Statistical Rules of Thumb


 Jasper Sparks
 2 years ago
 Views:
Transcription
1 Statistical Rules of Thumb Second Edition Gerald van Belle University of Washington Department of Biostatistics and Department of Environmental and Occupational Health Sciences Seattle, WA WILEY AJOHN WILEY & SONS, INC., PUBLICATION
2 Contents Preface to the Second Edition Preface to the First Edition Acronyms xiv xvi xix 1 The Basics I 1.1 Four Basic Questions Observation is Selection Replicate to Characterize Variability Variability Occurs at Multiple Levels Invalid Selection is the Primary Threat to Valid Inference There is Variation in Strength of Inference Distinguish Randomized and Observational Studies Beware of Linear Models Keep Models As Simple As Possible, But Not More Simple Understand Omnibus Quantities 14 vii
3 viii CONTENTS 1.11 Do Not Multiply Probabilities More Than Necessary Use Twosided pvalues pvalues for Sample Size, Confidence Intervals for Results At Least Twelve Observations for a Confidence Interval Estimate ± Two Standard Errors is Remarkably Robust Know the Unit of the Variable Be Flexible About Scale of Measurement Determining Analysis Be Eclectic and Ecumenical in Inference 25 2 Sample Size Begin with a Basic Formula for Sample SizeLehr's Equation Calculating Sample Size Using the Coefficient of Variation No Finite Population Correction for Survey Sample Size Standard Deviation and Sample Range Do Not Formulate a Study Solely in Terms of Effect Size Overlapping Confidence Intervals Do Not Imply Nonsignificance Sample Size Calculation for the Poisson Distribution Sample Size for Poisson With Background Rate Sample Size Calculation for the Binomial Distribution When Unequal Sample Sizes Matter; When They Don't Sample Size With Different Costs for the Two Samples The Rule of Threes for 95% Upper Bounds When There Are No Events Sample Size Calculations Are Determined by the Analysis 50
4 CONTENTS ix 3 Observational Studies The Model for an Observational Study is the Sample Survey Large Sample Size Does Not Guarantee Validity Good Observational Studies Are Designed To Establish Cause Effect Requires Longitudinal Data Make Theories Elaborate to Establish Cause and Effect The Hill Guidelines Are a Useful Guide to Show Cause Effect Sensitivity Analyses Assess Model Uncertainty and Missing Data 61 4 Covariation Assessing and Describing Covariation Don't Summarize Regression Sampling Schemes with Correlation Do Not Correlate Rates or Ratios Indiscriminately Determining Sample Size to Estimate a Correlation Pairing Data is not Always Good Go Beyond Correlation in Drawing Conclusions Agreement As Accuracy, Scale Differential, and Precision Assess Test Reliability by Means of Agreement Range of the Predictor Variable and Regression Measuring Change: Width More Important than Numbers 84 5 Environmental Studies Begin with the Lognormal Distribution in Environmental Studies Differences Are More Symmetrical Know the Sample Space for Statements of Risk Beware of Pseudoreplication Think Beyond Simple Random Sampling The Size of the Population and Small Effects 96
5 CONTENTS 5.7 Models of Small Effects Are Sensitive to Assumptions Distinguish Between Variability and Uncertainty Description of the Database is As Important as Its Data Always Assess the Statistical Basis for an Environmental Standard Measurement of a Standard and Policy Parametric Analyses Make Maximum Use of the Data Confidence, Prediction, and Tolerance Intervals Statistics and Risk Assessment Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants Assess the Errors in Calibration Due to Inverse Regression 111 Epidemiology Start with the Poisson to Model Incidence or Prevalence The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare The Number of Events is Crucial in Estimating Sample Sizes Use a Logarithmic Formulation to Calculate Sample Size Take No More than Four or Five Controls per Case Obtain at Least Ten Subjects for Every Variable Investigated Begin with the Exponential Distribution to Model Time to Event Begin with Two Exponentials for Comparing Survival Times Be Wary of Surrogates Prevalence Dominates in Screening Rare Diseases Do Not Dichotomize Unless Absolutely Necessary Additive and Multiplicative Models 139
