Flexible Imputation of Missing Data

Size: px
Start display at page:

Download "Flexible Imputation of Missing Data"

Transcription

1 Chapman & Hall/CRC Interdisciplinary Statistics Series Flexible Imputation of Missing Data Stef van Buuren TNO Leiden, The Netherlands University of Utrecht The Netherlands crc pness Taylor &l Francis Croup Boca Raton London New York CRC Press is an imprint of the Taylor & Francis an Group, Informa business A CHAPMAN St HALL BOOK

2 Contents Foreword xvii Preface xix About the Author xxi Symbol Description xxiii List of Algorithms xxv I Basics 1 1 Introduction The problem of missing data Current practice Changing perspective on missing data Concepts of MCAR, MAR and MNAR Simple solutions that do not (always) work Listwise deletion Pairwise deletion Mean imputation Regression imputation Stochastic regression imputation LOCF and BOFC Indicator method Summary Multiple imputation in a nutshell Procedure Reasons to use multiple imputation Example of multiple imputation Goal of the book What the book does not cover Prevention Weighting procedures Likelihood-based approaches Structure of the book Exercises 23 ix

3 .. ' X Contents 2 Multiple imputation 2.1 Historic overview Imputation Multiple imputation V The expanding literature on multiple imputation 2.2 Concepts in incomplete data Incomplete data perspective Causes of missing data Notation 2.2,1 MCAR. MAR and MNAR again Ignorable and nonignorable * Implications of ignorability 2.3 Why and when multiple imputation works Goal of multiple imputation Three sources of variation * Proper imputation Scope of the imputation model Variance ratios * 2.3.G * Degrees of freedom Numerical example 2.4 Statistical intervals and tests Scalar or multi-parameter inference? Scalar inference 2.5 Evaluation criteria Imputation is not prediction Simulation designs and performance measures 2.G When to use multiple imputation 2.7 How many imputations? 2.8 Ext rcises 3 Univariate missing data 3.1 How to generate multiple imputations Predict method Predict + noise method Predict + noise + parameter uncertainty A second predictor Drawing from the observed data 3.1.G Conclusion 3.2 Imputation under the normal linear normal Overview Algorithms * Performance Generating MAR missing data Conclusion 3.3 Imputation under non-normal distributions

4 Contents xi Overview Imputation from the t-distribution * Example * Predictive mean matching Overview Computational details * Algorithm * Conclusion Categorical data Overview Perfect prediction * Other data types Count data Semi-continuous data Censored, truncated and rounded data Classification and regression trees Overview Imputation using CART models Multilevel data Overview Two formulations of the linear multilevel model * Computation * Conclusion Nonignorable missing data Overview Selection model Pattern-mixture model Converting selection and pattern-mixture models Sensitivity analysis Role of sensitivity analysis Exercises 93 4 Multivariate missing data Missing data pattern Overview Summary statistics Influx and outflux Issues in multivariate imputation Monotone data imputation Overview Algorithm Joint modeling Overview Continuous data * Categorical data 107

5 xii Contents 4.5 Fully conditional specification Overview The MICE algorithm Performance Ill Compatibility * Ill Number of iterations Example of slow convergence FCS and,1m Relations between FCS and JM Comparison Illustration Conclusion Exercises Imputation in practice Overview of modeling choices lgnorable or nonignorable? Model form and predictors Model form Predictors Derived variables Ratio of two variables Sum scores Interaction terms Conditional imputation Compositional data * Quadratic relations * Algorithmic options Visit sequence Convergence Diagnostics Model fit versus distributional discrepancy Diagnostic graphs Conclusion Exercises Analysis of imputed data What to do with the imputed data? Averaging and stacking the data Repeated analyses Parameter pooling Scalar inference of normal quantities Scalar inference of non-normal quantities Statistical tests for multiple imputation Wald test * 157

6 Contents xiii Likelihood ratio test * x2-test * Custom hypothesis tests of model parameters * Computation Stepwise model selection Variable selection techniques Computation Model optimism Conclusion Exercises 166 II Case studies Measurement issues Too many columns Scientific question Leiden 85+Cohort Data exploration Outflux Logged events Quick predictor selection for wide data Generating the imputations A further improvement: Survival as predictor variable Some guidance Sensitivity analysis Causes and consequences of missing data Scenarios Generating imputations under the ^-adjustment Complete data analysis Conclusion Correct prevalence estimates from self-reported data Description of the problem Don't count on predictions The main idea Data Application Conclusion Enhancing comparability Description of the problem Pull dependence: Simple equating Independence: Imputation without 196 a bridge study Fully dependent or independent? Imputation using a bridge study Interpretation Conclusion 203

7 xiv Contents 7.5 Exercises Selection issues Correcting for selective drop-out POPS study; 19 years follow-up Characterization of the drop-out Imputation model A degenerate solution A better solution Results Conclusion Correcting for nonresponse Fifth Dutch Growth Study Nonresponse Comparison to known population totals Augmenting the sample Imputation model Influence of nonresponse on final height Discussion Exercises 219 ( Longitudinal data Long and wide format SE Fireworks Disaster Study Intention to treat Imputation model Inspecting imputations Complete data analysis Results from the complete data analysis Time raster imputation Change score Scientific question: Critical periods Broken stick model * Terneuzen Birth Cohort Shrinkage and the change score * Imputation Complete data analysis Conclusion Exercises 244 III Extensions 247

