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Introduction to Environmental Statistics [Kõva köide]

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  • Formaat: Hardback, 280 pages, kõrgus x laius: 229x152 mm
  • Ilmumisaeg: 01-Dec-2022
  • Kirjastus: Delve Publishing
  • ISBN-10: 1774694603
  • ISBN-13: 9781774694602
Teised raamatud teemal:
Introduction to Environmental StatisticsSuurem pilt
  • Formaat: Hardback, 280 pages, kõrgus x laius: 229x152 mm
  • Ilmumisaeg: 01-Dec-2022
  • Kirjastus: Delve Publishing
  • ISBN-10: 1774694603
  • ISBN-13: 9781774694602
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781774694602.html
Märksõnad:
Environmental statistics involves the use of statistical approaches to environmental science. It covers methods for managing questions regarding the natural ecosystem in its undisturbed condition. This reader-friendly book emphasizes the fields of probability hypothesis and dimensions that are significant in environmental data analysis, monitoring, research, ecological field studies, and ecological decision making. It discusses fundamental statistical theory with minimal documentation, however without precluding significant details and presumptions. The book likewise presents a hypothesis of how and why environmental physical cycles in the environment create right-slanted, lognormal dispersions. The volume likewise presents the Rollback Statistical Theory, which permits data analysts and administrators to appraise the impact of various emission control methodologies on environmental quality frequency diffusions. Assuming just a simple understanding of polynomial math and analytics, Environmental Data Analysis and Statistics provides a superior reference and assortment of measurable strategies for investigating environmental data and developing precise environmental predictions.
List of Figures
xiii
List of Abbreviations
xvii
Abstract xix
Preface xxi
Chapter 1 Statistics
1(22)
1.1 Introduction
2(2)
1.2 History of Statistics
4(5)
1.3 Types of Statistics
9(6)
1.4 Exploratory Data Analysis (EDA)
15(1)
1.5 Types of Descriptive Statistics
15(1)
1.6 Measure of Variability
16(1)
1.7 Inferential Statistics
17(6)
Chapter 2 Development of Environment Statistics
23(10)
2.1 Introduction
24(1)
2.2 Framework for the Development of Environment Statistics
25(5)
2.3 State-of-Environment Statistics in Developing Member Countries
30(3)
Chapter 3 Environmental Data
33(30)
3.1 The Frameworks of the Data
36(2)
3.2 Coals of Collecting Data About the Environment
38(2)
3.3 Additional Information and Analysis Regarding Risk Indices
40(1)
3.4 Methods for Public Relations and Retail Sales that Are Adapted Precisely to the Environment
41(1)
3.5 Environment APIs!
42(1)
3.6 The Amounts of Humidity
43(2)
3.7 The State of the Atmosphere Has a Role
45(3)
3.8 Parameters That are Used to Measure Biodiversity
48(2)
3.9 Diversity of Species and Representation of Taxonomic Groups in the Data
50(1)
3.10 Concerning Measurements, Accuracy, and Possible Bias
50(2)
3.11 The Benefits and Drawbacks of "Averaged" Indexes
52(1)
3.12 Considering the Numerous Error Causes
53(1)
3.13 Analysis of Biodiversity Data
54(1)
3.14 Societal and Occupational Health Information
55(1)
3.15 Excellent Air Quality
56(1)
3.16 Examine the Locations of Monitors Using an Interactive Map
56(1)
3.17 Data Visualization
57(1)
3.18 Some Fundamental Air Quality Concepts
58(1)
3.19 Particulate Matter Data
59(1)
3.20 Ozone Depletion Trends
59(1)
3.21 Sources That Give Information on the Environment
60(1)
3.22 How Should Data About the Environment Be Evaluated?
61(1)
3.23 What is the Cost of Environmental Data on Average?
61(1)
3.24 What Questions Should You Ask Environmental Data Providers?
62(1)
Chapter 4 The Role of Statistics in Environmental Science
63(34)
4.1 Uses of Statistics in Environmental Science
66(1)
4.2 Sources of Information
67(1)
4.3 Methods
68(1)
4.4 Basic Concepts
68(1)
4.5 Applications of Statistical Tools in Environment Science
69(6)
4.6 Statistical Models
75(2)
4.7 Goodness of Fit Test
77(1)
4.8 Theoretical or Biological Models
78(3)
4.9 Fitting Niche Apportionments Models to Empirical Data
81(1)
4.10 Species Accumulation Curves
82(2)
4.11 Users of Environmental Data
84(1)
4.12 Environmental Information
85(3)
4.13 Sources of Environmental Statistics
88(2)
4.14 Monitoring Systems
90(1)
4.15 Scientific Research
90(2)
4.16 Geospatial Information and Environment Statistics
92(1)
4.17 Institutional Dimensions of Environment Statistics
93(1)
4.18 Importance of Environmental Statisticians
94(3)
Chapter 5 Types of Data Sources
97(36)