6 CONTENTS XI 7 EvidenceBased Medicine Strength of Evidence Relevance of Information: POEM vs. DOE Begin with Absolute Risk Reduction, then Follow with Relative Risk The Number Needed to Treat (NNT) is Clinically Useful Variability in Response to Treatment Needs to be Considered Safety is the Weak Component of EBM Intent to Treat is the Default Analysis Use Prior Information but not Priors The Four Key Questions for Metaanalysts Design, Conduct, and Analysis Randomization Puts Systematic Effects into the Error Term Blocking is the Key to Reducing Variability Factorial Designs and Joint Effects HighOrder Interactions Occur Rarely Balanced Designs Allow Easy Assessment of Joint Effects Analysis Follows Design Independence, Equal Variance, and Normality Plan to Graph the Results of an Analysis Distinguish Between Design Structure and Treatment Structure Make Hierarchical Analyses the Default Analysis Distinguish Between Nested and Crossed Designs Not Always Easy Plan for Missing Data Address Multiple Comparisons Before Starting the Study Know Properties Preserved When Transforming Units Consider Bootstrapping for Complex Relationships 191
7 X/7 CONTENTS 9 Words, Tables, and Graphs Use Text for a Few Numbers, Tables for Many Numbers, Graphs for Complex Relationships Arrange Information in a Table to Drive Home the Message Always Graph the Data Always Graph Results of An Analysis of Variance Never Use a Pie Chart Bar Graphs Waste Ink; They Don't Illuminate Complex Relationships Stacked Bar Graphs Are Worse Than Bar Graphs ThreeDimensional Bar Graphs Constitute Misdirected Artistry Identify Crosssectional and Longitudinal Patterns in Longitudinal Data Use Rendering, Manipulation, and Linking in HighDimensional Data Consulting Session Has Beginning, Middle, and End Ask Questions Make Distinctions Know Yourself, Know the Investigator Tailor Advice to the Level of the Investigator Use Units the Investigator is Comfortable With Agree on Assignment of Responsibilities Any Basic Statistical Computing Package Will Do Ethics Precedes, Guides, and Follows Consultation Be Proactive in Statistical Consulting Use the Web for Reference, Resource, and Education Listen to, and Heed the Advice of Experts in the Field 233 Epilogue 236 References 239
8 CONTENTS xiii Author Index 255 Topic Index 261
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2015 Examinations Aim The aim of the Probability and Mathematical Statistics subject is to provide a grounding in
More informationfifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson
fifty Fathoms Statistics Demonstrations for Deeper Understanding Tim Erickson Contents What Are These Demos About? How to Use These Demos If This Is Your First Time Using Fathom Tutorial: An Extended Example
More informationbusiness statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
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 informationQUANTITATIVE METHODS. for Decision Makers. Mik Wisniewski. Fifth Edition. FT Prentice Hall
Fifth Edition QUANTITATIVE METHODS for Decision Makers Mik Wisniewski Senior Research Fellow, Department of Management Science, University of Strathclyde Business School FT Prentice Hall FINANCIAL TIMES
More informationData Analysis, Research Study Design and the IRB
Minding the pvalues p and Quartiles: Data Analysis, Research Study Design and the IRB Don AllensworthDavies, MSc Research Manager, Data Coordinating Center Boston University School of Public Health IRB
More informationSTRUTS: Statistical Rules of Thumb. Seattle, WA
STRUTS: Statistical Rules of Thumb Gerald van Belle Departments of Environmental Health and Biostatistics University ofwashington Seattle, WA 981954691 Steven P. Millard Probability, Statistics and Information
More informationMethods for Metaanalysis in Medical Research
Methods for Metaanalysis in Medical Research Alex J. Sutton University of Leicester, UK Keith R. Abrams University of Leicester, UK David R. Jones University of Leicester, UK Trevor A. Sheldon University
More informationCOURSE OUTLINE. Course Number Course Title Credits MAT201 Probability and Statistics for Science and Engineering 4. Co or Prerequisite
COURSE OUTLINE Course Number Course Title Credits MAT201 Probability and Statistics for Science and Engineering 4 Hours: Lecture/Lab/Other 4 Lecture Co or Prerequisite MAT151 or MAT149 with a minimum
