This authored book is primarily for the readers who are in search of how to use basic concepts of statistics in the field of fishery science. It has been written for the new comers coming in the fishery sector including university students, teachers and research scholars. It enriches the reader’s knowledge of data analysis step by step starting from data collection to its analysis and interpretation along with practical application with real example. It supports readers for enhancing theoretical and practical concept of application of fundamental statistics in fishery domain. Reader may also learn how to analyses data using different statistical software. Each chapter starts with an introduction of the topic followed by statistical concept with example and ends with chapter based question for comprehension purpose. This book is of interest to teachers, researchers, scientists, capacity builders in fishery sciences. Also the book serves as additional reading material for undergraduate and graduate students of fisheries and aquatic sciences.
Author(s): Basant Kumar Das, Dharm Nath Jha, Sanjeev Kumar Sahu, Anil Kumar Yadav, Rohan Kumar Raman, M. Kartikeyan
Publisher: Springer
Year: 2022
Language: English
Pages: 277
City: Singapore
Foreword
Foreword
Preface
Acknowledgements
Contents
About the Authors
List of Figures
List of Tables
1: Nature and Sources of Data for Inland Fisheries Management
Key Topics
1.1 Complexity of Inland Fisheries Management
1.2 Data Requirement
1.2.1 Objectives
1.2.2 Data Type
1.2.3 Data and Information
1.2.3.1 Some Indicators and Associated Data Types
1.2.4 Location and Time
1.2.4.1 Data Collection Frequency
1.3 Data Collection Methods
1.4 Some Frequently Collected Data in Inland Fisheries
1.4.1 Data for Biotic-Environment Assessment
1.4.2 Spatial Data for Fishery Assessment
1.4.3 Data for Stock Enhancement Strategies
1.4.4 Survey Data
1.4.4.1 Example 1.1: Floodplain Wetland Fisheries
1.4.5 Perception-Based Data
1.5 Conclusion
Further Reading
2: Concept of Sampling Methodologies and Their Applications
Key Topics
2.1 Some Definitions
2.2 Simple Random Sampling
2.3 Systematic Sampling
2.3.1 Advantages and Disadvantages Advantage
2.4 Stratified Random Sampling
2.4.1 Estimation of Mean and Variance
2.4.2 Allocation of Sample Size
2.4.3 Advantages of Stratified Random Sampling
2.5 Cluster Sampling
2.5.1 Estimation of Population Mean
2.6 Multistage Sampling
2.7 Sample Size
2.8 Margin of Error
2.9 Fish Sampling
2.10 Water Sampling and Monitoring
2.11 Ecological Sampling Methods
2.11.1 Some Spatial Sampling Methods
2.11.2 Density and Dispersion Patterns
2.11.3 Determining the Dispersion Pattern
2.12 Constraints of Data Collection in Inland Open Waters
2.13 Conclusion
Exercise
Further Reading
3: Descriptive Statistics for Inland Fisheries Data
Key Topics
3.1 Population and Sample
3.2 Parameter and Statistic
3.3 Types of Data
3.4 Data Quality
3.5 Tabular and Graphical Presentation of Data
3.5.1 Frequency Distribution Table
3.5.2 Scatter Diagram
3.5.3 Bar Diagrams
3.5.4 Pie Diagrams
3.5.5 Histogram
3.5.6 Boxplot
3.6 Measures of Central Tendency
3.6.1 Arithmetic Mean
3.6.2 Median
3.6.3 Mode
3.6.4 Geometric Mean (GM)
3.6.5 Harmonic Mean
3.6.6 Weighted Mean
3.7 Measures of Dispersion or Variation
3.7.1 Range
3.7.2 Variance and Standard Deviation (SD)
3.7.3 Standard Error
3.7.4 Coefficient of Variation (CV)
3.7.5 Shape of Data Set/Distribution
3.7.6 Types of Distribution
3.8 Conclusion
Exercise
Further Reading
4: Basic Concept of Hypothesis Testing and Parametric Test
Key Topics
4.1 Significance Level
4.2 Type of Errors
4.3 Power of Test
4.4 Confidence Level, Limits and Interval
4.4.1 Interpretation of Confidence Interval
4.5 Selection of Statistical Tools
4.5.1 One Sample Parametric Test
4.5.2 Large Sample Test of Significance for Population Parameter
4.5.3 Parametric Test of Significance Based on Two Samples
4.5.4 t-Test for Testing of Difference Between Two Means of Uncorrelated Observations
