OF KNOWLEDGE CERTIFIED SIX SIGMA YELLOW BELT
BODY OF KNOWLEDGE CERTIFIED SIX SIGMA YELLOW BELT The topics in this Body of Knowledge include additional detail in the form of subtext explanations and the cognitive level at which test questions will
More informationUsing Excel for inferential statistics
FACT SHEET Using Excel for inferential statistics Introduction When you collect data, you expect a certain amount of variation, just caused by chance. A wide variety of statistical tests can be applied
More informationLAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE
LAGUARDIA COMMUNITY COLLEGE CITY UNIVERSITY OF NEW YORK DEPARTMENT OF MATHEMATICS, ENGINEERING, AND COMPUTER SCIENCE MAT 119 STATISTICS AND ELEMENTARY ALGEBRA 5 Lecture Hours, 2 Lab Hours, 3 Credits Pre-
More informationUNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010
UNIVERSITY of MASSACHUSETTS DARTMOUTH Charlton College of Business Decision and Information Sciences Fall 2010 COURSE: POM 500 Statistical Analysis, ONLINE EDITION, Fall 2010 Prerequisite: Finite Math
More informationConsider a study in which. How many subjects? The importance of sample size calculations. An insignificant effect: two possibilities.
Consider a study in which How many subjects? The importance of sample size calculations Office of Research Protections Brown Bag Series KB Boomer, Ph.D. Director, boomer@stat.psu.edu A researcher conducts
More informationDESCRIPTIVE STATISTICS. The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses.
DESCRIPTIVE STATISTICS The purpose of statistics is to condense raw data to make it easier to answer specific questions; test hypotheses. DESCRIPTIVE VS. INFERENTIAL STATISTICS Descriptive To organize,
More informationWHAT IS A JOURNAL CLUB?
WHAT IS A JOURNAL CLUB? With its September 2002 issue, the American Journal of Critical Care debuts a new feature, the AJCC Journal Club. Each issue of the journal will now feature an AJCC Journal Club
More informationSample Size Planning, Calculation, and Justification
Sample Size Planning, Calculation, and Justification Theresa A Scott, MS Vanderbilt University Department of Biostatistics theresa.scott@vanderbilt.edu http://biostat.mc.vanderbilt.edu/theresascott Theresa
More informationUnit 1: Introduction to Quality Management
Unit 1: Introduction to Quality Management Definition & Dimensions of Quality Quality Control vs Quality Assurance Small-Q vs Big-Q & Evolution of Quality Movement Total Quality Management (TQM) & its
More informationCHAPTER 1 THE CERTIFIED QUALITY ENGINEER EXAM. 1.0 The Exam. 2.0 Suggestions for Study. 3.0 CQE Examination Content. Where shall I begin your majesty?
QReview 1 CHAPTER 1 THE CERTIFIED QUALITY ENGINEER EXAM 1.0 The Exam 2.0 Suggestions for Study 3.0 CQE Examination Content Where shall I begin your majesty? The White Rabbit Begin at the beginning, and
More informationStatistical tests for SPSS
Statistical tests for SPSS Paolo Coletti A.Y. 2010/11 Free University of Bolzano Bozen Premise This book is a very quick, rough and fast description of statistical tests and their usage. It is explicitly
More informationLecture 2: Types of Variables
2typesofvariables.pdf Michael Hallstone, Ph.D. hallston@hawaii.edu Lecture 2: Types of Variables Recap what we talked about last time Recall how we study social world using populations and samples. Recall
More informationCORRELATIONAL ANALYSIS: PEARSON S r Purpose of correlational analysis The purpose of performing a correlational analysis: To discover whether there
CORRELATIONAL ANALYSIS: PEARSON S r Purpose of correlational analysis The purpose of performing a correlational analysis: To discover whether there is a relationship between variables, To find out the
More informationThe Statistics Tutor s Quick Guide to
statstutor community project encouraging academics to share statistics support resources All stcp resources are released under a Creative Commons licence The Statistics Tutor s Quick Guide to Stcp-marshallowen-7
More informationCERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE
CERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE The topics in this Body of Knowledge include subtext explanations and the cognitive level at which the questions will be written. This information will
More informationValidation and Calibration. Definitions and Terminology
Validation and Calibration Definitions and Terminology ACCEPTANCE CRITERIA: The specifications and acceptance/rejection criteria, such as acceptable quality level and unacceptable quality level, with an
More informationCredit Risk Models. August 24 26, 2010
Credit Risk Models August 24 26, 2010 AGENDA 1 st Case Study : Credit Rating Model Borrowers and Factoring (Accounts Receivable Financing) pages 3 10 2 nd Case Study : Credit Scoring Model Automobile Leasing
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationMeasurement: Reliability and Validity Measures. Jonathan Weiner, DrPH Johns Hopkins University
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationCERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE
CERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE The topics in this Body of Knowledge include subtext explanations and the cognitive level at which the questions will be written. This information will
More informationStatistics for Sports Medicine
Statistics for Sports Medicine Suzanne Hecht, MD University of Minnesota (suzanne.hecht@gmail.com) Fellow s Research Conference July 2012: Philadelphia GOALS Try not to bore you to death!! Try to teach
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationStatistics. Measurement. Scales of Measurement 7/18/2012
Statistics Measurement Measurement is defined as a set of rules for assigning numbers to represent objects, traits, attributes, or behaviors A variableis something that varies (eye color), a constant does
More informationAdequacy of Biomath. Models. Empirical Modeling Tools. Bayesian Modeling. Model Uncertainty / Selection
Directions in Statistical Methodology for Multivariable Predictive Modeling Frank E Harrell Jr University of Virginia Seattle WA 19May98 Overview of Modeling Process Model selection Regression shape Diagnostics
More informationGuided Reading 9 th Edition. informed consent, protection from harm, deception, confidentiality, and anonymity.
