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E-raamat: Data Analysis and Statistics for Geography, Environmental Science, and Engineering

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(University of North Texas, Denton, USA)
  • Formaat: 557 pages
  • Ilmumisaeg: 07-Dec-2012
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781466592216
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Data Analysis and Statistics for Geography, Environmental Science, and EngineeringSuurem pilt
  • Formaat: 557 pages
  • Ilmumisaeg: 07-Dec-2012
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781466592216
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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 authors 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.
Preface xv
Acknowledgments xix
Author xxi
PART I Introduction to Probability, Statistics, Time Series, and Spatial Analysis
Chapter 1 Introduction
3(26)
1.1 Brief History of Statistical and Probabilistic Analysis
3(1)
1.2 Computers
4(1)
1.3 Applications
4(1)
1.4 Types of Variables
4(2)
1.4.1 Discrete
5(1)
1.4.2 Continuous
5(1)
1.4.3 Discretization
5(1)
1.4.4 Independent vs. Dependent Variables
6(1)
1.5 Probability Theory and Random Variables
6(1)
1.6 Methodology
6(1)
1.7 Descriptive Statistics
7(1)
1.8 Inferential Statistics
7(1)
1.9 Predictors, Models, and Regression
7(1)
1.10 Time Series
8(1)
1.11 Spatial Data Analysis
8(1)
1.12 Matrices and Multiple Dimensions
8(1)
1.13 Other Approaches: Process-Based Models
9(1)
1.14 Baby Steps: Calculations and Graphs
9(2)
1.14.1 Mean, Variance, and Standard Deviation of a Sample
9(1)
1.14.2 Simple Graphs as Text: Stem-and-Leaf Plots
10(1)
1.14.3 Histograms
11(1)
1.15 Exercises
11(1)
1.16 Computer Session: Introduction to R
11(18)
1.16.1 Working Directory
11(1)
1.16.2 Installing R
11(1)
1.16.3 Personalize the R GUI Shortcut
11(2)
1.16.4 Running R
13(1)
1.16.5 Basic R Skills
13(2)
1.16.6 R Console
15(1)
1.16.7 Scripts
15(1)
1.16.8 Graphics Device
16(1)
1.16.9 Downloading Data Files
17(1)
1.16.10 Read a Simple Text Data File
17(2)
1.16.11 Simple Statistics
19(1)
1.16.12 Simple Graphs as Text: Stem-and-Leaf Plots
20(1)
1.16.13 Simple Graphs to a Graphics Window
20(1)
1.16.14 Addressing Entries of an Array
20(2)
1.16.15 Example: Salinity
22(1)
1.16.16 CSV Text Files
23(1)
1.16.17 Store Your Data Files and Objects
24(1)
1.16.18 Command History and Long Sequences of Commands
25(1)
1.16.19 Editing Data in Objects
25(1)
1.16.20 Cleanup and Close R Session
26(1)
1.16.21 Computer Exercises
26(1)
Supplementary Reading
27(2)
Chapter 2 Probability Theory
29(30)
2.1 Events and Probabilities
29(1)
2.2 Algebra of Events
29(2)
2.3 Combinations
31(1)
2.4 Probability Trees
32(1)
2.5 Conditional Probability
33(1)
2.6 Testing Water Quality: False Negative and False Positive
34(1)
2.7 Bayes' Theorem
35(1)
2.8 Generalization of Bayes' Rule to Many Events
36(1)
2.9 Bio-Sensing
36(1)
2.10 Decision Making
37(2)
2.11 Exercises
39(1)
2.12 Computer Session: Introduction to Rcmdr, Programming, and Multiple Plots
40(19)