4.6 F-Test for Equality of Two Population Variances
4.7 Conclusion
Exercise
Further Reading
5: Chi-Square Test of Significance
Key Topics
5.1 Chi-Square Test for Goodness of Fit (One Sample Chi-Square)
5.2 Chi-Square Test for Independence
5.3 Measure of Association
5.4 Conclusion
Exercise
Further Reading
6: Use of Non-parametric Test in Fisheries Data
Key Topics
6.1 Concept of Non-parametric Test
6.1.1 Advantages of Non-parametric Hypothesis Test
6.1.2 Disadvantages of Non-parametric Hypothesis Test
6.2 Data Diagnostics for Using Non-parametric Test
6.3 Ranking of Data
6.3.1 Ranking with Ties
6.4 A Systematic Guide to Apply Non-parametric Test Procedures
6.5 Some Frequently Used Non-parametric Test Methods
6.5.1 Single Sample Problem
6.5.2 Comparing Two Dependent Samples
6.5.3 Comparing Two Independent Samples: The Mann-Whitney U-Test
6.5.4 Comparing Three or More Independent Samples: The Kruskal- Wallis Test
6.6 Conclusion
Exercise
Further Reading
7: Analysis of Variance (ANOVA) and Design of Experiments
Key Topics
7.1 Assumptions of ANOVA
7.2 Hypotheses and Procedure of ANOVA
7.3 One-Way ANOVA
7.4 Two-Way ANOVA
7.5 Repeated Measures ANOVA (RMANOVA)
7.6 Analysis of Variance and Experimental Designs
7.7 Experimental Designs
7.7.1 Principles of Experimental Design
7.8 Completely Randomized Design (CRD)
7.9 Randomized Block Design (RBD)
7.10 Conclusion
Exercise
Further Reading
8: Concept of Simple Correlation and Regression
Key Topics
8.1 Simple Correlation Coefficient
8.1.1 Some Properties of Correlation Coefficient
8.1.2 Distinction Between r and r2
8.2 Test of Significance of Correlation Coefficient
8.3 Non-parametric Correlation or Rank Correlation
SAS Code for Karl Pearson´s Correlation Coefficient Analysis
8.4 Concept of Regression Analysis
8.4.1 Properties of Regression Coefficient
8.4.2 Testing of Regression Parameters
8.4.3 Model Accuracy Measurement and Outlier Detection
SAS Code for Regression Analysis
8.5 Conclusion
Exercise
Further Reading
9: Analysis and Interpretation of Weight-Length Data of Fish
Key Topics
9.1 Concept of Weight-Length Relationship
9.2 Interpretation of Parameters
9.3 Condition Factor
9.4 Determining WLR from Observed Data
9.4.1 Analysis of Weight-Length Data Using R Software
9.5 Determine Fulton´s and Relative Condition Factor
9.5.1 Plotting Condition Factors Against Length
9.6 Conclusion
Exercise
Further Reading
10: Some Multivariate Analysis Techniques for Fisheries Data
Key Topics
10.1 Principal Component Analysis
10.1.1 PCA Workflow
10.1.2 Real Data Example
10.1.2.1 Questions
10.1.2.2 Step-by-Step Analysis
10.1.2.3 PCA Through SAS
Data Set Specification
Analytical Detail Specification
10.2 Multiple Regression Analysis
10.2.1 Working Principle
10.2.2 Workflow of Multiple Regression Analysis
10.2.3 Real Data Example
10.2.3.1 Systematic Analysis
10.2.3.2 Interpretation of R2
