# Data Analysis and Statistics for Geography, Environmental Science, and Engineering

557 pages | 320 B/W Illus.

Hardback: 9781439885017
pub: 2012-12-07
\$112.95
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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.

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

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

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

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

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

Regression

Simple Linear Least Squares Regression

ANOVA as Predictive Tool

Nonlinear Regression

Computer Session: Simple Regression

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

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

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

Multivariate Models

Multiple Linear Regression

Multivariate Regression

Two-Group Discriminant Analysis

Multiple Analysis of Variance (MANOVA)

Exercises

Computer Session: Multivariate Models

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

Geostatistics: Kriging

Kriging

Ordinary Kriging

Universal Kriging

Data Transformations

Exercises

Computer Session: Geostatistics, Kriging

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

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

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

Bibliography

Index

#### Miguel F. Acevedo

Denton, Texas, USA