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 informationEBM Cheat Sheet Measurements Card
EBM Cheat Sheet Measurements Card Basic terms: Prevalence = Number of existing cases of disease at a point in time / Total population. Notes: Numerator includes old and new cases Prevalence is crosssectional
More informationStatistics 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 1.11.6) Objectives
More informationContents. The Real Numbers. Linear Equations and Inequalities in One Variable
dug33513_fm.qxd 11/20/07 3:21 PM Page vii Preface Guided Tour: Features and Supplements Applications Index 1 2 The Real Numbers 1.1 1.2 1.3 1.4 1.5 1.6 1 Sets 2 The Real Numbers 9 Operations on the Set
More informationRegression Modeling Strategies
Frank E. Harrell, Jr. Regression Modeling Strategies With Applications to Linear Models, Logistic Regression, and Survival Analysis With 141 Figures Springer Contents Preface Typographical Conventions
More informationSAMPLE SIZE TABLES FOR LOGISTIC REGRESSION
STATISTICS IN MEDICINE, VOL. 8, 795802 (1989) SAMPLE SIZE TABLES FOR LOGISTIC REGRESSION F. Y. HSIEH* Department of Epidemiology and Social Medicine, Albert Einstein College of Medicine, Bronx, N Y 10461,
More informationCompetency 1 Describe the role of epidemiology in public health
The Northwest Center for Public Health Practice (NWCPHP) has developed competencybased epidemiology training materials for public health professionals in practice. Epidemiology is broadly accepted as
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 informationINTRODUCTORY STATISTICS
INTRODUCTORY STATISTICS FIFTH EDITION Thomas H. Wonnacott University of Western Ontario Ronald J. Wonnacott University of Western Ontario WILEY JOHN WILEY & SONS New York Chichester Brisbane Toronto Singapore
More informationHypothesis Testing Level I Quantitative Methods. IFT Notes for the CFA exam
Hypothesis Testing 2014 Level I Quantitative Methods IFT Notes for the CFA exam Contents 1. Introduction... 3 2. Hypothesis Testing... 3 3. Hypothesis Tests Concerning the Mean... 10 4. Hypothesis Tests
More informationPrinciples of Hypothesis Testing for Public Health
Principles of Hypothesis Testing for Public Health Laura Lee Johnson, Ph.D. Statistician National Center for Complementary and Alternative Medicine johnslau@mail.nih.gov Fall 2011 Answers to Questions
More informationCONTENTS OF DAY 2. II. Why Random Sampling is Important 9 A myth, an urban legend, and the real reason NOTES FOR SUMMER STATISTICS INSTITUTE COURSE
1 2 CONTENTS OF DAY 2 I. More Precise Definition of Simple Random Sample 3 Connection with independent random variables 3 Problems with small populations 8 II. Why Random Sampling is Important 9 A myth,
More informationStatistical Tools for Nonlinear Regression
S. Huet A. Bouvier M.A. Poursat E. Jolivet Statistical Tools for Nonlinear Regression A Practical Guide With SPLUS and R Examples Second Edition Springer Preface to the Second Edition Preface to the
More informationCurriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010
Curriculum Map Statistics and Probability Honors (348) Saugus High School Saugus Public Schools 20092010 Week 1 Week 2 14.0 Students organize and describe distributions of data by using a number of different
More informationElements of statistics (MATH04871)
Elements of statistics (MATH04871) Prof. Dr. Dr. K. Van Steen University of Liège, Belgium December 10, 2012 Introduction to Statistics Basic Probability Revisited Sampling Exploratory Data Analysis 
More informationSimple Regression Theory II 2010 Samuel L. Baker
SIMPLE REGRESSION THEORY II 1 Simple Regression Theory II 2010 Samuel L. Baker Assessing how good the regression equation is likely to be Assignment 1A gets into drawing inferences about how close the
More informationSize of a study. Chapter 15
Size of a study 15.1 Introduction It is important to ensure at the design stage that the proposed number of subjects to be recruited into any study will be appropriate to answer the main objective(s) of
More informationChris Slaughter, DrPH. GI Research Conference June 19, 2008