8 Contents *v 10 Conclusion Some dangers, some do's and some don'ts Some dangers Some do's Some don'ts Reporting Reporting guidelines Template Other applications Synthetic datasets for data protection Imputation of potential outcomes Analysis of coarsened data File matching of multiple datasets Planned missing data for efficient designs Adjusting for verification bias Correcting for measurement error Future developments Derived variables Convergence of MICE algorithm Algorithms for blocks and batches Parallel computation Nested imputation Machine learning for imputation Incorporating expert knowledge Distribution-free pooling rules Improved diagnostic techniques Building block in modular statistics Exercises 260 A Software 263 A.J H 263 A.2 S-PLUS 265 A.3 Stata 265 A.4 SAS 266 A.5 SPSS 266 A.6 Other software 266 References 269 Author Index 299 Subject Index 307

Missing Data Methods (Part I): Multiple Imputation. Advanced Multivariate Statistical Methods Workshop

Missing Data Methods (Part I): Multiple Imputation. Advanced Multivariate Statistical Methods Workshop Missing Data Methods (Part I): Multiple Imputation Advanced Multivariate Statistical Methods Workshop University of Georgia: Institute for Interdisciplinary Research in Education and Human Development

More information

Multiple Imputation for Missing Data in KLoSA

Multiple Imputation for Missing Data in KLoSA Multiple Imputation for Missing Data in KLoSA Juwon Song Korea University and UCLA Contents 1. Missing Data and Missing Data Mechanisms 2. Imputation 3. Missing Data and Multiple Imputation in Baseline

More information

RELATIVE EFFICIENCY OF ESTIMATES BASED ON PERCENTAGES OF MISSINGNESS USING THREE IMPUTATION NUMBERS IN MULTIPLE IMPUTATION ANALYSIS ABSTRACT

RELATIVE EFFICIENCY OF ESTIMATES BASED ON PERCENTAGES OF MISSINGNESS USING THREE IMPUTATION NUMBERS IN MULTIPLE IMPUTATION ANALYSIS ABSTRACT RELATIVE EFFICIENCY OF ESTIMATES BASED ON PERCENTAGES OF MISSINGNESS USING THREE IMPUTATION NUMBERS IN MULTIPLE IMPUTATION ANALYSIS Nwakuya, M. T. (Ph.D) Department of Mathematics/Statistics University

More information

Handling Missing Data. Ashley Parker EDU 7312

Handling Missing Data. Ashley Parker EDU 7312 Handling Missing Data Ashley Parker EDU 7312 Presentation Outline Types of Missing Data Treatments for Handling Missing Data Deletion Techniques Listwise Deletion Pairwise Deletion Single Imputation Techniques

More information

Missing value imputation in SAS: an intro to Proc MI and MIANALYZE

Missing value imputation in SAS: an intro to Proc MI and MIANALYZE Victoria SAS Users Group November 26, 2013 Missing value imputation in SAS: an intro to Proc MI and MIANALYZE Sylvain Tremblay SAS Canada Education Copyright 2010 SAS Institute Inc. All rights reserved.

More information

Missing Data Treatments

Missing Data Treatments Missing Data Treatments Lindsey Perry EDU7312: Spring 2012 Presentation Outline Types of Missing Data Listwise Deletion Pairwise Deletion Single Imputation Methods Mean Imputation Hot Deck Imputation Multiple

More information

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and private study only. The thesis may not be reproduced elsewhere

More information

Missing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS. Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13

Missing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS. Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13 Missing Data: Part 2 Implementing Multiple Imputation in STATA and SPSS Carol B. Thompson Johns Hopkins Biostatistics Center SON Brown Bag 4/24/13 Overview Reminder Steps in Multiple Imputation Implementation

More information

Method for the imputation of the earnings variable in the Belgian LFS

Method for the imputation of the earnings variable in the Belgian LFS Method for the imputation of the earnings variable in the Belgian LFS Workshop on LFS methodology, Madrid 2012, May 10-11 Astrid Depickere, Anja Termote, Pieter Vermeulen Outline 1. Introduction 2. Imputation

More information

Imputation of multivariate continuous data with non-ignorable missingness

Imputation of multivariate continuous data with non-ignorable missingness Imputation of multivariate continuous data with non-ignorable missingness Thais Paiva Jerry Reiter Department of Statistical Science Duke University NCRN Meeting Spring 2014 May 23, 2014 Thais Paiva, Jerry

More information

Missing data in political science

Missing data in political science SOC 597A Seminar in survey research Final paper Missing data in political science Claudiu Tufis December 10, 2003 Abstract In this paper I analyze a series of techniques designed for replacing missing

More information

Chained equations and more in multiple imputation in Stata 12

Chained equations and more in multiple imputation in Stata 12 Chained equations and more in multiple imputation in Stata 12 Yulia Marchenko Associate Director, Biostatistics StataCorp LP 2011 UK Stata Users Group Meeting Yulia Marchenko (StataCorp) September 16,

More information

Decision making with incomplete information Some new developments. Rudolf Vetschera University of Vienna. Tamkang University May 15, 2017

