# Analysing Ecological Data

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 Alain F. Zuur Elena N. Ieno Graham M. Smith Analysing Ecological Data University- una Landesbibliothe;< Darmstadt Eibliothek Biologie tov.-nr. 4y Springer

2 Contents Contributors xix 1 Introduction Part 1: Applied statistical theory Part 2: The case studies Data, software and flowcharts 6 2 Data management and software Introduction, Data management Data preparation Statistical software 13 3 Advice for teachers Introduction 17 4 Exploration The first steps : Outliers, transformations and standardisations A final thought on data exploration 47 5 Linear regression Bivariate linear regression Multiple linear regression Partial linear regression 73 6 Generalised linear modelling Poisson regression Logistic regression 88 7 Additive and generalised additive modelling Introduction The additive model Example of an additive model Estimate the smoother and amount of smoothing Additive models with multiple explanatory variables 108

3 xii Contents 7.6 Choosing the amount of smoothing Model selection and validation Generalised additive modelling Where to go from here Introduction to mixed modelling Introduction.: The random intercept and slope model Model selection and validation A bit of theory Another mixed modelling example Additive mixed modelling Univariate tree models Introduction Pruning the tree.' Classification trees A detailed example: Ditch data Measures of association Introduction Association between sites: Q analysis Association among species: R analysis Q and R analysis: Concluding remarks Hypothesis testing with measures of association Ordination First encounter Bray-Curtis ordination Principal component analysis and redundancy analysis The underlying principle of PCA PCA: Two easy explanations PCA: Two technical explanations Example of PCA The biplot General remarks Chord and Hellinger transformations Explanatory variables Redundancy analysis Partial RDA and variance partitioning PCA regression to deal with collinearity Correspondence analysis and canonical correspondence analysis Gaussian regression and extensions..: Three rationales for correspondence analysis From RGR to CCA 238

4 Contents xiii 13.4 Understanding the CCA triplot When to use PCA, CA, RDA or CCA Problems with CA and CCA : Introduction to discriminant analysis Introduction Assumptions Example The mathematics : The numerical output for the sparrow data Principal coordinate analysis and non-metric multidimensional scaling Principal coordinate analysis Non-metric multidimensional scaling ^ Time series analysis Introduction Using what we have already seen before Auto-regressive integrated moving average models with exogenous variables L Common trends and sudden changes Repeated LOESS smoothing Identifying the seasonal component Common trends: MAFA Common trends: Dynamic factor analysis Sudden changes: Chronological clustering ;..: Analysis and modelling of lattice data Lattice data... : Numerical representation of the lattice structure Spatial correlation.....= Modelling lattice data More exotic models...'...-..! ^6 Summary Spatially continuous data analysis and modelling Spatially continuous data : Geostatistical functions and assumptions Exploratory variography analysis : Geostatistical modelling: Kriging A full spatial analysis of the bird radar data Univariate methods to analyse abundance of decapod larvae Introduction The data : Data exploration 377

5 xiv Contents 20.4 Linear regression results Additive modelling results How many samples to take? Discussion Analysing presence and absence data for flatfish distribution in the Tagus estuary, Portugal Introduction Data and materials, Data exploration Classification trees Generalised additive modelling Generalised linear modelling Discussion Crop pollination by honeybees in Argentina using additive mixed modelling Introduction Experimental setup Abstracting the information First steps of the analyses: Data exploration Additive mixed modelling Discussion and conclusions Investigating the effects of rice farming on aquatic birds with mixed modelling Introduction ".'.' The data : Getting familiar with the data: Exploration Building a mixed model. : The optimal model in terms of random components Validating the optimal linear mixed model More numerical output for the optimal model Discussion Classification trees and radar detection of birds for North Sea wind farms Introduction From radars to data Classification trees A tree for the birds A tree for birds, clutter and more clutter Discussion and conclusions Fish stock identification through neural network analysis of parasite fauna 449