2.12.1 R Commander
40(1)
2.12.2 Package Installation and Loading
40(3)
2.12.3 R GUI SDI Option: Best for R Commander
43(1)
2.12.4 How to Import a Text Data File Using Rcmdr
43(2)
2.12.5 Simple Graphs on a Text Window
45(1)
2.12.6 Simple Graphs on a Graphics Window: Histograms
46(1)
2.12.7 More than One Variable: Reading Files and Plot Variables
47(1)
2.12.7.1 Using the R Console
48(3)
2.12.7.2 Using the R Commander
51(2)
2.12.8 Programming Loops
53(1)
2.12.9 Application: Bayes' Theorem
54(1)
2.12.10 Application: Decision Making
55(1)
2.12.11 More on Graphics Windows
55(1)
2.12.12 Editing Data in Objects
56(1)
2.12.13 Clean Up and Exit
56(1)
2.12.14 Additional GUIs to Use R
57(1)
2.12.15 Modifying the R Commander
57(1)
2.12.16 Other Packages to Be Used in the Book
57(1)
2.12.17 Computer Exercises
58(1)
Supplementary Reading
58(1)
Chapter 3 Random Variables, Distributions, Moments, and Statistics
59(36)
3.1 Random Variables
59(1)
3.2 Distributions
59(4)
3.2.1 Probability Mass and Density Functions (pmf and pdf)
59(3)
3.2.2 Cumulative Functions (cmf and cdf)
62(1)
3.2.3 Histograms
62(1)
3.3 Moments
63(5)
3.3.1 First Moment or Mean
63(1)
3.3.2 Second Central Moment or Variance
64(2)
3.3.3 Population and Sample
66(1)
3.3.4 Other Statistics and Ways of Characterizing a Sample
67(1)
3.4 Some Important RV and Distributions
68(4)
3.5 Application Examples: Species Diversity
72(1)
3.6 Central Limit Theorem
72(1)
3.7 Random Number Generation
73(1)
3.8 Exercises
74(1)
3.9 Computer Session: Probability and Descriptive Statistics
75(20)
3.9.1 Descriptive Statistics: Categorical Data, Table, and Pie Chart
75(3)
3.9.2 Using a Previously Generated Object or a Dataset
78(1)
3.9.3 Summary of Descriptive Statistics and Histogram
78(3)
3.9.4 Density Approximation
81(1)
3.9.5 Theoretical Distribution: Example Binomial Distribution
82(4)
3.9.6 Application Example: Species Diversity
86(1)
3.9.7 Random Number Generation
86(3)
3.9.8 Comparing Sample and Theoretical Distributions: Example Binomial
89(1)
3.9.9 Programming Application: Central Limit Theorem
90(2)
3.9.10 Sampling: Function Sample
92(1)
3.9.11 Cleanup and Close R Session
92(1)
3.9.12 Computer Exercises
93(1)
Supplementary Reading
93(2)
Chapter 4 Exploratory Analysis and Introduction to Inferential Statistics
95(42)
4.1 Exploratory Data Analysis (EDA)
95(3)
4.1.1 Index Plot
95(1)
4.1.2 Boxplot
95(1)
4.1.3 Empirical Cumulative Distribution Function (ecdf)
96(2)
4.1.4 Quantile-Quantile (q - q) Plots
98(1)
4.1.5 Combining Plots for Exploratory Data Analysis (EDA)
98(1)
4.2 Relationships: Covariance and Correlation
98(4)
4.2.1 Serial Data: Time Series and Autocorrelation
101(1)
4.3 Statistical Inference
102(7)
4.3.1 Hypothesis Testing
103(2)
4.3.2 p-Value
105(1)
4.3.3 Power
105(2)
4.3.4 Confidence Intervals
107(2)
4.4 Statistical Methods
109(1)
4.5 Parametric Methods
110(2)
4.5.1 Z Test or Standard Normal
110(1)
4.5.2 The t-Test
110(1)
4.5.3 The F Test
111(1)
4.5.4 Correlation
112(1)
4.6 Nonparametric Methods
112(1)
4.6.1 Mann-Whitney or Wilcoxon Rank Sum Test
112(1)
4.6.2 Wilcoxon Signed Rank Test