Chris Slaughter, DrPH Assistant Professor, Department of Biostatistics Vanderbilt University School of Medicine GI Research Conference June 19, 2008 Outline 1 2 3 Factors that Impact Power 4 5 6 Conclusions
More informationMissing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13
Missing Data: Part 1 What to Do? Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 3/20/13 Overview Missingness and impact on statistical analysis Missing data assumptions/mechanisms Conventional
More informationIntroduction to Statistics with SPSS for Social Science
New Introduction to Statistics with SPSS for Social Science Gareth Norris Faiza Qureshi Dennis Howitt Duncan Cramer Aberystwyth University City University London University of Loughborough University of
More informationLean Six Sigma Analyze Phase Introduction. TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY
TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY Before we begin: Turn on the sound on your computer. There is audio to accompany this presentation. Audio will accompany most of the online
More informationProbability of rejecting the null hypothesis when
Sample Size The first question faced by a statistical consultant, and frequently the last, is, How many subjects (animals, units) do I need? This usually results in exploring the size of the treatment
More informationConfidence Intervals for the Difference Between Two Means
Chapter 47 Confidence Intervals for the Difference Between Two Means Introduction This procedure calculates the sample size necessary to achieve a specified distance from the difference in sample means
More informationSTATISTICA Formula Guide: Logistic Regression. Table of Contents
: Table of Contents... 1 Overview of Model... 1 Dispersion... 2 Parameterization... 3 SigmaRestricted Model... 3 Overparameterized Model... 4 Reference Coding... 4 Model Summary (Summary Tab)... 5 Summary
More informationTechnology StepbyStep Using StatCrunch
Technology StepbyStep Using StatCrunch Section 1.3 Simple Random Sampling 1. Select Data, highlight Simulate Data, then highlight Discrete Uniform. 2. Fill in the following window with the appropriate
More informationConstruction of the Real Line 2 Is Every Real Number Rational? 3 Problems Algebra of the Real Numbers 7
About the Author v Preface to the Instructor xiii WileyPLUS xviii Acknowledgments xix Preface to the Student xxi 1 The Real Numbers 1 1.1 The Real Line 2 Construction of the Real Line 2 Is Every Real Number
More informationAppendix G STATISTICAL METHODS INFECTIOUS METHODS STATISTICAL ROADMAP. Prepared in Support of: CDC/NCEH Cross Sectional Assessment Study.
Appendix G STATISTICAL METHODS INFECTIOUS METHODS STATISTICAL ROADMAP Prepared in Support of: CDC/NCEH Cross Sectional Assessment Study Prepared by: Centers for Disease Control and Prevention National
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, McGrawHill/Irwin, 2008, ISBN: 9780073319889. Required Computing
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 informationOrganizing Your Approach to a Data Analysis
Biost/Stat 578 B: Data Analysis Emerson, September 29, 2003 Handout #1 Organizing Your Approach to a Data Analysis The general theme should be to maximize thinking about the data analysis and to minimize
More informationService courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.
Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are
More informationIntroduction to Financial Models for Management and Planning
CHAPMAN &HALL/CRC FINANCE SERIES Introduction to Financial Models for Management and Planning James R. Morris University of Colorado, Denver U. S. A. John P. Daley University of Colorado, Denver U. S.
More informationPrinciples of Hypothesis
Principles of Hypothesis Testing for Public Health Laura Lee Johnson, Ph.D. Statistician National Center for Complementary and Alternative Medicine johnslau@mail.nih.gov Fall 2011 Answers to Questions
More informationStatistical & Analytical Curriculum
Statistical & Analytical Curriculum 2014 1 Courses Days Engineering Statistics and Data Analysis 3 Design of Experiments 2 Mixture DOE 1 Robust Optimization and Tolerance Design 2 Measurement Systems Analysis
More informationRegression. Name: Class: Date: Multiple Choice Identify the choice that best completes the statement or answers the question.