6 Contents xv 25.1 Introduction : Horse mackerel in the northeast Atlantic Neural networks Collection of data i;! Data exploration Neural network results..= Discussion.:: Monitoring for change: Using generalised least squares, non-metric multidimensional scaling, and the Mantel test on western Montana grasslands Introduction The data Data exploration Linear regression results Generalised least squares results...: Multivariate analysis results Discussion..' Univariate and multivariate analysis applied on a Dutch sandy beach community Introduction The variables Analysing the data using univariate methods Analysing the data using multivariate methods Discussion and conclusions ': : Multivariate analyses of South-American zoobenthic species spoilt for choice Introduction and the underlying questions Study site and sample collection Data exploration, The Mantel test approach The transformation plus RDA approach Discussion and conclusions Principal component analysis applied to harbour porpoise fatty acid data Introduction The data Principal component analysis Data exploration Principal component analysis results Simpler alternatives to PCA Discussion..526

7 xvi Contents 30 Multivariate analyses of morphometric turtle data size and shape Introduction The turtle data Data exploration Overview of classic approaches related to PCA 534,3.0.5 Applying PCA to the original turtle data Classic morphometric data analysis approaches A geometric morphometric approach ; Redundancy analysis and additive modelling applied on savanna tree data, Introduction Study area Methods Results Discussion Canonical correspondence analysis of lowland pasture vegetation in the humid tropics of Mexico Introduction The study area The data, Data exploration Canonical correspondence analysis results African star grass Discussion and conclusion Estimating common trends in Portuguese fisheries landings Introduction '..rr., The time series data MAFA and DFA : MAFA results DFA results v......; Discussion Common trends in demersal communities on the Newfoundland-Labrador Shelf Introduction Data Time series analysis, Discussion Sea level change and salt marshes in the Wadden Sea: A time series analysis Interaction between hydrodynamical and biological factors The data 603

8 Contents xvii 35.3 Data exploration Additive mixed modelling Additive mixed modelling results Discussion Time series analysis of Hawaiian waterbirds Introduction Endangered Hawaiian waterbirds Data exploration Three ways to estimate trends Additive mixed modelling Sudden breakpoints Discussion Spatial modelling of forest community features in the Volzhsko-Kamsky reserve Introduction Study area Data exploration Models of boreality without spatial auto-correlation Models of boreality with spatial auto-correlation Conclusion 646 References 649 Index 667

### Design & Analysis of Ecological Data. Landscape of Statistical Methods...

Design & Analysis of Ecological Data Landscape of Statistical Methods: Part 3 Topics: 1. Multivariate statistics 2. Finding groups - cluster analysis 3. Testing/describing group differences 4. Unconstratined

### Applied Multivariate Analysis

Neil H. Timm Applied Multivariate Analysis With 42 Figures Springer Contents Preface Acknowledgments List of Tables List of Figures vii ix xix xxiii 1 Introduction 1 1.1 Overview 1 1.2 Multivariate Models

### Univariate and Multivariate Methods PEARSON. Addison Wesley

Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston

### MULTIVARIATE DATA ANALYSIS i.-*.'.. ' -4

SEVENTH EDITION MULTIVARIATE DATA ANALYSIS i.-*.'.. ' -4 A Global Perspective Joseph F. Hair, Jr. Kennesaw State University William C. Black Louisiana State University Barry J. Babin University of Southern

Ronald Christensen Advanced Linear Modeling Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization Second Edition Springer Preface to the Second Edition

### 36 Time series analysis of Hawaiian waterbirds

Reed, J. M., C. S. Elphick, A. F. Zuur, E. N. Ieno, and G. M. Smith. 2007. Time series analysis of Hawaiian waterbirds. Pages 615-632 in, Analysing Ecological Data. A. F. Zuur, E. N. Ieno, and G. M. Smith.

### Service 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

### Step 5: Conduct Analysis. The CCA Algorithm

Model Parameterization: Step 5: Conduct Analysis P Dropped species with fewer than 5 occurrences P Log-transformed species abundances P Row-normalized species log abundances (chord distance) P Selected

### Applied Regression Analysis and Other Multivariable Methods

THIRD EDITION Applied Regression Analysis and Other Multivariable Methods David G. Kleinbaum Emory University Lawrence L. Kupper University of North Carolina, Chapel Hill Keith E. Muller University of

### DATA ANALYTICS USING R

DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

### Assumptions. Assumptions of linear models. Boxplot. Data exploration. Apply to response variable. Apply to error terms from linear model

Assumptions Assumptions of linear models Apply to response variable within each group if predictor categorical Apply to error terms from linear model check by analysing residuals Normality Homogeneity

### Course Agenda. First Day. 4 th February - Monday 14.30-19.00. 14:30-15.30 Students Registration Polo Didattico Laterino