Decision making with incomplete information Some new developments. Rudolf Vetschera University of Vienna. Tamkang University May 15, 2017 Decision making with incomplete information Some new developments Rudolf Vetschera University of Vienna Tamkang University May 15, 2017 Agenda Problem description Overview of methods Single parameter approaches

More information

Imputation Procedures for Missing Data in Clinical Research

Imputation Procedures for Missing Data in Clinical Research Imputation Procedures for Missing Data in Clinical Research Appendix B Overview The MATRICS Consensus Cognitive Battery (MCCB), building on the foundation of the Measurement and Treatment Research to Improve

More information

A Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation

A Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation A Comparison of Approximate Bayesian Bootstrap and Weighted Sequential Hot Deck for Multiple Imputation Darryl V. Creel RTI International 1 RTI International is a trade name of Research Triangle Institute.

More information

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts When you need to understand situations that seem to defy data analysis, you may be able to use techniques

More information

Predicting Wine Quality

Predicting Wine Quality March 8, 2016 Ilker Karakasoglu Predicting Wine Quality Problem description: You have been retained as a statistical consultant for a wine co-operative, and have been asked to analyze these data. Each

More information

Modeling Wine Quality Using Classification and Regression. Mario Wijaya MGT 8803 November 28, 2017

Modeling Wine Quality Using Classification and Regression. Mario Wijaya MGT 8803 November 28, 2017 Modeling Wine Quality Using Classification and Mario Wijaya MGT 8803 November 28, 2017 Motivation 1 Quality How to assess it? What makes a good quality wine? Good or Bad Wine? Subjective? Wine taster Who

More information

Gail E. Potter, Timo Smieszek, and Kerstin Sailer. April 24, 2015

Gail E. Potter, Timo Smieszek, and Kerstin Sailer. April 24, 2015 Supplementary Material to Modelling workplace contact networks: the effects of organizational structure, architecture, and reporting errors on epidemic predictions, published in Network Science Gail E.

More information

MBA 503 Final Project Guidelines and Rubric

MBA 503 Final Project Guidelines and Rubric MBA 503 Final Project Guidelines and Rubric Overview There are two summative assessments for this course. For your first assessment, you will be objectively assessed by your completion of a series of MyAccountingLab

More information

Online Appendix to. Are Two heads Better Than One: Team versus Individual Play in Signaling Games. David C. Cooper and John H.

Online Appendix to. Are Two heads Better Than One: Team versus Individual Play in Signaling Games. David C. Cooper and John H. Online Appendix to Are Two heads Better Than One: Team versus Individual Play in Signaling Games David C. Cooper and John H. Kagel This appendix contains a discussion of the robustness of the regression

More information

Missing Data Imputation Method Comparison in Ohio University Student Retention. Database. A thesis presented to. the faculty of

Missing Data Imputation Method Comparison in Ohio University Student Retention. Database. A thesis presented to. the faculty of Missing Data Imputation Method Comparison in Ohio University Student Retention Database A thesis presented to the faculty of the Russ College of Engineering and Technology of Ohio University In partial

More information

You know what you like, but what about everyone else? A Case study on Incomplete Block Segmentation of white-bread consumers.

You know what you like, but what about everyone else? A Case study on Incomplete Block Segmentation of white-bread consumers. You know what you like, but what about everyone else? A Case study on Incomplete Block Segmentation of white-bread consumers. Abstract One man s meat is another man s poison. There will always be a wide

More information

Wine Rating Prediction

Wine Rating Prediction CS 229 FALL 2017 1 Wine Rating Prediction Ke Xu (kexu@), Xixi Wang(xixiwang@) Abstract In this project, we want to predict rating points of wines based on the historical reviews from experts. The wine

More information

The R survey package used in these examples is version 3.22 and was run under R v2.7 on a PC.

The R survey package used in these examples is version 3.22 and was run under R v2.7 on a PC. CHAPTER 7 ANALYSIS EXAMPLES REPLICATION-R SURVEY PACKAGE 3.22 GENERAL NOTES ABOUT ANALYSIS EXAMPLES REPLICATION These examples are intended to provide guidance on how to use the commands/procedures for

More information

Activity 10. Coffee Break. Introduction. Equipment Required. Collecting the Data

Activity 10. Coffee Break. Introduction. Equipment Required. Collecting the Data . Activity 10 Coffee Break Economists often use math to analyze growth trends for a company. Based on past performance, a mathematical equation or formula can sometimes be developed to help make predictions

More information

This module is part of the. Memobust Handbook. on Methodology of Modern Business Statistics

This module is part of the. Memobust Handbook. on Methodology of Modern Business Statistics This module is part of the Memobust Handbook on Methodology of Modern Business Statistics 26 March 2014 Theme: Imputation Main Module Contents General section... 3 1. Summary... 3 2. General description...

