Figure Slides for Data Analysis and Statistics for Geography, Environmental Science, and Engineering

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines. Learn How to Use a Variety of Data Analysis and Statistics Methods Based on the author's many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods. Build from Fundamental Concepts to Effective Problem Solving Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results. This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work.

Table of Contents:
PART I Introduction to Probability, Statistics, Time Series, and Spatial Analysis Introduction Brief History of Statistical and Probabilistic Analysis Computers Applications Types of Variables Probability Theory and Random Variables Methodology Descriptive Statistics Inferential Statistics Predictors, Models, and Regression Time Series Spatial Data Analysis Matrices and Multiple Dimensions Other Approaches: Process-Based Models Baby Steps: Calculations and Graphs Exercises Computer Session: Introduction to R Supplementary Reading Probability Theory Events and Probabilities Algebra of Events Combinations Probability Trees Conditional Probability Testing Water Quality: False Negative and False Positive Bayes' Theorem Generalization of Bayes' Rule to Many Events Bio-Sensing Decision Making Exercises Computer Session: Introduction to Rcmdr, Programming, and Multiple Plots Supplementary Reading Random Variables, Distributions, Moments, and Statistics Random Variables Distributions Moments Some Important RV and Distributions Application Examples: Species Diversity Central Limit Theorem Random Number Generation Exercises Computer Session: Probability and Descriptive Statistics Example Binomial Supplementary Reading Exploratory Analysis and Introduction to Inferential Statistics Exploratory Data Analysis (EDA) Relationships: Covariance and Correlation Statistical Inference Statistical Methods Parametric Methods Nonparametric Methods Exercises Computer Session: Exploratory Analysis and Inferential Statistics Supplementary Reading More on Inferential Statistics: Goodness of Fit, Contingency Analysis, and Analysis of Variance Goodness of Fit (GOF) Counts and Proportions Contingency Tables and Cross-Tabulation Analysis of Variance Exercises Computer Session: More on Inferential Statistics Supplementary Reading Regression Simple Linear Least Squares Regression ANOVA as Predictive Tool Nonlinear Regression Computer Session: Simple Regression Supplementary Reading Stochastic or Random Processes and Time Series Stochastic Processes and Time Series: Basics Gaussian Autocovariance and Autocorrelation Periodic Series, Filtering, and Spectral Analysis Poisson Process Marked Poisson Process Simulation Exercises Computer Session: Random Processes and Time Series Supplementary Reading Spatial Point Patterns Types of Spatially Explicit Data Types of Spatial Point Patterns Spatial Distribution Testing Spatial Patterns: Cell Count Methods Nearest-Neighbor Analysis Marked Point Patterns Geostatistics: Regionalized Variables Variograms: Covariance and Semivariance Directions Variogram Models Exercises Computer Session: Spatial Analysis Supplementary Reading PART II Matrices, Tempral and Spatial Autoregressive Processes, and Multivariate Analysis Matrices and Linear Algebra Matrices Dimension of a Matrix Vectors Square Matrices Matrix Operations Solving Systems of Linear Equations Linear Algebra Solution of the Regression Problem Alternative Matrix Approach to Linear Regression Exercises Computer Session: Matrices and Linear Algebra Supplementary Reading Multivariate Models Multiple Linear Regression Multivariate Regression Two-Group Discriminant Analysis Multiple Analysis of Variance (MANOVA) Exercises Computer Session: Multivariate Models Supplementary Reading Dependent Stochastic Processes and Time Series Markov Semi-Markov Processes Autoregressive (AR) Process ARMA and ARIMA Models Exercises Computer Session: Markov Processes and Autoregressive Time Series Supplementary Reading Geostatistics: Kriging Kriging Ordinary Kriging Universal Kriging Data Transformations Exercises Computer Session: Geostatistics, Kriging Supplementary Reading Spatial Auto-Correlation and Auto-Regression Lattice Data: Spatial Auto-Correlation and Auto-Regression Spatial Structure and Variance Inflation Neighborhood Structure Spatial Auto-Correlation Spatial Auto-Regression Exercises Computer Session: Spatial Correlation and Regression Supplementary Reading Multivariate Analysis I: Reducing Dimensionality Multivariate Analysis: Eigen-Decomposition Vectors and Linear Transformation Eigenvalues and Eigenvectors Eigen-Decomposition of a Covariance Matrix Principal Components Analysis (PCA) Singular Value Decomposition and Biplots Factor Analysis Correspondence Analysis Exercises Computer Session: Multivariate Analysis, PCA Supplementary Reading Multivariate Analysis II: Identifying and Developing Relationships among Observations and Variables Introduction Multigroup Discriminant Analysis (MDA) Canonical Correlation Constrained (or Canonical) Correspondence Analysis (CCA) Cluster Analysis Multidimensional Scaling (MDS) Exercises Computer Session: Multivariate Analysis II Supplementary Reading Bibliography Index