Course Agenda First Day 4 th February - Monday 14.30-19.00 14:30-15.30 Students Registration Main Entrance Registration Desk 15.30-17.00 Opening Works Teacher presentation Brief Students presentation Course

### Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com

SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING

### 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

### Multi-scale upscaling approaches of soil properties from soil monitoring data

local scale landscape scale forest stand/ site level (management unit) Multi-scale upscaling approaches of soil properties from soil monitoring data sampling plot level Motivation: The Need for Regionalization

Graduate Programs in Statistics Course Titles STAT 100 CALCULUS AND MATR IX ALGEBRA FOR STATISTICS. Differential and integral calculus; infinite series; matrix algebra STAT 195 INTRODUCTION TO MATHEMATICAL

### 7 Time series analysis

7 Time series analysis In Chapters 16, 17, 33 36 in Zuur, Ieno and Smith (2007), various time series techniques are discussed. Applying these methods in Brodgar is straightforward, and most choices are

### 4.7. Canonical ordination

Université Laval Analyse multivariable - mars-avril 2008 1 4.7.1 Introduction 4.7. Canonical ordination The ordination methods reviewed above are meant to represent the variation of a data matrix in a

### Regression 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

### Exploratory Data Analysis with MATLAB

Computer Science and Data Analysis Series Exploratory Data Analysis with MATLAB Second Edition Wendy L Martinez Angel R. Martinez Jeffrey L. Solka ( r ec) CRC Press VV J Taylor & Francis Group Boca Raton

### Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences Third Edition Jacob Cohen (deceased) New York University Patricia Cohen New York State Psychiatric Institute and Columbia University

### Computer program review

Journal of Vegetation Science 16: 355-359, 2005 IAVS; Opulus Press Uppsala. - Ginkgo, a multivariate analysis package - 355 Computer program review Ginkgo, a multivariate analysis package Bouxin, Guy Haute

### Probability and Statistics

Probability and Statistics Syllabus for the TEMPUS SEE PhD Course (Podgorica, April 4 29, 2011) Franz Kappel 1 Institute for Mathematics and Scientific Computing University of Graz Žaneta Popeska 2 Faculty

### Geostatistics Exploratory Analysis

Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Exploratory Analysis Carlos Alberto Felgueiras cfelgueiras@isegi.unl.pt

### Semester 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

### Multivariate Statistical Inference and Applications

Multivariate Statistical Inference and Applications ALVIN C. RENCHER Department of Statistics Brigham Young University A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York Chichester Weinheim

### Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier

Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.

Statistics Graduate Courses STAT 7002--Topics in Statistics-Biological/Physical/Mathematics (cr.arr.).organized study of selected topics. Subjects and earnable credit may vary from semester to semester.

### Multivariate Analysis. Overview

Multivariate Analysis Overview Introduction Multivariate thinking Body of thought processes that illuminate the interrelatedness between and within sets of variables. The essence of multivariate thinking

### Economic 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

### Business 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

### Collinearity of independent variables. Collinearity is a condition in which some of the independent variables are highly correlated.

Collinearity of independent variables Collinearity is a condition in which some of the independent variables are highly correlated. Why is this a problem? Collinearity tends to inflate the variance of

### Learning Example. Machine learning and our focus. Another Example. An example: data (loan application) The data and the goal

Learning Example Chapter 18: Learning from Examples 22c:145 An emergency room in a hospital measures 17 variables (e.g., blood pressure, age, etc) of newly admitted patients. A decision is needed: whether

### Learning outcomes. Knowledge and understanding. Competence and skills

Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

### Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model

Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model Xavier Conort xavier.conort@gear-analytics.com Motivation Location matters! Observed value at one location is

### Teaching Multivariate Analysis to Business-Major Students

Teaching Multivariate Analysis to Business-Major Students Wing-Keung Wong and Teck-Wong Soon - Kent Ridge, Singapore 1. Introduction During the last two or three decades, multivariate statistical analysis

### What is correlational research?

Key Ideas Purpose and use of correlational designs How correlational research developed Types of correlational designs Key characteristics of correlational designs Procedures used in correlational studies

### Ecology Chapter Teacher Sheet. Activity #3: Biotic vs. Abiotic Factors

Ecology Chapter Teacher Sheet Activity #3: Biotic vs. Abiotic Factors California Content Standards Biology (Ecology) 6e Biologoy (Ecology) 6f Objectives: To determine student's prior knowledge of the environment