More information

Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches

Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches Return to wine: A comparison of the hedonic, repeat sales, and hybrid approaches James J. Fogarty a* and Callum Jones b a School of Agricultural and Resource Economics, The University of Western Australia,

More information

wine 1 wine 2 wine 3 person person person person person

wine 1 wine 2 wine 3 person person person person person 1. A trendy wine bar set up an experiment to evaluate the quality of 3 different wines. Five fine connoisseurs of wine were asked to taste each of the wine and give it a rating between 0 and 10. The order

More information

Relation between Grape Wine Quality and Related Physicochemical Indexes

Relation between Grape Wine Quality and Related Physicochemical Indexes Research Journal of Applied Sciences, Engineering and Technology 5(4): 557-5577, 013 ISSN: 040-7459; e-issn: 040-7467 Maxwell Scientific Organization, 013 Submitted: October 1, 01 Accepted: December 03,

More information

AGREEMENT n LLP-LDV-TOI-10-IT-538 UNITS FRAMEWORK ABOUT THE MAITRE QUALIFICATION

AGREEMENT n LLP-LDV-TOI-10-IT-538 UNITS FRAMEWORK ABOUT THE MAITRE QUALIFICATION Transparency for Mobility in Tourism: transfer and making system of methods and instruments to improve the assessment, validation and recognition of learning outcomes and the transparency of qualifications

More information

Comparing R print-outs from LM, GLM, LMM and GLMM

Comparing R print-outs from LM, GLM, LMM and GLMM 3. Inference: interpretation of results, plotting results, confidence intervals, hypothesis tests (Wald,LRT). 4. Asymptotic distribution of maximum likelihood estimators and tests. 5. Checking the adequacy

More information

Analysis of Things (AoT)

Analysis of Things (AoT) Analysis of Things (AoT) Big Data & Machine Learning Applied to Brent Crude Executive Summary Data Selecting & Visualising Data We select historical, monthly, fundamental data We check for correlations

More information

From VOC to IPA: This Beer s For You!

From VOC to IPA: This Beer s For You! From VOC to IPA: This Beer s For You! Joel Smith Statistician Minitab Inc. jsmith@minitab.com 2013 Minitab, Inc. Image courtesy of amazon.com The Data Online beer reviews Evaluated overall and: Appearance

More information

The multivariate piecewise linear growth model for ZHeight and zbmi can be expressed as:

The multivariate piecewise linear growth model for ZHeight and zbmi can be expressed as: Bi-directional relationships between body mass index and height from three to seven years of age: an analysis of children in the United Kingdom Millennium Cohort Study Supplementary material The multivariate

More information

Much ado about nothing: methods and implementations to estim. regression models

Much ado about nothing: methods and implementations to estim. regression models : methods and implementations to estimate incomplete data regression models Smith College, Northampton, MA, USA and University of Auckland, New Zealand December 6, 2007, Australasian Biometrics Conference

More information

Evaluating a harvest control rule of the NEA cod considering capelin

Evaluating a harvest control rule of the NEA cod considering capelin The 17th Russian Norwegian Symposium Long term sustainable management of living marine resources in the Northern Seas Bergen, March 2016 Evaluating a harvest control rule of the NEA cod considering capelin

More information

Evaluation of Alternative Imputation Methods for 2017 Economic Census Products 1 Jeremy Knutson and Jared Martin

Evaluation of Alternative Imputation Methods for 2017 Economic Census Products 1 Jeremy Knutson and Jared Martin Evaluation of Alternative Imputation Methods for 2017 Economic Census Products 1 Jeremy Knutson and Jared Martin Abstract In preparation for the 2017 change to the North American Product Classification

More information

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials

1. Continuing the development and validation of mobile sensors. 3. Identifying and establishing variable rate management field trials Project Overview The overall goal of this project is to deliver the tools, techniques, and information for spatial data driven variable rate management in commercial vineyards. Identified 2016 Needs: 1.

More information

PSYC 6140 November 16, 2005 ANOVA output in R

PSYC 6140 November 16, 2005 ANOVA output in R PSYC 6140 November 16, 2005 ANOVA output in R Type I, Type II and Type III Sums of Squares are displayed in ANOVA tables in a mumber of packages. The car library in R makes these available in R. This handout

More information

Improving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data

Improving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data Improving Capacity for Crime Repor3ng: Data Quality and Imputa3on Methods Using State Incident- Based Repor3ng System Data July 31, 2014 Justice Research and Statistics Association 720 7th Street, NW,

More information

STACKING CUPS STEM CATEGORY TOPIC OVERVIEW STEM LESSON FOCUS OBJECTIVES MATERIALS. Math. Linear Equations

STACKING CUPS STEM CATEGORY TOPIC OVERVIEW STEM LESSON FOCUS OBJECTIVES MATERIALS. Math. Linear Equations STACKING CUPS STEM CATEGORY Math TOPIC Linear Equations OVERVIEW Students will work in small groups to stack Solo cups vs. Styrofoam cups to see how many of each it takes for the two stacks to be equal.

More information

To: Professor Roger Bohn & Hyeonsu Kang Subject: Big Data, Assignment April 13th. From: xxxx (anonymized) Date: 4/11/2016

To: Professor Roger Bohn & Hyeonsu Kang Subject: Big Data, Assignment April 13th. From: xxxx (anonymized) Date: 4/11/2016 To: Professor Roger Bohn & Hyeonsu Kang Subject: Big Data, Assignment April 13th. From: xxxx (anonymized) Date: 4/11/2016 Data Preparation: 1. Separate trany variable into Manual which takes value of 1

More information

Improving the safety and quality of nuts

Improving the safety and quality of nuts Woodhead Publishing Series in Food Science, Technology and Nutrition: Number 250 Improving the safety and quality of nuts Edited by Linda J. Harris WP WOODHEAD PUBLISHING Oxford Cambridge Philadelphia

More information

Final Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006

Final Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006 Dr. Roland Füss Winter Term 2005/2006 Final Exam Financial Data Analysis (6 Credit points/imp Students) March 2, 2006 Note the following important information: 1. The total disposal time is 60 minutes.

