Introduction to Probability and Statistics for Ecosystem Managers - Simulation and Resampling

Informationen zum Autor Timothy C. Haas, Lubar School of Business Administration, University of Wisconsin at Milwaukee. Timothy Haas is involved in teaching undergraduate and graduate courses in statistical methods, pursuing decision making and environmental statistics re?search, and collaborating with faculty on application of statistics to Marketing and Eco?nomics. Klappentext Explores computer-intensive probability and statistics for ecosystem management decision makingSimulation is an accessible way to explain probability and stochastic model behavior to beginners. This book introduces probability and statistics to future and practicing ecosystem managers by providing a comprehensive treatment of these two areas. The author presents a self-contained introduction for individuals involved in monitoring, assessing, and managing ecosystems and features intuitive, simulation-based explanations of probabilistic and statistical concepts. Mathematical programming details are provided for estimating ecosystem model parameters with Minimum Distance, a robust and computer-intensive method.The majority of examples illustrate how probability and statistics can be applied to ecosystem management challenges. There are over 50 exercises - making this book suitable for a lecture course in a natural resource and/or wildlife management department, or as the main text in a program of self-study.Key features:* Reviews different approaches to wildlife and ecosystem management and inference.* Uses simulation as an accessible way to explain probability and stochastic model behavior to beginners.* Covers material from basic probability through to hierarchical Bayesian models and spatial/ spatio-temporal statistical inference.* Provides detailed instructions for using R, along with complete R programs to recreate the output of the many examples presented.* Provides an introduction to Geographic Information Systems (GIS) along with examples from Quantum GIS, a free GIS software package.* A companion website featuring all R code and data used throughout the book.* Solutions to all exercises are presented along with an online intelligent tutoring system that supports readers who are using the book for self-study. Zusammenfassung Explores computer-intensive probability and statistics for ecosystem management decision making Simulation is an accessible way to explain probability and stochastic model behavior to beginners. Inhaltsverzeichnis List of figures xiii List of tables xvii Preface xix Acknowledgments xxi List of abbreviations xxiii 1 Introduction 1 1.1 The textbook's purpose 1 1.1.1 The textbook's focus on ecosystem management 2 1.1.2 Reader level, prerequisites, and typical reader jobs 3 1.2 The textbook's pedagogical approach 4 1.2.1 General points 4 1.2.2 Use of this textbook for self-study 4 1.2.3 Learning resources 5 1.3 Chapter summaries 7 1.4 Installing and running R Commander 9 1.4.1 Running R 9 1.4.2 Starting an R Commander session 9 1.4.3 Terminating an R Commander session 10 1.5 Introductory R Commander session 10 1.6 Teaching probability through simulation 13 1.6.1 The frequentist statistical inference paradigm 14 1.7 Summary 15 2 Probability and simulation 17 2.1 Introduction 17 2.2 Basic probability 17 2.2.1 Definitions 17 2.2.2 Independence 20 2.3 Random variables 22 2.3.1 Definitions 22 2.3.2 Simulating random variables 26 2.3.3 A random variable's expected value (mean) and variance 26 2.3.4 Details of the normal (Gaussian) distribution 28 2.3.5 Distribution approximations 30 2.4 Joint distributions 31 2.4.1 Definition 31 2.4.2 Mixed variables 32 2.4.3 Marginal distributio...