Class: Date: Regression Multiple Choice Identify the choice that best completes the statement or answers the question. 1. Given the least squares regression line y8 = 5 2x: a. the relationship between
More informationSemester 2 Statistics Short courses
Semester 2 Statistics Short courses Course: STAA0001  Basic Statistics Blackboard Site: STAA0001 Dates: Sat 10 th Sept and 22 Oct 2016 (9 am 5 pm) Room EN409 Assumed Knowledge: None Day 1: Exploratory
More informationDelme John Pritchard
THE GENETICS OF ALZHEIMER S DISEASE, MODELLING DISABILITY AND ADVERSE SELECTION IN THE LONGTERM CARE INSURANCE MARKET By Delme John Pritchard Submitted for the Degree of Doctor of Philosophy at HeriotWatt
More informationChi Squared and Fisher's Exact Tests. Observed vs Expected Distributions
BMS 617 Statistical Techniques for the Biomedical Sciences Lecture 11: ChiSquared and Fisher's Exact Tests Chi Squared and Fisher's Exact Tests This lecture presents two similarly structured tests, Chisquared
More informationLecture 18 Linear Regression
Lecture 18 Statistics Unit Andrew Nunekpeku / Charles Jackson Fall 2011 Outline 1 1 Situation  used to model quantitative dependent variable using linear function of quantitative predictor(s). Situation
More informationCOMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES.
277 CHAPTER VI COMPARISONS OF CUSTOMER LOYALTY: PUBLIC & PRIVATE INSURANCE COMPANIES. This chapter contains a full discussion of customer loyalty comparisons between private and public insurance companies
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 informationQUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC TESTS
QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NONPARAMETRIC 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 informationStatistics for Experimenters
Statistics for Experimenters Design, Innovation, and Discovery Second Edition GEORGE E. P. BOX J. STUART HUNTER WILLIAM G. HUNTER WILEY INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION FACHGEBIETSBGCHEREI
More informationBusiness Analytics. Methods, Models, and Decisions. James R. Evans : University of Cincinnati PEARSON
Business Analytics Methods, Models, and Decisions James R. Evans : University of Cincinnati PEARSON Boston Columbus Indianapolis New York San Francisco Upper Saddle River Amsterdam Cape Town Dubai London
More information12/31/2016. PSY 512: Advanced Statistics for Psychological and Behavioral Research 2
PSY 512: Advanced Statistics for Psychological and Behavioral Research 2 Understand when to use multiple Understand the multiple equation and what the coefficients represent Understand different methods
More informationSemester 1 Statistics Short courses
Semester 1 Statistics Short courses Course: STAA0001 Basic Statistics Blackboard Site: STAA0001 Dates: Sat. March 12 th and Sat. April 30 th (9 am 5 pm) Assumed Knowledge: None Course Description Statistical
More informationAMS7: WEEK 8. CLASS 1. Correlation Monday May 18th, 2015
AMS7: WEEK 8. CLASS 1 Correlation Monday May 18th, 2015 Type of Data and objectives of the analysis Paired sample data (Bivariate data) Determine whether there is an association between two variables This
More informationChapter 2: Systems of Linear Equations and Matrices:
At the end of the lesson, you should be able to: Chapter 2: Systems of Linear Equations and Matrices: 2.1: Solutions of Linear Systems by the Echelon Method Define linear systems, unique solution, inconsistent,
More informationWhen Does it Make Sense to Perform a MetaAnalysis?
CHAPTER 40 When Does it Make Sense to Perform a MetaAnalysis? Introduction Are the studies similar enough to combine? Can I combine studies with different designs? How many studies are enough to carry
More informationSchools Valueadded Information System Technical Manual
Schools Valueadded Information System Technical Manual Quality Assurance & Schoolbased Support Division Education Bureau 2015 Contents Unit 1 Overview... 1 Unit 2 The Concept of VA... 2 Unit 3 Control
More informationANOVA Analysis of Variance
ANOVA Analysis of Variance What is ANOVA and why do we use it? Can test hypotheses about mean differences between more than 2 samples. Can also make inferences about the effects of several different IVs,
More informationLEARNING OBJECTIVES SCALES OF MEASUREMENT: A REVIEW SCALES OF MEASUREMENT: A REVIEW DESCRIBING RESULTS DESCRIBING RESULTS 8/14/2016
UNDERSTANDING RESEARCH RESULTS: DESCRIPTION AND CORRELATION LEARNING OBJECTIVES Contrast three ways of describing results: Comparing group percentages Correlating scores Comparing group means Describe
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 information1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number
1) Write the following as an algebraic expression using x as the variable: Triple a number subtracted from the number A. 3(x  x) B. x 3 x C. 3x  x D. x  3x 2) Write the following as an algebraic expression
More informationMinitab Guide. This packet contains: A Friendly Guide to Minitab. Minitab StepByStep
Minitab Guide This packet contains: A Friendly Guide to Minitab An introduction to Minitab; including basic Minitab functions, how to create sets of data, and how to create and edit graphs of different
More informationStatistics Graduate Courses
Statistics Graduate Courses STAT 7002Topics in StatisticsBiological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.