Guided Reading Educational Research: Competencies for Analysis and Applications 9th Edition EDFS 635: Educational Research Chapter 1: Introduction to Educational Research 1. List and briefly describe the
More informationData quality in Accounting Information Systems
Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania
More informationMeasurement and Measurement Scales
Measurement and Measurement Scales Measurement is the foundation of any scientific investigation Everything we do begins with the measurement of whatever it is we want to study Definition: measurement
More informationCenter for Advanced Studies in Measurement and Assessment. CASMA Research Report
Center for Advanced Studies in Measurement and Assessment CASMA Research Report Number 13 and Accuracy Under the Compound Multinomial Model Won-Chan Lee November 2005 Revised April 2007 Revised April 2008
More informationNAG C Library Chapter Introduction. g08 Nonparametric Statistics
g08 Nonparametric Statistics Introduction g08 NAG C Library Chapter Introduction g08 Nonparametric Statistics Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Parametric and Nonparametric
More informationSocial Media Mining. Data Mining Essentials
Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers
More informationConcepts of Variables. Levels of Measurement. The Four Levels of Measurement. Nominal Scale. Greg C Elvers, Ph.D.
Concepts of Variables Greg C Elvers, Ph.D. 1 Levels of Measurement When we observe and record a variable, it has characteristics that influence the type of statistical analysis that we can perform on it
More informationMeasurement Systems Correlation MSC for Suppliers
Measurement Systems Correlation MSC for Suppliers Copyright 2003-2007 Raytheon Company. All rights reserved. R6σ is a Raytheon trademark registered in the United States and Europe. Raytheon Six Sigma is
More informationEPS 625 INTERMEDIATE STATISTICS FRIEDMAN TEST
EPS 625 INTERMEDIATE STATISTICS The Friedman test is an extension of the Wilcoxon test. The Wilcoxon test can be applied to repeated-measures data if participants are assessed on two occasions or conditions
More information200628 - DAIC - Advanced Experimental Design in Clinical Research
Coordinating unit: Teaching unit: Academic year: Degree: ECTS credits: 2015 200 - FME - School of Mathematics and Statistics 1004 - UB - (ENG)Universitat de Barcelona MASTER'S DEGREE IN STATISTICS AND
More informationFairfield Public Schools
Mathematics Fairfield Public Schools AP Statistics AP Statistics BOE Approved 04/08/2014 1 AP STATISTICS Critical Areas of Focus AP Statistics is a rigorous course that offers advanced students an opportunity
More informationA full analysis example Multiple correlations Partial correlations
A full analysis example Multiple correlations Partial correlations New Dataset: Confidence This is a dataset taken of the confidence scales of 41 employees some years ago using 4 facets of confidence (Physical,
More informationSOST 201 September 18-20, 2006. Measurement of Variables 2
1 Social Studies 201 September 18-20, 2006 Measurement of variables See text, chapter 3, pp. 61-86. These notes and Chapter 3 of the text examine ways of measuring variables in order to describe members
More informationStudy Design and Statistical Analysis
Study Design and Statistical Analysis Anny H Xiang, PhD Department of Preventive Medicine University of Southern California Outline Designing Clinical Research Studies Statistical Data Analysis Designing
More informationREGULATIONS FOR THE POSTGRADUATE DIPLOMA IN CLINICAL RESEARCH METHODOLOGY (PDipClinResMethodology)
452 REGULATIONS FOR THE POSTGRADUATE DIPLOMA IN CLINICAL RESEARCH METHODOLOGY (PDipClinResMethodology) (See also General Regulations) M.57 Admission requirements To be eligible for admission to the courses
More informationComparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3)
Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) What is EngineRoom? A Microsoft Excel add in A suite of powerful, simple to use Lean and Six Sigma data analysis tools Built for
More informationOnline 12 - Sections 9.1 and 9.2-Doug Ensley
Student: Date: Instructor: Doug Ensley Course: MAT117 01 Applied Statistics - Ensley Assignment: Online 12 - Sections 9.1 and 9.2 1. Does a P-value of 0.001 give strong evidence or not especially strong
More informationConfidence Intervals for Spearman s Rank Correlation
Chapter 808 Confidence Intervals for Spearman s Rank Correlation Introduction This routine calculates the sample size needed to obtain a specified width of Spearman s rank correlation coefficient confidence
More informationGenetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve
Genetic Algorithms commonly used selection, replacement, and variation operators Fernando Lobo University of Algarve Outline Selection methods Replacement methods Variation operators Selection Methods
More information