112(1)
4.6.3 Spearman Correlation
112(1)
4.7 Exercises
113(1)
4.8 Computer Session: Exploratory Analysis and Inferential Statistics
113(24)
4.8.1 Create an Example Dataset
113(1)
4.8.2 Index Plot
113(1)
4.8.3 Boxplot
114(1)
4.8.4 Empirical Cumulative Plot
114(1)
4.8.5 Functions
115(1)
4.8.6 Building a Function: Example
115(1)
4.8.7 More on the Standard Normal
116(2)
4.8.8 Quantile-Quantile (q-q) Plots
118(1)
4.8.9 Function to Plot Exploratory Data Analysis (EDA) Graphs
119(1)
4.8.10 Time Series and Autocorrelation Plots
120(1)
4.8.11 Additional Functions for the Rconsole and the R Commander
121(1)
4.8.12 Parametric: One Sample t-Test or Means Test
122(2)
4.8.13 Power Analysis: One Sample t-Test
124(2)
4.8.14 Parametric: Two-Sample t-Test
126(2)
4.8.15 Power Analysis: Two Sample t-Test
128(1)
4.8.16 Using Data Sets from Packages
129(1)
4.8.17 Nonparametric: Wilcoxon Test
130(2)
4.8.18 Bivariate Data and Correlation Test
132(3)
4.8.19 Computer Exercises
135(1)
Supplementary Reading
136(1)
Chapter 5 More on Inferential Statistics: Goodness of Fit, Contingency Analysis, and Analysis of Variance
137(40)
5.1 Goodness of Fit (GOF)
137(4)
5.1.1 Qualitative: Exploratory Analysis
137(1)
5.1.2 x2 (Chi-Square) Test
137(3)
5.1.3 Kolmogorov-Smirnov (K-S)
140(1)
5.1.4 Shapiro-Wilk Test
140(1)
5.2 Counts and Proportions
141(1)
5.3 Contingency Tables and Cross-Tabulation
141(3)
5.4 Analysis of Variance
144(7)
5.4.1 ANOVA One-Way
145(3)
5.4.2 ANOVA Two-Way
148(1)
5.4.3 Factor Interaction in ANOVA Two-Way
149(1)
5.4.4 Nonparametric Analysis of Variance
150(1)
5.5 Exercises
151(2)
5.6 Computer Session: More on Inferential Statistics
153(24)
5.6.1 GOF: Exploratory Analysis
153(1)
5.6.2 GOF: Chi-Square Test
154(1)
5.6.3 GOF: Kolmogorov-Smirnov Test
155(1)
5.6.4 GOF: Shapiro-Wilk
156(1)
5.6.5 Count Tests and the Binomial
156(1)
5.6.6 Obtaining a Single Element of a Test Result
157(1)
5.6.7 Comparing Proportions: prop.test
158(1)
5.6.8 Contingency Tables: Direct Input
159(1)
5.6.9 Contingency Tables: Cross-Tabulation
160(2)
5.6.10 ANOVA One-Way
162(4)
5.6.11 ANOVA Two-Way
166(3)
5.6.12 ANOVA Nonparametric: Kruskal-Wallis
169(3)
5.6.13 ANOVA Nonparametric: Friedman
172(1)
5.6.14 ANOVA: Generating Fictional Data for Further Learning
172(3)
5.6.15 Computer Exercises
175(1)
Supplementary Reading
176(1)
Chapter 6 Regression
177(48)
6.1 Simple Linear Least Squares Regression
177(18)
6.1.1 Derivatives and Optimization
178(2)
6.1.2 Calculating Regression Coefficients
180(3)
6.1.3 Interpreting the Coefficients Using Sample Means, Variances and Covariance
183(1)
6.1.4 Regression Coefficients from Expected Values
184(1)
6.1.5 Interpretation of the Error Terms
185(3)
6.1.6 Evaluating Regression Models
188(4)
6.1.7 Regression through the Origin
192(3)
6.2 ANOVA as Predictive Tool
195(1)
6.3 Nonlinear Regression
196(4)
6.3.1 Log Transform
197(1)
6.3.2 Nonlinear Optimization
197(1)
6.3.3 Polynomial Regression
198(1)
6.3.4 Predicted vs. Observed Plots
198(2)
6.4 Computer Session: Simple Regression
200(25)
6.4.1 Scatter Plots
200(2)