More informationEcological Methodology Second Edition
Ecological Methodology Second Edition Charles J. Krebs University of British Columbia Technische Universitat Darmstadt FACHBEREICH 10 BIOLOGIE Bi bliothek SchnittspahnstraBe 10 D64 28 7 Darmstadt Inv.Nr.
More informationEconomic Order Quantity and Economic Production Quantity Models for Inventory Management
Economic Order Quantity and Economic Production Quantity Models for Inventory Management Inventory control is concerned with minimizing the total cost of inventory. In the U.K. the term often used is stock
More informationCourse Text. Required Computing Software. Course Description. Course Objectives. StraighterLine. Business Statistics
Course Text Business Statistics Lind, Douglas A., Marchal, William A. and Samuel A. Wathen. Basic Statistics for Business and Economics, 7th edition, McGrawHill/Irwin, 2010, ISBN: 9780077384470 [This
More informationRUTHERFORD HIGH SCHOOL Rutherford, New Jersey COURSE OUTLINE STATISTICS AND PROBABILITY
RUTHERFORD HIGH SCHOOL Rutherford, New Jersey COURSE OUTLINE STATISTICS AND PROBABILITY I. INTRODUCTION According to the Common Core Standards (2010), Decisions or predictions are often based on data numbers
More informationMTH 140 Statistics Videos
MTH 140 Statistics Videos Chapter 1 Picturing Distributions with Graphs Individuals and Variables Categorical Variables: Pie Charts and Bar Graphs Categorical Variables: Pie Charts and Bar Graphs Quantitative
More informationCHAPTER THREE COMMON DESCRIPTIVE STATISTICS COMMON DESCRIPTIVE STATISTICS / 13
COMMON DESCRIPTIVE STATISTICS / 13 CHAPTER THREE COMMON DESCRIPTIVE STATISTICS The analysis of data begins with descriptive statistics such as the mean, median, mode, range, standard deviation, variance,
More informationJournal Article Reporting Standards (JARS)
APPENDIX Journal Article Reporting Standards (JARS), MetaAnalysis Reporting Standards (MARS), and Flow of Participants Through Each Stage of an Experiment or QuasiExperiment 245 Journal Article Reporting
More informationChecklists and Examples for Registering Statistical Analyses
Checklists and Examples for Registering Statistical Analyses For welldesigned confirmatory research, all analysis decisions that could affect the confirmatory results should be planned and registered
More informationSimple Linear Regression
STAT 101 Dr. Kari Lock Morgan Simple Linear Regression SECTIONS 9.3 Confidence and prediction intervals (9.3) Conditions for inference (9.1) Want More Stats??? If you have enjoyed learning how to analyze
More informationRegression in SPSS. Workshop offered by the Mississippi Center for Supercomputing Research and the UM Office of Information Technology
Regression in SPSS Workshop offered by the Mississippi Center for Supercomputing Research and the UM Office of Information Technology John P. Bentley Department of Pharmacy Administration University of
More informationSome Critical Information about SOME Statistical Tests and Measures of Correlation/Association
Some Critical Information about SOME Statistical Tests and Measures of Correlation/Association This information is adapted from and draws heavily on: Sheskin, David J. 2000. Handbook of Parametric and
More informationConfidence Intervals in Public Health
Confidence Intervals in Public Health When public health practitioners use health statistics, sometimes they are interested in the actual number of health events, but more often they use the statistics
More informationtraining programme in pharmaceutical medicine Clinical Data Management and Analysis
training programme in pharmaceutical medicine Clinical Data Management and Analysis 1921 may 2011 Clinical Data Management and Analysis 19 21 MAY 2011 LocaL: University of Aveiro, Campus Universitário
More informationTailDependence an Essential Factor for Correctly Measuring the Benefits of Diversification
TailDependence an Essential Factor for Correctly Measuring the Benefits of Diversification Presented by Work done with Roland Bürgi and Roger Iles New Views on Extreme Events: Coupled Networks, Dragon
More informationIntroduction to method validation
Introduction to method validation Introduction to method validation What is method validation? Method validation provides documented objective evidence that a method measures what it is intended to measure,
More informationScience & Engineering Practices in Next Generation Science Standards