List of contents

List of figures xiii
 
List of tables xvii
 
Preface xix
 
Acknowledgments xxi
 
List of abbreviations xxiii
 
1 Introduction 1
 
1.1 The textbook's purpose 1
 
1.1.1 The textbook's focus on ecosystem management 2
 
1.1.2 Reader level, prerequisites, and typical reader jobs 3
 
1.2 The textbook's pedagogical approach 4
 
1.2.1 General points 4
 
1.2.2 Use of this textbook for self-study 4
 
1.2.3 Learning resources 5
 
1.3 Chapter summaries 7
 
1.4 Installing and running R Commander 9
 
1.4.1 Running R 9
 
1.4.2 Starting an R Commander session 9
 
1.4.3 Terminating an R Commander session 10
 
1.5 Introductory R Commander session 10
 
1.6 Teaching probability through simulation 13
 
1.6.1 The frequentist statistical inference paradigm 14
 
1.7 Summary 15
 
2 Probability and simulation 17
 
2.1 Introduction 17
 
2.2 Basic probability 17
 
2.2.1 Definitions 17
 
2.2.2 Independence 20
 
2.3 Random variables 22
 
2.3.1 Definitions 22
 
2.3.2 Simulating random variables 26
 
2.3.3 A random variable's expected value (mean) and variance 26
 
2.3.4 Details of the normal (Gaussian) distribution 28
 
2.3.5 Distribution approximations 30
 
2.4 Joint distributions 31
 
2.4.1 Definition 31
 
2.4.2 Mixed variables 32
 
2.4.3 Marginal distribution 32
 
2.4.4 Conditional distributions 33
 
2.4.5 Independent random variables 34
 
2.5 Influence diagrams 34
 
2.5.1 Definitions 34
 
2.5.2 Example of a Bayesian network in ecosystem management 36
 
2.5.3 Modeling causal relationships with an influence diagram 38
 
2.6 Advantages of influence diagrams in ecosystem management 40
 
2.7 Two ecosystem management Bayesian networks 41
 
2.7.1 Waterbody eutrophication 41
 
2.7.2 Wildlife population viability 41
 
2.8 Influence diagram sensitivity analysis 41
 
2.9 Drawbacks to influence diagrams 42
 
3 Application of probability: Models of political decision making in ecosystem management 43
 
3.1 Introduction 43
 
3.2 Influence diagram models of decision making 43
 
3.2.1 Ecosystem status perception nodes 44
 
3.2.2 Image nodes 44
 
3.2.3 Economic, militaristic, and institutional goal nodes 45
 
3.2.4 Audience effect nodes 45
 
3.2.5 Resource nodes 46
 
3.2.6 Action and target nodes 46
 
3.2.7 Overall goal attainment node 47
 
3.2.8 How a group influence diagram reaches a decision 47
 
3.2.9 An advantage of this decision-making architecture 47
 
3.2.10 Evaluation dimensions 47
 
3.3 Rhino poachers: A simplified model 50
 
3.4 Policymakers: A simplified model 57
 
3.5 Conclusions 59
 
4 Statistical inference I: Basic ideas and parameter estimation 61
 
4.1 Definitions of some fundamental terms 61
 
4.2 Estimating the PDF and CDF 62
 
4.2.1 Histograms 62
 
4.2.2 Ogive 64
 
4.3 Measures of central tendency and dispersion 64
 
4.4 Sample quantiles 65
 
4.4.1 Sample quartiles 65
 
4.4.2 Sample deciles and percentiles 65
 
4.5 Distribution of a statistic 65
 
4.5.1 Basic setup in statistics 65
 
4.5.2 Sampling distributions 66
 
4.5.3 Normal quantile-quantile plot 66
 
4.6 The central limit theorem 68
 
4.7 Parameter estimation 68
 
4.7.1 Bias, variance, and efficiency 69
 
4.8 Interval e

Product details

• Authors: Tc Haas, Timothy C Haas, Timothy C. Haas, Timothy C. (Sheldon B. Lubar School of Busin Haas, HAAS TIMOTHY C, Haas Timothy C.
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 12.07.2013
• EAN: 9781118357682
• ISBN: 978-1-118-35768-2
• No. of pages: 312
• Series: Statistics in Practice; Statistics in Practice
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Business > Economics; Statistik, Statistics, Life Sciences, Biowissenschaften, Umweltstatistik, Methods & Statistics in Ecology, Ökologie / Methoden, Statistik, Umweltstatistik u. Environmetrics, Environmental Statistics & Environmetrics