5.1 What Are Sources of Data?
99(1)
5.2 Types of Data Sources
99(3)
5.3 Statistical Surveys
102(2)
5.4 Collection of Data
104(1)
5.5 Processing and Editing of Data
105(1)
5.6 Estimates and Projections Are Created
105(1)
5.7 Analysis of Data
106(1)
5.8 Procedures for Review
106(1)
5.9 Dissemination of Information Products
107(3)
5.10 The Benefits of Administrative Data
110(1)
5.11 Limitations of Administrative Data
111(2)
5.12 Obtaining and Learning from Administrative Data
113(1)
5.13 Remote Sensing and Mapping
114(5)
5.14 Technologies of Digital Information and Communication
119(2)
5.15 Environmental Monitoring Types
121(2)
5.16 Lot-Based Environmental Monitoring
123(1)
5.17 Reasons For Environmental Monitoring
124(1)
5.18 Data From Scientific Research and Special Projects
124(3)
5.19 Global And International Sources of Data
127(1)
5.20 Key Government Databases
128(3)
5.21 A Data Source is the Location Where Data That is Being Used Originates From
131(1)
5.22 Data Source Types
131(1)
5.23 Sources of Machine Data
132(1)
Chapter 6 Environmental Sampling
133(22)
6.1 Introduction
134(1)
6.2 Importance of Environmental Sampling
135(1)
6.3 Environmental Sampling Methods
136(4)
6.4 Hydrological Traces
140(1)
6.5 Measuring PH and Electrical Conductivity (EC)
141(2)
6.6 Stream Caging
143(3)
6.7 Winkler Method for Measuring Dissolved Oxygen
146(2)
6.8 Measuring Turbidity Using a Secchi Disk
148(2)
6.9 Conductivity, Temperature, and Depth Rosette (CTD)
150(2)
6.10 Stable Isotope Primer and Hydrological Applications
152(2)
6.11 Challenges of Environmental Sampling
154(1)
Chapter 7 Models for Data
155(38)
7.1 Introduction
156(2)
7.2 Literature Review
158(5)
7.3 The Process of Developing Models for Data
163(1)
7.4 Types of Data Models
164(3)
7.5 The Advantages that Come With Using The ER Model
167(3)
7.6 Importance of Data Models
170(6)
7.7 What Makes a Data Model Good?
176(7)
7.8 Data Properties
183(1)
7.9 Data Organization
184(1)
7.10 Data Structure
184(1)
7.11 Data Modeling Tools to Know
185(1)
7.12 ER/Studio
186(1)
7.13 Db Modeling
186(1)
7.14 Erbuilder
186(1)
7.15 Heidisql
187(1)
7.16 Open-Source
187(1)
7.17 A Modeling Tool for SQL Databases
188(1)
7.18 Data Flow Diagram (DFD)
188(1)
7.19 Data Conceptualization
188(1)
7.20 Unified Modeling Language (UML) Models
189(1)
7.21 Data Modeling Features
190(1)
7.22 Data Modeling Examples
191(1)
7.23 Summary
192(1)
Chapter 8 Spatial-Data Analysis
193(32)
8.1 Sa Geometric
197(4)
8.2 History
201(2)
8.3 Spatial Data Analysis In Science
203(1)
8.4 Functions of Spatial Analysis
204(2)
8.5 Spatial Processes
206(3)
8.6 The Spatial Data Matrix: It's Quality
209(2)
8.7 Sources of Spatial Data
211(2)
8.8 The Purpose and Conduct of Spatial Sampling
213(1)
8.9 Models for Measurement Error
214(1)
8.10 Analysis of Spatial Data and Data Consistency
214(1)
8.11 EDA (Exploratory Data Analysis) and ESDA (Exploratory Spatial Data Analysis)
215(5)
8.12 Data Visualization: Approaches and Tasks
220(5)
Chapter 9 Challenges in Environmental Statistics
225(16)
9.1 Introduction
226(1)
9.2 Statistical Models for Spatiotemporal Data (STD)
227(1)
9.3 Spatiotemporal (ST) Relationships
228(1)
9.4 Data Characteristics
229(2)
9.5 Random Fields
231(1)
9.6 Gaussian Processes and Machine Learning (MI)
232(1)
9.7 Neural Networks
232(1)
9.8 Population Dynamics Stochastic Modeling
233(1)
9.9 Population Dynamics
234(1)
9.10 Spatial Extended System
235(1)
9.11 Non-Gaussian Noise Sources
236(1)
9.12 Environmental Exposures and Health Effects in Collection of Environmental Statistics
237(1)
9.13 General Logic and Strategy
237(4)
Chapter 10 Future of Environment Statistics
241(8)
10.1 Use of New Technologies
242(1)
10.2 Technologies that Can Be Used in Environment Statistics: Predictive Analytics
243(2)
10.3 Changes in Utilization of Resources
245(4)
Bibliography 249(6)
Index 255
Dr. Akansha Singh is presently working as Project Scientist in Department of Genetics and Plant Breeding, Institute of Agricultural Sciences, Banaras Hindu University, India. She has also worked as Postdoctoral fellow, Mumbai University and as Associate Professor, Department of Genetics and Plant Breeding, College of Agriculture, Parul University, India She has obtained her Ph.D. (Ag) in Genetics and Plant breeding from Banaras Hindu University in the year 2012. She has been Awarded with ICAR, senior research fellowship in 2010 to pursue PhD. She has authored numerous national and international publications in journals of repute.