10.2.3.3 Interpretation of Regression Coefficients
10.2.3.4 Relative Importance of Independent Variables
10.3 Conclusion
Exercise
Further Reading
11: Introduction to Electronic Spreadsheet and Microsoft Excel
Key Topics
11.1 What Is a Spreadsheet?
11.2 Spreadsheet Development Timeline
11.2.1 Beginnings and the `Tale of VisiCalc´
11.2.2 Daniel Fylstra
11.2.3 After VisiCalc?
11.2.4 Lotus 1-2-3
11.2.5 Microsoft Excel
11.3 Other Electronic Spreadsheets
11.3.1 Google Docs
11.3.2 OpenOffice
11.3.3 Microsoft Excel
11.3.4 Excel Web Application
11.3.5 Zoho Sheet
11.4 Electronic Spreadsheets and Microsoft Excel
11.4.1 Uses of an Electronic Spreadsheet
11.4.2 Advantages of Using an Electronic Spreadsheet
11.4.3 Anatomy of an Excel Program Window
11.4.3.1 Some Terminologies
11.5 Functions Related to Inland Fisheries Management
11.5.1 Data Validation
11.6 Data Entry by Creating Forms
11.7 Conclusion
Exercise
Further Reading
12: Introduction to R Software
Key Topics
12.1 Why Use R?
12.1.1 Programming Features of R
12.2 Getting Started
12.2.1 Installation (for Windows Users)
12.2.1.1 To Install R
12.2.1.2 To Install RStudio
12.2.1.3 To Install the SDSFoundations Package
12.2.2 Working with R Scripts
12.2.3 R Graphical User Interface (RGui)
12.2.4 RStudio
12.2.5 R Scripting
12.2.6 Sourcing a Script in R
12.3 Arithmetic and Logical Operators
12.3.1 Arithmetic Operators
12.3.2 Comments
12.3.3 Logical Operators
12.4 Data Types and Objects in R
12.4.1 Scalars
12.4.1.1 Number
12.4.1.2 Logical Value
12.4.1.3 Characters and Strings
12.4.2 Vectors
12.4.3 Matrix
12.4.4 Data Frames
12.4.5 Lists
12.4.6 Objects
12.5 Handling Data Frames
12.5.1 Creating a Data Frame
12.5.2 Summary of Data
12.5.3 Extracting Data from a Data Frame
12.5.4 Expanding the Data Frame
12.5.4.1 Adding Columns
12.5.4.2 Adding Rows
12.6 Importing Data from Files
12.6.1 Working Directory
12.6.2 Importing from Excel
12.6.3 Importing from Table File
12.6.4 Importing from CSV Files
12.7 Conclusions
Exercise
Further Reading
13: Introduction to Graphics in R
Key Topics
13.1 Standard Graphics in R
13.1.1 Titles, Legends and Annotations Titles
13.1.1.1 Legend
13.1.1.2 Text in the Margin and in the Graph
13.1.1.3 Annotations and Customization
13.1.1.4 Types of Plots
13.1.1.5 Axes
13.1.1.6 Axis Label Style
13.1.1.7 Colours
13.1.1.8 Plotting Curve
13.2 Statistical Graphics Gallery
13.2.1 Line Plot
13.2.2 Scatter Plot
13.2.2.1 Logarithmic Scale
13.2.3 Scatter Plot Smoothing
13.2.4 Box Plots
13.2.5 Histogram Plot
13.2.6 Histogram with Density Estimate
13.2.7 Normal Q-Q Plot
13.2.8 Empirical Distribution Function Plot
13.2.9 Bivariate Contour Plot
13.3 Conclusion
Exercise
Further Reading
Appendix: Answers of All Exercises
Chapter 2
Answer of MCQ
Chapter 3
Chapter 4
Chapter 5
Chapter 6
Chapter 7
Chapter 8
Chapter 9
Chapter 10
Chapter 11
Chapter 12
Chapter 13