Science & Engineering Practices in Next Generation Science Standards Asking Questions and Defining Problems: A practice of science is to ask and refine questions that lead to descriptions and explanations
More informationGuide to Biostatistics
MedPage Tools Guide to Biostatistics Study Designs Here is a compilation of important epidemiologic and common biostatistical terms used in medical research. You can use it as a reference guide when reading
More informationSENSITIVITY ANALYSIS AND INFERENCE. Lecture 12
This work is licensed under a Creative Commons AttributionNonCommercialShareAlike License. Your use of this material constitutes acceptance of that license and the conditions of use of materials on this
More informationBASIC MATH CALCULATIONS
BASIC MATH CALCULATIONS It is highly suggested you complete this packet to assist you in being successful with the basic math competency test. You will be allowed to use a BASIC calculator for all math
More informationCHAPTER 3 COMMONLY USED STATISTICAL TERMS
CHAPTER 3 COMMONLY USED STATISTICAL TERMS There are many statistics used in social science research and evaluation. The two main areas of statistics are descriptive and inferential. The third class of
More informationMultivariate Statistical Inference and Applications
Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A WileyInterscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim
More informationAnalysis of Variance. MINITAB User s Guide 2 31
3 Analysis of Variance Analysis of Variance Overview, 32 OneWay Analysis of Variance, 35 TwoWay Analysis of Variance, 311 Analysis of Means, 313 Overview of Balanced ANOVA and GLM, 318 Balanced
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 informationAP Statistics: Syllabus 3
AP Statistics: Syllabus 3 Scoring Components SC1 The course provides instruction in exploring data. 4 SC2 The course provides instruction in sampling. 5 SC3 The course provides instruction in experimentation.
More informationGCSE HIGHER Statistics Key Facts
GCSE HIGHER Statistics Key Facts Collecting Data When writing questions for questionnaires, always ensure that: 1. the question is worded so that it will allow the recipient to give you the information
More informationHYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION
HYPOTHESIS TESTING: CONFIDENCE INTERVALS, TTESTS, ANOVAS, AND REGRESSION HOD 2990 10 November 2010 Lecture Background This is a lightning speed summary of introductory statistical methods for senior undergraduate
More informationInferential Statistics
Inferential Statistics Sampling and the normal distribution Zscores Confidence levels and intervals Hypothesis testing Commonly used statistical methods Inferential Statistics Descriptive statistics are
More informationA Bayesian hierarchical surrogate outcome model for multiple sclerosis
A Bayesian hierarchical surrogate outcome model for multiple sclerosis 3 rd Annual ASA New Jersey Chapter / Bayer Statistics Workshop David Ohlssen (Novartis), Luca Pozzi and Heinz Schmidli (Novartis)
More informationNCSS Statistical Software
Chapter 06 Introduction This procedure provides several reports for the comparison of two distributions, including confidence intervals for the difference in means, twosample ttests, the ztest, the
More informationAdvanced Algebra 2. I. Equations and Inequalities
Advanced Algebra 2 I. Equations and Inequalities A. Real Numbers and Number Operations 6.A.5, 6.B.5, 7.C.5 1) Graph numbers on a number line 2) Order real numbers 3) Identify properties of real numbers
More informationMAT140: Applied Statistical Methods Summary of Calculating Confidence Intervals and Sample Sizes for Estimating Parameters
MAT140: Applied Statistical Methods Summary of Calculating Confidence Intervals and Sample Sizes for Estimating Parameters Inferences about a population parameter can be made using sample statistics for
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 informationMATH BOOK OF PROBLEMS SERIES. New from Pearson Custom Publishing!
MATH BOOK OF PROBLEMS SERIES New from Pearson Custom Publishing! The Math Book of Problems Series is a database of math problems for the following courses: Prealgebra Algebra Precalculus Calculus Statistics
More informationConfidence Intervals for One Standard Deviation Using Standard Deviation
Chapter 640 Confidence Intervals for One Standard Deviation Using Standard Deviation Introduction This routine calculates the sample size necessary to achieve a specified interval width or distance from
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 information