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Applied Statistics for Environmental Science with R [Mīkstie vāki]

(Universiti Kuala Lumpur (Unikl), Malaysia), (Consultant, Malaysia)
  • Formāts: Paperback / softback, 240 pages, height x width: 276x216 mm, weight: 660 g, Approximately 45 black and white illustrations and 35 black and white tables; Illustrations, unspecified
  • Izdošanas datums: 13-Sep-2019
  • Izdevniecība: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128186224
  • ISBN-13: 9780128186220
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  • Cena: 124,93 €
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Applied Statistics for Environmental Science with RPalielināt
  • Formāts: Paperback / softback, 240 pages, height x width: 276x216 mm, weight: 660 g, Approximately 45 black and white illustrations and 35 black and white tables; Illustrations, unspecified
  • Izdošanas datums: 13-Sep-2019
  • Izdevniecība: Elsevier Science Publishing Co Inc
  • ISBN-10: 0128186224
  • ISBN-13: 9780128186220
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780128186220.html
Keywords:

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.

  • Includes step-by-step tutorials to aid in understanding the process and implementation of unique data
  • Presents statistical theory in a simple way without complex mathematical proofs
  • Shows how to analyze data using R software and provides R scripts for all examples and figures
Preface ix
1 Multivariate Data
Learning Objectives
1(1)
1.1 The Concept of Environmental Statistics
1(1)
1.2 The Concept of Multivariate Analysis
1(1)
1.3 Configuration of Multivariate Data
2(1)
1.4 Examples of Multivariate Data
2(7)
1.5 Multivariate Normal Distribution
9(1)
Further Reading
10(1)
2 R Statistical Software
Learning Objectives
11(1)
2.1 Introduction
11(1)
2.2 Installing R
12(3)
2.3 The R Console
15(1)
2.4 Expression and Assignment in R
16(2)
2.5 Variables and Vectors in R
18(4)
2.6 Basic Definitions
22(1)
2.7 Plots in R
23(1)
2.8 RStudio
24(2)
2.9 Importing Data
26(1)
Further Reading
27(2)
3 Statistical Notions
Learning Objectives
29(1)
3.1 Introduction
29(1)
3.2 The Concept of Statistics
29(1)
3.3 Common Concepts
30(1)
3.4 Data Gathering
31(1)
3.5 Sampling Methods
31(2)
Further Reading
33(2)
4 Measures of Center and Variation
Learning Objectives
35(1)
4.1 Introduction
35(1)
4.2 Measures of Center and Dispersion in R
36(1)
4.3 Measures of Center
37(5)
4.4 Measure of Variation
42(1)
4.5 The Concept of Covariance
43(1)
4.6 Correlation Analysis
44(5)
4.7 Scatter Diagram
49(4)
4.8 Euclidean Distance
53(2)
Further Reading
55(2)
5 Statistical Hypothesis Testing
Learning Objectives
57(1)
5.1 Introduction
57(1)
5.2 Statistical Hypothesis Testing in R
57(1)
5.3 Common Steps for Hypothesis Testing
58(3)
5.4 Hypothesis Testing for a Mean Value
61(14)
5.5 Hypothesis Testing for Two Population Means
75(10)
Further Reading
85(2)
6 Multivariate Analysis of Variance
Learning Objectives
87(1)
6.1 Introduction
87(1)
6.2 Analysis of Variance in R
87(1)
6.3 The Concept of Analysis of Variance
88(12)
6.4 The Concept of Multivariate Analysis of Variance
100(12)
Further Reading
112(1)
7 Regression Analysis
Learning Objectives
113(1)
7.1 The Concept of Regression Analysis
113(1)
7.2 Regression Models in R
114(1)
7.3 The Concept of Simple Linear Regression Model
114(13)
7.4 Multiple Linear Regression Model
127(1)
7.5 Hypothesis Testing for Multiple Linear Regression
128(1)
7.6 Adjusted Coefficient of Determination
129(2)
7.7 Multivariate Multiple Linear Regression Model
131(1)
Further Readings
132(1)
8 Principal Components
Learning Objectives
133(1)
8.1 Introduction
133(1)
8.2 Principal Components Analysis in R
133(2)
8.3 Describing Principal Components
135(1)
8.4 Common Procedure for Computing Principal Components
136(1)
8.5 Extract Principal Components from Correlation Matrix
136(1)
8.6 Standardization
136(1)
8.7 Selecting the Number of Components
137(12)
Further Reading
149(2)
9 Factor Analysis
Learning Objectives
151(1)
9.1 Introduction
151(1)
9.2 Factor Analysis in R
151(1)
9.3 General Model for Factor Analysis
152(1)
9.4 Common Steps for Factor Analysis
153(18)
Further Reading
171(2)
10 Discriminant Analysis
Learning Objectives
173(1)
10.1 Introduction
173(1)
10.2 Discriminant Analysis in R
173(2)
10.3 Configuration of Discriminant Analysis Data
175(1)
10.4 The Concept of Discriminant Function
175(2)
10.5 Allocation
177(13)
Further Reading
190(1)
11 Clustering Approaches
Learning Objectives
191(1)
11.1 What is Cluster Analysis?
191(1)
11.2 Cluster Analysis in R
191(1)
11.3 Measures of Distance
192(1)
11.4 Clustering Procedures
193(11)
Further Reading
204(1)
Appendix 205(22)
Index 227
Abbas F. M. Alkarkhi received his Ph.D. in applied statistics from the University of Science, Malaysia and his BSc and MSc in statistics from University of Baghdad in 1985 and 1992. Dr. Alkarkhi spent fourteen years as a faculty member in the School of Industrial Technology at University of Science, Malaysia (2002-2016), then moved to Kuala Lumpur University (MICET campus). Before joining a Ph.D. study, he worked as a lecturer in Iraq for two years and in Libya for five years. Dr. Alkarkhi has published more than 90 papers in international journals and more than 40 in conferences. He is the author of two books on applied statistics, including Easy Statistics for Food Science with R (Elsevier 2018). Wasin Alqaraghuli received her BSc and MSc in statistics from Al-Mustansirya University. She worked at a specialized institute for engineering industries in Iraq, and during this time, she conducted training in statistical methods. She also worked at the University level in Iraq, Jordan and then Libya after receiving her MSc in statistics. In 2014, Dr. Alqaraghuli received her Ph. D from the school of mathematical sciences at the University of Science, Malaysia. Dr. Alqaraghulis research is focused on the application of experimental design, modeling, and multivariate statistics. She is currently self-employed with Skill Education Center, conducts workshops for non-statisticians and collaborates with other researchers to carry out research. Dr. Alqaraghuli is published in numerous international journals and is the co-author of Easy Statistics for Food Science with R (Elsevier 2018).