More information

Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data

Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data University of Massachusetts Amherst From the SelectedWorks of Daiheng Ni March 1, 2005 Multiple Imputation Scheme for Overcoming the Missing Values and Variability Issues in ITS Data Daiheng Ni, University

More information

Structures of Life. Investigation 1: Origin of Seeds. Big Question: 3 rd Science Notebook. Name:

Structures of Life. Investigation 1: Origin of Seeds. Big Question: 3 rd Science Notebook. Name: 3 rd Science Notebook Structures of Life Investigation 1: Origin of Seeds Name: Big Question: What are the properties of seeds and how does water affect them? 1 Alignment with New York State Science Standards

More information

The Development of a Weather-based Crop Disaster Program

The Development of a Weather-based Crop Disaster Program The Development of a Weather-based Crop Disaster Program Eric Belasco Montana State University 2016 SCC-76 Conference Pensacola, FL March 19, 2016. Belasco March 2016 1 / 18 Motivation Recent efforts to

More information

Please sign and date here to indicate that you have read and agree to abide by the above mentioned stipulations. Student Name #4

Please sign and date here to indicate that you have read and agree to abide by the above mentioned stipulations. Student Name #4 The following group project is to be worked on by no more than four students. You may use any materials you think may be useful in solving the problems but you may not ask anyone for help other than the

More information

Buying Filberts On a Sample Basis

Buying Filberts On a Sample Basis E 55 m ^7q Buying Filberts On a Sample Basis Special Report 279 September 1969 Cooperative Extension Service c, 789/0 ite IP") 0, i mi 1910 S R e, `g,,ttsoliktill:torvti EARs srin ITQ, E,6

More information

Predictors of Repeat Winery Visitation in North Carolina

Predictors of Repeat Winery Visitation in North Carolina University of Massachusetts Amherst ScholarWorks@UMass Amherst Tourism Travel and Research Association: Advancing Tourism Research Globally 2013 ttra International Conference Predictors of Repeat Winery

More information

Not to be published - available as an online Appendix only! 1.1 Discussion of Effects of Control Variables

Not to be published - available as an online Appendix only! 1.1 Discussion of Effects of Control Variables 1 Appendix Not to be published - available as an online Appendix only! 1.1 Discussion of Effects of Control Variables Table 1 in the main text includes a number of additional control variables. We find

More information

Computerized Models for Shelf Life Prediction of Post-Harvest Coffee Sterilized Milk Drink

Computerized Models for Shelf Life Prediction of Post-Harvest Coffee Sterilized Milk Drink Libyan Agriculture esearch Center Journal International (6): 74-78, 011 ISSN 19-4304 IDOSI Publications, 011 Computerized Models for Shelf Life Prediction of Post-Harvest Coffee Sterilized Milk Drink 1

More information

STAT 5302 Applied Regression Analysis. Hawkins

STAT 5302 Applied Regression Analysis. Hawkins Homework 3 sample solution 1. MinnLand data STAT 5302 Applied Regression Analysis. Hawkins newdata

More information

7 th Annual Conference AAWE, Stellenbosch, Jun 2013

7 th Annual Conference AAWE, Stellenbosch, Jun 2013 The Impact of the Legal System and Incomplete Contracts on Grape Sourcing Strategies: A Comparative Analysis of the South African and New Zealand Wine Industries * Corresponding Author Monnane, M. Monnane,

More information

Analyzing Human Impacts on Population Dynamics Outdoor Lab Activity Biology

Analyzing Human Impacts on Population Dynamics Outdoor Lab Activity Biology Human Impact on Ecosystems and Dynamics: Common Assignment 1 Dynamics Lab Report Analyzing Human Impacts on Dynamics Outdoor Lab Activity Biology Introduction The populations of various organisms in an

More information

UNIT TITLE: TAKE FOOD ORDERS AND PROVIDE TABLE SERVICE NOMINAL HOURS: 80

UNIT TITLE: TAKE FOOD ORDERS AND PROVIDE TABLE SERVICE NOMINAL HOURS: 80 UNIT TITLE: TAKE FOOD ORDERS AND PROVIDE TABLE SERVICE NOMINAL HOURS: 80 UNIT NUMBER: D1.HBS.CL5.16 UNIT DESCRIPTOR: This unit deals with the skills and knowledge required to take food orders and provide

More information

The premium for organic wines

The premium for organic wines Enometrics XV Collioure May 29-31, 2008 Estimating a hedonic price equation from the producer side Points of interest: - assessing whether there is a premium for organic wines, and which one - estimating

More information

A study on consumer perception about soft drink products

A study on consumer perception about soft drink products A study on consumer perception about soft drink products Dr.S.G.Parekh Assistant Professor, Faculty of Business Administration, Dharmsinh Desai University, Nadiad, Gujarat, India Email: sg_parekh@yahoo.com