6.4.2 Simple Linear Regression
202(4)
6.4.3 Nonintercept Model or Regression through the Origin
206(2)
6.4.4 ANOVA One Way: As Linear Model
208(3)
6.4.5 Linear Regression: Lack-of-Fit to Nonlinear Data
211(3)
6.4.6 Nonlinear Regression by Transformation
214(2)
6.4.7 Nonlinear Regression by Optimization
216(3)
6.4.8 Polynomial Regression
219(2)
6.4.9 Predicted vs. Observed Plots
221(1)
6.4.10 Computer Exercises
221(2)
Supplementary Reading
223(2)
Chapter 7 Stochastic or Random Processes and Time Series
225(34)
7.1 Stochastic Processes and Time Series: Basics
225(1)
7.2 Gaussian
225(2)
7.3 Autocovariance and Autocorrelation
227(4)
7.4 Periodic Series, Filtering, and Spectral Analysis
231(7)
7.5 Poisson Process
238(3)
7.6 Marked Poisson Process
241(6)
7.7 Simulation
247(2)
7.8 Exercises
249(1)
7.9 Computer Session: Random Processes and Time Series
250(9)
7.9.1 Gaussian Random Processes
250(2)
7.9.2 Autocorrelation
252(1)
7.9.3 Periodic Process
252(1)
7.9.4 Filtering and Spectrum
253(1)
7.9.5 Sunspots Example
254(1)
7.9.6 Poisson Process
255(1)
7.9.7 Poisson Process Simulation
255(1)
7.9.8 Marked Poisson Process Simulation: Rainfall
256(1)
7.9.9 Computer Exercises
257(1)
Supplementary Reading
258(1)
Chapter 8 Spatial Point Patterns
259(50)
8.1 Types of Spatially Explicit Data
259(1)
8.2 Types of Spatial Point Patterns
259(1)
8.3 Spatial Distribution
259(1)
8.4 Testing Spatial Patterns: Cell Count Methods
260(4)
8.4.1 Testing Uniform Patterns
260(1)
8.4.2 Testing for Spatial Randomness
261(2)
8.4.3 Clustered Patterns
263(1)
8.5 Nearest-Neighbor Analysis
264(4)
8.5.1 First-Order Analysis
264(2)
8.5.2 Second-Order Analysis
266(2)
8.6 Marked Point Patterns
268(1)
8.7 Geostatistics: Regionalized Variables
269(1)
8.8 Variograms: Covariance and Semivariance
270(4)
8.8.1 Covariance
271(1)
8.8.2 Semivariance
272(2)
8.9 Directions
274(2)
8.10 Variogram Models
276(5)
8.10.1 Exponential Model
276(2)
8.10.2 Spherical Model
278(1)
8.10.3 Gaussian Model
278(1)
8.10.4 Linear and Power Models
279(1)
8.10.5 Modeling the Empirical Variogram
280(1)
8.11 Exercises
281(3)
8.12 Computer Session: Spatial Analysis
284(25)
8.12.1 Packages and Functions
284(1)
8.12.2 File Format
284(1)
8.12.3 Creating a Pattern: Location-Only
285(1)
8.12.4 Generating Patterns with Random Numbers
286(2)
8.12.5 Grid or Quadrat Analysis: Chi-Square Test for Uniformity
288(1)
8.12.6 Grid or Quadrat Analysis: Randomness, Poisson Model
289(1)
8.12.7 Nearest-Neighbor Analysis: G and K Functions
290(3)
8.12.8 Monte Carlo: Nearest-Neighbor Analysis of Uniformity
293(1)
8.12.9 Marked Spatial Patterns: Categorical Marks
294(4)
8.12.10 Marked Spatial Patterns: Continuous Values
298(3)
8.12.11 Marked Patterns: Use Sample Data from sgeostat
301(4)
8.12.12 Computer Exercises
305(1)
Supplementary Reading
306(3)
PART II Matrices, Tempral and Spatial Autoregressive Processes, and Multivariate Analysis
Chapter 9 Matrices and Linear Algebra
309(24)
9.1 Matrices
309(1)
9.2 Dimension of a Matrix
309(1)
9.3 Vectors
310(1)
9.4 Square Matrices
310(2)
9.4.1 Trace
311(1)
9.4.2 Symmetric Matrices: Covariance Matrix