### Analysis of algorithms of time series analysis for forecasting sales

SAINT-PETERSBURG STATE UNIVERSITY Mathematics & Mechanics Faculty Chair of Analytical Information Systems Garipov Emil Analysis of algorithms of time series analysis for forecasting sales Course Work Scientific

### Semester 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

### business 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

### Multivariate Analysis of Ecological Data

Multivariate Analysis of Ecological Data MICHAEL GREENACRE Professor of Statistics at the Pompeu Fabra University in Barcelona, Spain RAUL PRIMICERIO Associate Professor of Ecology, Evolutionary Biology

### Predictive Modeling Techniques in Insurance

Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics

### Ecological Methodology Second Edition

Ecological Methodology Second Edition Charles J. Krebs University of British Columbia Technische Universitat Darmstadt FACHBEREICH 10 BIOLOGIE Bi bliothek SchnittspahnstraBe 10 D-64 28 7 Darmstadt Inv.-Nr.

### Course 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, McGraw-Hill/Irwin, 2010, ISBN: 9780077384470 [This

### Computer-Aided Multivariate Analysis

Computer-Aided Multivariate Analysis FOURTH EDITION Abdelmonem Af if i Virginia A. Clark and Susanne May CHAPMAN & HALL/CRC A CRC Press Company Boca Raton London New York Washington, D.C Contents Preface

### Applied Spatial Statistics in R, Section 5

Applied Spatial Statistics in R, Section 5 Geostatistics Yuri M. Zhukov IQSS, Harvard University January 16, 2010 Yuri M. Zhukov (IQSS, Harvard University) Applied Spatial Statistics in R, Section 5 January

### Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression

Unit 31 A Hypothesis Test about Correlation and Slope in a Simple Linear Regression Objectives: To perform a hypothesis test concerning the slope of a least squares line To recognize that testing for a

### Appendix 1: Time series analysis of peak-rate years and synchrony testing.

Appendix 1: Time series analysis of peak-rate years and synchrony testing. Overview The raw data are accessible at Figshare ( Time series of global resources, DOI 10.6084/m9.figshare.929619), sources are

### INTRODUCTORY 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

### Chapter 14: Analyzing Relationships Between Variables

Chapter Outlines for: Frey, L., Botan, C., & Kreps, G. (1999). Investigating communication: An introduction to research methods. (2nd ed.) Boston: Allyn & Bacon. Chapter 14: Analyzing Relationships Between

### BayesX - Software for Bayesian Inference in Structured Additive Regression

BayesX - Software for Bayesian Inference in Structured Additive Regression Thomas Kneib Faculty of Mathematics and Economics, University of Ulm Department of Statistics, Ludwig-Maximilians-University Munich

### Econometric Analysis of Cross Section and Panel Data Second Edition. Jeffrey M. Wooldridge. The MIT Press Cambridge, Massachusetts London, England

Econometric Analysis of Cross Section and Panel Data Second Edition Jeffrey M. Wooldridge The MIT Press Cambridge, Massachusetts London, England Preface Acknowledgments xxi xxix I INTRODUCTION AND BACKGROUND

### QUANTITATIVE 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

### The University of British Columbia Faculty of Forestry Forestry Advanced Regression Analysis Course Outline for 2011

The University of British Columbia Faculty of Forestry Forestry 530 -- Advanced Regression Analysis Course Outline for 2011 Calendar Description: FRST 530 (3) Multiple Regression Methods. Matrix algebra;

### Chapter 11: Two Variable Regression Analysis

Department of Mathematics Izmir University of Economics Week 14-15 2014-2015 In this chapter, we will focus on linear models and extend our analysis to relationships between variables, the definitions

### (and sex and drugs and rock 'n' roll) ANDY FIELD

DISCOVERING USING SPSS STATISTICS THIRD EDITION (and sex and drugs and rock 'n' roll) ANDY FIELD CONTENTS Preface How to use this book Acknowledgements Dedication Symbols used in this book Some maths revision

### COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments

Contents List of Figures Foreword Preface xxv xxiii xv Acknowledgments xxix Chapter 1 Fraud: Detection, Prevention, and Analytics! 1 Introduction 2 Fraud! 2 Fraud Detection and Prevention 10 Big Data for

### Audit Analytics. --An innovative course at Rutgers. Qi Liu. Roman Chinchila

Audit Analytics --An innovative course at Rutgers Qi Liu Roman Chinchila A new certificate in Analytic Auditing Tentative courses: Audit Analytics Special Topics in Audit Analytics Forensic Accounting

### Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

### Non-Life Insurance Mathematics

Thomas Mikosch Non-Life Insurance Mathematics An Introduction with the Poisson Process Second Edition 4y Springer Contents Part I Collective Risk Models 1 The Basic Model 3 2 Models for the Claim Number

### Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

### Chapter 4: Vector Autoregressive Models

Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...