More information

THE STATISTICAL SOMMELIER

THE STATISTICAL SOMMELIER THE STATISTICAL SOMMELIER An Introduction to Linear Regression 15.071 The Analytics Edge Bordeaux Wine Large differences in price and quality between years, although wine is produced in a similar way Meant

More information

An application of cumulative prospect theory to travel time variability

An application of cumulative prospect theory to travel time variability Katrine Hjorth (DTU) Stefan Flügel, Farideh Ramjerdi (TØI) An application of cumulative prospect theory to travel time variability Sixth workshop on discrete choice models at EPFL August 19-21, 2010 Page

More information

Archival copy. For current information, see the OSU Extension Catalog: https://catalog.extension.oregonstate.edu/em9070

Archival copy. For current information, see the OSU Extension Catalog: https://catalog.extension.oregonstate.edu/em9070 EM 9070 June 2013 How to Measure Grapevine Leaf Area Patricia A. Skinkis and R. Paul Schreiner Figure 1. A leaf area template can be easily made using typical office supplies. The template, above, is being

More information

Biocides IT training Vienna - 4 December 2017 IUCLID 6

Biocides IT training Vienna - 4 December 2017 IUCLID 6 Biocides IT training Vienna - 4 December 2017 IUCLID 6 Biocides IUCLID training 2 (18) Creation and update of a Biocidal Product Authorisation dossier and use of the report generator Background information

More information

Mobility tools and use: Accessibility s role in Switzerland

Mobility tools and use: Accessibility s role in Switzerland Mobility tools and use: Accessibility s role in Switzerland A Loder IVT ETH Brisbane, July 2017 In Swiss cities, public transport is competitive if not advantageous. 22 min 16-26 min 16-28 min 2 And between

More information

Using Growing Degree Hours Accumulated Thirty Days after Bloom to Help Growers Predict Difficult Fruit Sizing Years

Using Growing Degree Hours Accumulated Thirty Days after Bloom to Help Growers Predict Difficult Fruit Sizing Years Using Growing Degree Hours Accumulated Thirty Days after Bloom to Help Growers Predict Difficult Fruit Sizing Years G. Lopez 1 and T. DeJong 2 1 Àrea de Tecnologia del Reg, IRTA, Lleida, Spain 2 Department

More information

Temperature effect on pollen germination/tube growth in apple pistils

Temperature effect on pollen germination/tube growth in apple pistils FINAL PROJECT REPORT Project Title: Temperature effect on pollen germination/tube growth in apple pistils PI: Dr. Keith Yoder Co-PI(): Dr. Rongcai Yuan Organization: Va. Tech Organization: Va. Tech Telephone/email:

More information

Appendix A. Table A.1: Logit Estimates for Elasticities

Appendix A. Table A.1: Logit Estimates for Elasticities Estimates from historical sales data Appendix A Table A.1. reports the estimates from the discrete choice model for the historical sales data. Table A.1: Logit Estimates for Elasticities Dependent Variable:

More information

IT 403 Project Beer Advocate Analysis

IT 403 Project Beer Advocate Analysis 1. Exploratory Data Analysis (EDA) IT 403 Project Beer Advocate Analysis Beer Advocate is a membership-based reviews website where members rank different beers based on a wide number of categories. The

More information

Business Statistics /82 Spring 2011 Booth School of Business The University of Chicago Final Exam

Business Statistics /82 Spring 2011 Booth School of Business The University of Chicago Final Exam Business Statistics 41000-81/82 Spring 2011 Booth School of Business The University of Chicago Final Exam Name You may use a calculator and two cheat sheets. You have 3 hours. I pledge my honor that I

More information

Summary of Main Points

Summary of Main Points 1 Model Selection in Logistic Regression Summary of Main Points Recall that the two main objectives of regression modeling are: Estimate the effect of one or more covariates while adjusting for the possible

More information

Alcoholic Fermentation in Yeast A Bioengineering Design Challenge 1

Alcoholic Fermentation in Yeast A Bioengineering Design Challenge 1 Alcoholic Fermentation in Yeast A Bioengineering Design Challenge 1 I. Introduction Yeasts are single cell fungi. People use yeast to make bread, wine and beer. For your experiment, you will use the little

More information

Regression Models for Saffron Yields in Iran

Regression Models for Saffron Yields in Iran Regression Models for Saffron ields in Iran Sanaeinejad, S.H., Hosseini, S.N 1 Faculty of Agriculture, Ferdowsi University of Mashhad, Iran sanaei_h@yahoo.co.uk, nasir_nbm@yahoo.com, Abstract: Saffron

More information

SLO Presentation. Cerritos College. CA Date: 09/13/2018

SLO Presentation. Cerritos College. CA Date: 09/13/2018 CA Date: 09/13/2018 HEALTH OCCUPATIONS CA Professional Baking and Pastries--AS Students apply the proper baking and pastry techniques and procedures to produce quality products. Students define basic baking

More information

Diploma in Hospitality Management (610) Food and Beverage Management

Diploma in Hospitality Management (610) Food and Beverage Management Diploma in Hospitality Management (610) Food and Beverage Management Pre-requisites: Knowledge of business Co-requisites: A pass or higher in Certificate in organisation. Business Studies or equivalence.