311(1)
9.4.3 Identity
312(1)
9.5 Matrix Operations
312(7)
9.5.1 Addition and Subtraction
312(1)
9.5.2 Scalar Multiplication
313(1)
9.5.3 Linear Combination
313(1)
9.5.4 Matrix Multiplication
313(2)
9.5.5 Determinant of a Matrix
315(1)
9.5.6 Matrix Transposition
316(1)
9.5.7 Major Product
316(1)
9.5.8 Matrix Inversion
317(2)
9.6 Solving Systems of Linear Equations
319(2)
9.7 Linear Algebra Solution of the Regression Problem
321(2)
9.8 Alternative Matrix Approach to Linear Regression
323(2)
9.9 Exercises
325(1)
9.10 Computer Session: Matrices and Linear Algebra
326(7)
9.10.1 Creating Matrices
326(1)
9.10.2 Operations
327(3)
9.10.3 Other Operations
330(1)
9.10.4 Solving System of Linear Equations
331(1)
9.10.5 Inverse
331(1)
9.10.6 Computer Exercises
332(1)
Supplementary Reading
332(1)
Chapter 10 Multivariate Models
333(36)
10.1 Multiple Linear Regression
333(9)
10.1.1 Matrix Approach
333(5)
10.1.2 Population Concepts and Expected Values
338(1)
10.1.3 Evaluation and Diagnostics
339(1)
10.1.4 Variable Selection
340(2)
10.2 Multivariate Regression
342(2)
10.3 Two-Group Discriminant Analysis
344(5)
10.4 Multiple Analysis of Variance (MANOVA)
349(4)
10.5 Exercises
353(2)
10.6 Computer Session: Multivariate Models
355(14)
10.6.1 Multiple Linear Regression
355(4)
10.6.2 Multivariate Regression
359(2)
10.6.3 Two-Group Linear Discriminant Analysis
361(2)
10.6.4 MANOVA
363(2)
10.6.5 Computer Exercises
365(1)
10.6.6 Functions
365(2)
Supplementary Reading
367(2)
Chapter 11 Dependent Stochastic Processes and Time Series
369(36)
11.1 Markov
369(9)
11.1.1 Dependent Models: Markov Chain
369(2)
11.1.2 Two-Step Rainfall Generation: First Step Markov Sequence
371(1)
11.1.3 Combining Dry/Wet Days with Amount on Wet Days
371(3)
11.1.4 Forest Succession
374(4)
11.2 Semi-Markov Processes
378(3)
11.3 Autoregressive (AR) Process
381(6)
11.4 ARMA and ARIMA Models
387(2)
11.5 Exercises
389(1)
11.6 Computer Session: Markov Processes and Autoregressive Time Series
389(16)
11.6.1 Weather Generation: Rainfall Models
389(2)
11.6.2 Semi-Markov
391(1)
11.6.3 AR(p) Modeling and Forecast
392(3)
11.6.4 ARIMA(p, d, q) Modeling and Forecast
395(3)
11.6.5 Computer Exercises
398(2)
11.6.6 SEEG Functions
400(3)
Supplementary Reading
403(2)
Chapter 12 Geostatistics: Kriging
405(24)
12.1 Kriging
405(1)
12.2 Ordinary Kriging
405(8)
12.3 Universal Kriging
413(1)
12.4 Data Transformations
414(1)
12.5 Exercises
414(1)
12.6 Computer Session: Geostatistics, Kriging
415(14)
12.6.1 Ordinary Kriging
415(2)
12.6.2 Universal Kriging
417(5)
12.6.3 Regular Grid Data Files
422(3)
12.6.4 Functions
425(3)
12.6.5 Computer Exercises
428(1)
Supplementary Reading
428(1)
Chapter 13 Spatial Auto-Correlation and Auto-Regression
429(26)
13.1 Lattice Data: Spatial Auto-Correlation and Auto-Regression
429(1)
13.2 Spatial Structure and Variance Inflation
429(1)
13.3 Neighborhood Structure
429(3)
13.4 Spatial Auto-Correlation
432(2)
13.4.1 Moran's
432(1)
13.4.2 Transformations
433(1)
13.4.3 Geary's c
434(1)
13.5 Spatial Auto-Regression
434(2)
13.6 Exercises
436(1)
