Applied statistics for environmental science with R [electronic resource] / Abbas F. M. Alkarkhi, Wasin A. A. Alqaraghuli.
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 m...
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Full Text (via ScienceDirect) |
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| Format: | Electronic eBook |
| Language: | English |
| Published: |
San Diego :
Elsevier,
2019.
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| Subjects: |
MARC
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| 100 | 1 | |a Alkarkhi, Abbas F. M., |e author. | |
| 245 | 1 | 0 | |a Applied statistics for environmental science with R |h [electronic resource] / |c Abbas F. M. Alkarkhi, Wasin A. A. Alqaraghuli. |
| 260 | |a San Diego : |b Elsevier, |c 2019. | ||
| 300 | |a 1 online resource (242 pages) : |b illustrations (black and white) | ||
| 336 | |a text |b txt |2 rdacontent. | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. 6.4.1. One-Way Multivariate Analysis of Variance. | |
| 520 | |a 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. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Environmental sciences |x Statistical methods. |0 http://id.loc.gov/authorities/subjects/sh2009103465. | |
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407. | |
| 650 | 7 | |a Environmental sciences |x Statistical methods. |2 fast |0 (OCoLC)fst00913513. | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207. | |
| 700 | 1 | |a Alqaraghuli, Wasin A. A., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Alkarkhi, Abbas F. M. |t Applied statistics for environmental science with R. |d Amsterdam : Elsevier, [2020] |z 9780128186220 |w (OCoLC)1090982590. |
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