More information

Problem Set #3 Key. Forecasting

Problem Set #3 Key. Forecasting Problem Set #3 Key Sonoma State University Business 581E Dr. Cuellar The data set bus581e_ps3.dta is a Stata data set containing annual sales (cases) and revenue from December 18, 2004 to April 2 2011.

More information

What makes a good muffin? Ivan Ivanov. CS229 Final Project

What makes a good muffin? Ivan Ivanov. CS229 Final Project What makes a good muffin? Ivan Ivanov CS229 Final Project Introduction Today most cooking projects start off by consulting the Internet for recipes. A quick search for chocolate chip muffins returns a

More information

Comparison of Multivariate Data Representations: Three Eyes are Better than One

Comparison of Multivariate Data Representations: Three Eyes are Better than One Comparison of Multivariate Data Representations: Three Eyes are Better than One Natsuhiko Kumasaka (Keio University) Antony Unwin (Augsburg University) Content Visualisation of multivariate data Parallel

More information

UNIT TITLE: PROVIDE ADVICE TO PATRONS ON FOOD AND BEVERAGE SERVICES NOMINAL HOURS: 80

UNIT TITLE: PROVIDE ADVICE TO PATRONS ON FOOD AND BEVERAGE SERVICES NOMINAL HOURS: 80 UNIT TITLE: PROVIDE ADVICE TO PATRONS ON FOOD AND BEVERAGE SERVICES NOMINAL HOURS: 80 UNIT NUMBER: D1.HBS.CL5.10 UNIT DESCRIPTOR: This unit deals with the skills and knowledge required to provide advice

More information

UNIT TITLE: PREPARE AND PRESENT GATEAUX, TORTEN AND CAKES NOMINAL HOURS: 60

UNIT TITLE: PREPARE AND PRESENT GATEAUX, TORTEN AND CAKES NOMINAL HOURS: 60 UNIT TITLE: PREPARE AND PRESENT GATEAUX, TORTEN AND CAKES NOMINAL HOURS: 60 UNIT NUMBER: D1.HPA.CL4.07 UNIT DESCRIPTOR: This unit deals with skills and knowledge required by cooks, chefs and patissiers

More information

UNIT TITLE: PLAN, PREPARE AND DISPLAY A BUFFET SERVICE NOMINAL HOURS: 45

UNIT TITLE: PLAN, PREPARE AND DISPLAY A BUFFET SERVICE NOMINAL HOURS: 45 UNIT TITLE: PLAN, PREPARE AND DISPLAY A BUFFET SERVICE NOMINAL HOURS: 45 UNIT NUMBER: D1.HCC.CL2.07 UNIT DESCRIPTOR: This unit deals with skills and knowledge required by cooks and chefs to plan, prepare,

More information

Statistics: Final Project Report Chipotle Water Cup: Water or Soda?

Statistics: Final Project Report Chipotle Water Cup: Water or Soda? Statistics: Final Project Report Chipotle Water Cup: Water or Soda? Introduction: For our experiment, we wanted to find out how many customers at Chipotle actually get water when they order a water cup.

More information

Curtis Miller MATH 3080 Final Project pg. 1. The first question asks for an analysis on car data. The data was collected from the Kelly

Curtis Miller MATH 3080 Final Project pg. 1. The first question asks for an analysis on car data. The data was collected from the Kelly Curtis Miller MATH 3080 Final Project pg. 1 Curtis Miller 4/10/14 MATH 3080 Final Project Problem 1: Car Data The first question asks for an analysis on car data. The data was collected from the Kelly

More information

Statistics & Agric.Economics Deptt., Tocklai Experimental Station, Tea Research Association, Jorhat , Assam. ABSTRACT

Statistics & Agric.Economics Deptt., Tocklai Experimental Station, Tea Research Association, Jorhat , Assam. ABSTRACT Two and a Bud 59(2):152-156, 2012 RESEARCH PAPER Global tea production and export trend with special reference to India Prasanna Kumar Bordoloi Statistics & Agric.Economics Deptt., Tocklai Experimental

More information

Flexible Working Arrangements, Collaboration, ICT and Innovation

Flexible Working Arrangements, Collaboration, ICT and Innovation Flexible Working Arrangements, Collaboration, ICT and Innovation A Panel Data Analysis Cristian Rotaru and Franklin Soriano Analytical Services Unit Economic Measurement Group (EMG) Workshop, Sydney 28-29

More information

Evaluation of FY2E Reprocessed AMVs IN GRAPES. Wei Han, Xiaomin Wan and Jiandong Gong NWP/CMA

Evaluation of FY2E Reprocessed AMVs IN GRAPES. Wei Han, Xiaomin Wan and Jiandong Gong NWP/CMA Evaluation of FY2E Reprocessed AMVs IN GRAPES Wei Han, Xiaomin Wan and Jiandong Gong NWP/CMA IWW13, June 2016 Outline l FY2E reprocessed AMVs l Observation Error and Number l Experiments in GRAPES global

More information

Forecasting the Value of Fine Wines

Forecasting the Value of Fine Wines Paper 1829-2018 Forecasting the Value of Fine Wines Joseph L. Breeden, auctionforecast.com ABSTRACT Fine wines have gained attention globally as an investment opportunity with possible diversification

