Applied statistics for environmental science with R 2.3. The R Console2.4. Expression and Assignment in R; 2.5. Variables and Vectors in R; 2.5.1. Matrix in R; 2.6. Basic Definitions; 2.7. Plots in R; 2.8. RStudio; 2.8.1. Navigating RStudio; 2.9. Importing Data; Further Reading; Chapter 3: Statistical Notions; Learning Objectives; 3.1. Introduction; 3.2. The Concept of Statistics; 3.3. Common Concepts; 3.3.1. Qualitative Variables; 3.3.2. Quantitative Variables; 3.3.2.1. Discrete Variable; 3.3.2.2. Continuous Variable; 3.4. Data Gathering; 3.4.1. Approaches for Gathering Data; 3.5. Sampling Methods; 3.5.1. Simple Random Sampling Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods this book acts as a step by step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day to day research problems Front Cover; Applied Statistics for Environmental Science with R; Copyright; Dedication; Contents; Preface; Chapter 1: Multivariate Data; Learning Objectives; 1.1. The Concept of Environmental Statistics; 1.2. The Concept of Multivariate Analysis; 1.3. Configuration of Multivariate Data; 1.4. Examples of Multivariate Data; 1.5. Multivariate Normal Distribution; 1.5.1. Univariate Normal Distribution; 1.5.2. Multivariate Normal Distribution; Further Reading; Chapter 2: R Statistical Software; Learning Objectives; 2.1. Introduction; 2.2. Installing R; 2.2.1. R Material; 2.2.2. R Packages 4.7.1. The Scatter Diagram Matrix4.8. Euclidean Distance; Further Reading; Chapter 5: Statistical Hypothesis Testing; Learning Objectives; 5.1. Introduction; 5.2. Statistical Hypothesis Testing in R; 5.3. Common Steps for Hypothesis Testing; 5.3.1. The Concept of Null and Alternative Hypotheses; 5.3.2. Basic Concepts; 5.4. Hypothesis Testing for a Mean Value; 5.4.1. Hypothesis Testing for One Population Mean; 5.4.2. Hypothesis Testing for a Mean Vector for one Sample; 5.5. Hypothesis Testing for Two Population Means; 5.5.1. Hypothesis Testing for Two Population Means 3.5.2. Systematic Sampling3.5.3. Stratified Sampling; 3.5.4. Cluster Sampling; Further Reading; Chapter 4: Measures of Center and Variation; Learning Objectives; 4.1. Introduction; 4.2. Measures of Center and Dispersion in R; 4.3. Measures of Center; 4.3.1. The Arithmetic Mean for a Single Variable; 4.3.2. The Mean Vector (Multivariate); 4.4. Measure of Variation; 4.4.1. Variance and Standard Deviation for a Single Variable; 4.5. The Concept of Covariance; 4.5.1. Covariance Matrices (Multivariate); 4.6. Correlation Analysis; 4.6.1. Correlation Matrices; 4.7. Scatter Diagram 5.5.2. Hypothesis Testing for Mean Vectors for Two PopulationsFurther Reading; Chapter 6: Multivariate Analysis of Variance; Learning Objectives; 6.1. Introduction; 6.2. Analysis of Variance in R; 6.3. The Concept of Analysis of Variance; 6.3.1. One Way Analysis of Variance; 6.3.1.1. Hypothesis Testing for a One Way Analysis of Variance; 6.3.1.2. Explanation of the Analysis of Variance Results; 6.3.2. Two Way Analysis of Variance; 6.3.2.1. Hypothesis Testing for a Two way Analysis of Variance; 6.4. The Concept of Multivariate Analysis of Variance