### Chapter 1 Introduction. 1.1 Introduction

Chapter 1 Introduction 1.1 Introduction 1 1.2 What Is a Monte Carlo Study? 2 1.2.1 Simulating the Rolling of Two Dice 2 1.3 Why Is Monte Carlo Simulation Often Necessary? 4 1.4 What Are Some Typical Situations

### Class 6: Chapter 12. Key Ideas. Explanatory Design. Correlational Designs

Class 6: Chapter 12 Correlational Designs l 1 Key Ideas Explanatory and predictor designs Characteristics of correlational research Scatterplots and calculating associations Steps in conducting a correlational

### STAT 200: Course Aims and Objectives

STAT 200: Course Aims and Objectives Attitudinal aims In addition to specific learning outcomes, the course aims to shape the attitudes of learners regarding the field of Statistics. Specifically, the

### Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images

Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images S. E. Báez Cazull Pre-Service Teacher Program University

### Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

### THE MULTIVARIATE ANALYSIS RESEARCH GROUP. Carles M Cuadras Departament d Estadística Facultat de Biologia Universitat de Barcelona

THE MULTIVARIATE ANALYSIS RESEARCH GROUP Carles M Cuadras Departament d Estadística Facultat de Biologia Universitat de Barcelona The set of statistical methods known as Multivariate Analysis covers a

### Data Mining mit der JMSL Numerical Library for Java Applications

Data Mining mit der JMSL Numerical Library for Java Applications Stefan Sineux 8. Java Forum Stuttgart 07.07.2005 Agenda Visual Numerics JMSL TM Numerical Library Neuronale Netze (Hintergrund) Demos Neuronale

### Nominal and ordinal logistic regression

Nominal and ordinal logistic regression April 26 Nominal and ordinal logistic regression Our goal for today is to briefly go over ways to extend the logistic regression model to the case where the outcome

### New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction

Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.

### y = Xβ + ε B. Sub-pixel Classification

Sub-pixel Mapping of Sahelian Wetlands using Multi-temporal SPOT VEGETATION Images Jan Verhoeye and Robert De Wulf Laboratory of Forest Management and Spatial Information Techniques Faculty of Agricultural

### Classification and Regression Trees

Classification and Regression Trees Bob Stine Dept of Statistics, School University of Pennsylvania Trees Familiar metaphor Biology Decision tree Medical diagnosis Org chart Properties Recursive, partitioning

### EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

### PLANT ECOLOGY. How many plants are in each plot? Why do different plants grow in different areas?

81 CHAPTER 1: 4: PLANT ECOLOGY You just have to start at a particular point and count. Concentrate on what you re doing and try not to lose track. LIZ JOHNSON (Plant Inventory, p.184) TARGET QUESTION:

### , then the form of the model is given by: which comprises a deterministic component involving the three regression coefficients (

Multiple regression Introduction Multiple regression is a logical extension of the principles of simple linear regression to situations in which there are several predictor variables. For instance if we

### STATISTICS COURSES UNDERGRADUATE CERTIFICATE FACULTY. Explanation of Course Numbers. Bachelor's program. Master's programs.

STATISTICS Statistics is one of the natural, mathematical, and biomedical sciences programs in the Columbian College of Arts and Sciences. The curriculum emphasizes the important role of statistics as

### Fairfield 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

### UNIT 3 LECTURE 3 FOOD CHAIN, FOOD WEB, ECOLOGICAL PYRAMID. Italics indicate text already on slide

UNIT 3 LECTURE 3 FOOD CHAIN, FOOD WEB, ECOLOGICAL PYRAMID Italics indicate text already on slide SLIDE 1 Definition of food chain The transfer of food energy from the source in plants through a series