More information

Relationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good

Relationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good Relationships Among Wine Prices, Ratings, Advertising, and Production: Examining a Giffen Good Carol Miu Massachusetts Institute of Technology Abstract It has become increasingly popular for statistics

More information

Web Appendix to Identifying Sibling Inuence on Teenage Substance Use. Joseph G. Altonji, Sarah Cattan, and Iain Ware

Web Appendix to Identifying Sibling Inuence on Teenage Substance Use. Joseph G. Altonji, Sarah Cattan, and Iain Ware Web Appendix to Identifying Sibling Inuence on Teenage Substance Use Joseph G. Altonji, Sarah Cattan, and Iain Ware A Data The paper uses data from the rst eight rounds of the National Longitudinal Survey

More information

Archdiocese of New York Practice Items

Archdiocese of New York Practice Items Archdiocese of New York Practice Items Mathematics Grade 8 Teacher Sample Packet Unit 1 NY MATH_TE_G8_U1.indd 1 NY MATH_TE_G8_U1.indd 2 1. Which choice is equivalent to 52 5 4? A 1 5 4 B 25 1 C 2 1 D 25

More information

UNIT TITLE: PREPARE HOT, COLD AND FROZEN DESSERT NOMINAL HOURS: 55

UNIT TITLE: PREPARE HOT, COLD AND FROZEN DESSERT NOMINAL HOURS: 55 UNIT TITLE: PREPARE HOT, COLD AND FROZEN DESSERT NOMINAL HOURS: 55 UNIT NUMBER: D1.HCC.CL2.14 UNIT DESCRIPTOR: This unit deals with skills and knowledge required by cooks, chefs and patissiers to prepare,

More information

NOMINAL HOURS: UNIT NUMBER: UNIT DESCRIPTOR:

NOMINAL HOURS: UNIT NUMBER: UNIT DESCRIPTOR: UNIT TITLE: PREPARE AND SERVE COCKTAILS NOMINAL HOURS: UNIT NUMBER: UNIT DESCRIPTOR: This unit deals with the skills and knowledge required to prepare and serve cocktails within the hotel industry workplace

More information

NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY DEPARTMENT OF HOSPITALITY MANAGEMENT COURSE OUTLINE COURSE #: HMGT 4961 COURSE TITLE: CONTEMPORARY CUISINE

NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY DEPARTMENT OF HOSPITALITY MANAGEMENT COURSE OUTLINE COURSE #: HMGT 4961 COURSE TITLE: CONTEMPORARY CUISINE NEW YORK CITY COLLEGE OF TECHNOLOGY, CUNY DEPARTMENT OF HOSPITALITY MANAGEMENT COURSE OUTLINE COURSE #: HMGT 4961 COURSE TITLE: CONTEMPORARY CUISINE CLASS HOURS: 1.5 LAB HOURS: 4.5 CREDITS: 3 1. COURSE

More information

2016 China Dry Bean Historical production And Estimated planting intentions Analysis

2016 China Dry Bean Historical production And Estimated planting intentions Analysis 2016 China Dry Bean Historical production And Estimated planting intentions Analysis Performed by Fairman International Business Consulting 1 of 10 P a g e I. EXECUTIVE SUMMARY A. Overall Bean Planting

More information

Fibonacci Numbers: How To Use Fibonacci Numbers To Predict Price Movements [Kindle Edition] By Glenn Wilson

Fibonacci Numbers: How To Use Fibonacci Numbers To Predict Price Movements [Kindle Edition] By Glenn Wilson Fibonacci Numbers: How To Use Fibonacci Numbers To Predict Price Movements [Kindle Edition] By Glenn Wilson If you are searching for a book by Glenn Wilson Fibonacci Numbers: How to Use Fibonacci Numbers

More information

Climate change may alter human physical activity patterns

Climate change may alter human physical activity patterns In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION VOLUME: 1 ARTICLE NUMBER: 0097 Climate change may alter human physical activity patterns Nick Obradovich and James H. Fowler

More information

Online Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform

Online Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform Online Appendix to Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform This document contains several additional results that are untabulated but referenced

More information

Detecting Melamine Adulteration in Milk Powder

Detecting Melamine Adulteration in Milk Powder Detecting Melamine Adulteration in Milk Powder Introduction Food adulteration is at the top of the list when it comes to food safety concerns, especially following recent incidents, such as the 2008 Chinese

More information

Primary Learning Outcomes: Students will be able to define the term intent to purchase evaluation and explain its use.

Primary Learning Outcomes: Students will be able to define the term intent to purchase evaluation and explain its use. THE TOMATO FLAVORFUL OR FLAVORLESS? Written by Amy Rowley and Jeremy Peacock Annotation In this classroom activity, students will explore the principles of sensory evaluation as they conduct and analyze

More information

AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship

AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship AJAE Appendix: Testing Household-Specific Explanations for the Inverse Productivity Relationship Juliano Assunção Department of Economics PUC-Rio Luis H. B. Braido Graduate School of Economics Getulio

More information

Napa County Planning Commission Board Agenda Letter

Napa County Planning Commission Board Agenda Letter Agenda Date: 7/1/2015 Agenda Placement: 10A Continued From: May 20, 2015 Napa County Planning Commission Board Agenda Letter TO: FROM: Napa County Planning Commission John McDowell for David Morrison -

More information