### Multivariate Tools for Modern Pharmaceutical Control FDA Perspective

Multivariate Tools for Modern Pharmaceutical Control FDA Perspective IFPAC Annual Meeting 22 January 2013 Christine M. V. Moore, Ph.D. Acting Director ONDQA/CDER/FDA Outline Introduction to Multivariate

### Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0

### ECONOMETRIC THEORY. MODULE I Lecture - 1 Introduction to Econometrics

ECONOMETRIC THEORY MODULE I Lecture - 1 Introduction to Econometrics Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Econometrics deals with the measurement

### Lecture 9: Introduction to Pattern Analysis

Lecture 9: Introduction to Pattern Analysis g Features, patterns and classifiers g Components of a PR system g An example g Probability definitions g Bayes Theorem g Gaussian densities Features, patterns

### Regression Analysis Using ArcMap. By Jennie Murack

Regression Analysis Using ArcMap By Jennie Murack Regression Basics How is Regression Different from other Spatial Statistical Analyses? With other tools you ask WHERE something is happening? Are there

### Predicting Customer Default Times using Survival Analysis Methods in SAS

Predicting Customer Default Times using Survival Analysis Methods in SAS Bart Baesens Bart.Baesens@econ.kuleuven.ac.be Overview The credit scoring survival analysis problem Statistical methods for Survival

### Statistical Analysis. NBAF-B Metabolomics Masterclass. Mark Viant

Statistical Analysis NBAF-B Metabolomics Masterclass Mark Viant 1. Introduction 2. Univariate analysis Overview of lecture 3. Unsupervised multivariate analysis Principal components analysis (PCA) Interpreting

### Practical Data Science with R

Practical Data Science with R Instructor Matthew Renze Twitter: @matthewrenze Email: matthew@matthewrenze.com Web: http://www.matthewrenze.com Course Description Data science is the practice of transforming

### Using Machine Learning Techniques to Improve Precipitation Forecasting

Using Machine Learning Techniques to Improve Precipitation Forecasting Joshua Coblenz Abstract This paper studies the effect of machine learning techniques on precipitation forecasting. Twelve features

### Species Associations: The Kendall Coefficient of Concordance Revisited

Species Associations: The Kendall Coefficient of Concordance Revisited Pierre LEGENDRE The search for species associations is one of the classical problems of community ecology. This article proposes to

### Spatial Statistics Chapter 3 Basics of areal data and areal data modeling

Spatial Statistics Chapter 3 Basics of areal data and areal data modeling Recall areal data also known as lattice data are data Y (s), s D where D is a discrete index set. This usually corresponds to data

### Exploratory Data Analysis -- Introduction

Lecture Notes in Quantitative Biology Exploratory Data Analysis -- Introduction Chapter 19.1 Revised 25 November 1997 ReCap: Correlation Exploratory Data Analysis What is it? Exploratory vs confirmatory

### List of Examples. Examples 319

Examples 319 List of Examples DiMaggio and Mantle. 6 Weed seeds. 6, 23, 37, 38 Vole reproduction. 7, 24, 37 Wooly bear caterpillar cocoons. 7 Homophone confusion and Alzheimer s disease. 8 Gear tooth strength.

### A protocol for data exploration to avoid common statistical problems

Methods in Ecology and Evolution 2010, 1, 3 14 doi: 10.1111/j.2041-210X.2009.00001.x A protocol for data exploration to avoid common statistical problems Alain F. Zuur* 1,2, Elena N. Ieno 1,2 and Chris

### Practical. I conometrics. data collection, analysis, and application. Christiana E. Hilmer. Michael J. Hilmer San Diego State University

Practical I conometrics data collection, analysis, and application Christiana E. Hilmer Michael J. Hilmer San Diego State University Mi Table of Contents PART ONE THE BASICS 1 Chapter 1 An Introduction

### ECOL 411/ MARI451 Reading Ecology and Special Topic Marine Science - (20 pts) Course coordinator: Professor Stephen Wing Venue: Department of Marine

ECOL 411/ MARI451 Reading Ecology and Special Topic Marine Science - (20 pts) Course coordinator: Professor Stephen Wing Venue: Department of Marine Science Seminar Room (140) Time: Mondays between 12-3

### Correlation and Regression

Dublin Institute of Technology ARROW@DIT Books/Book Chapters School of Management 2012-10 Correlation and Regression Donal O'Brien Dublin Institute of Technology, donal.obrien@dit.ie Pamela Sharkey Scott