13.7 Computer Session: Spatial Correlation and Regression
437(18)
13.7.1 Packages
437(1)
13.7.2 Mapping Regions
438(2)
13.7.3 Neighborhood Structure
440(1)
13.7.4 Structure Using Distance
441(4)
13.7.5 Structure Based on Borders
445(1)
13.7.6 Spatial Auto-Correlation
446(2)
13.7.7 Spatial Auto-Regression Models
448(3)
13.7.8 Neighborhood Structure Using Tripack
451(1)
13.7.9 Neighborhood Structure for Grid Data
452(1)
13.7.10 Computer Exercises
453(1)
Supplementary Reading
454(1)
Chapter 14 Multivariate Analysis I: Reducing Dimensionality
455(46)
14.1 Multivariate Analysis: Eigen-Decomposition
455(1)
14.2 Vectors and Linear Transformation
455(1)
14.3 Eigenvalues and Eigenvectors
455(4)
14.3.1 Finding Eigenvalues
457(1)
14.3.2 Finding Eigenvectors
458(1)
14.4 Eigen-Decomposition of a Covariance Matrix
459(6)
14.4.1 Covariance Matrix
459(2)
14.4.2 Bivariate Case
461(4)
14.5 Principal Components Analysis (PCA)
465(4)
14.6 Singular Value Decomposition and Biplots
469(3)
14.7 Factor Analysis
472(3)
14.8 Correspondence Analysis
475(4)
14.9 Exercises
479(1)
14.10 Computer Session: Multivariate Analysis, PCA
480(21)
14.10.1 Eigenvalues and Eigenvectors of Covariance Matrices
480(1)
14.10.2 PCA: A Simple 2x2 Example Using Eigenvalues and Eigenvectors
481(2)
14.10.3 PCA: A 2 × 2 Example
483(2)
14.10.4 PCA Higher-Dimensional Example
485(1)
14.10.5 PCA Using the Rcmdr
486(4)
14.10.6 Factor Analysis
490(3)
14.10.7 Factor Analysis Using Rcmdr
493(2)
14.10.8 Correspondence Analysis
495(4)
14.10.9 Computer Exercises
499(1)
Supplementary Reading
500(1)
Chapter 15 Multivariate Analysis II: Identifying and Developing Relationships among Observations and Variables
501(20)
15.1 Introduction
501(1)
15.2 Multigroup Discriminant Analysis (MDA)
501(1)
15.3 Canonical Correlation
502(3)
15.4 Constrained (or Canonical) Correspondence Analysis (CCA)
505(1)
15.5 Cluster Analysis
506(2)
15.6 Multidimensional Scaling (MDS)
508(1)
15.7 Exercises
509(1)
15.8 Computer Session: Multivariate Analysis II
509(12)
15.8.1 Multigroup Linear Discriminant Analysis
509(5)
15.8.2 Canonical Correlation
514(1)
15.8.3 Canonical Correspondence Analysis
515(1)
15.8.4 Cluster Analysis
516(2)
15.8.5 Multidimensional Scaling (MDS)
518(2)
15.8.6 Computer Exercises
520(1)
Supplementary Reading
520(1)
Bibliography 521(4)
Index 525
Miguel F. Acevedo has 38 years of academic experience, the last 20 of these as faculty member of the University of North Texas (UNT). His career has been interdisciplinary, especially at the interface of science and engineering. He obtained his Ph.D. in biophysics from the University of California Berkeley and master's degrees in electrical engineering and computer science from Berkeley and the University of Texas at Austin, respectively. Prior to UNT, he was at the Universidad de Los Andes in Merida, Venezuela, where he taught for 18 years. He has served on the Science Advisory Board of the U.S. Environmental Protection Agency and on many review panels of the U.S. National Science Foundation. He has received numerous research grants and written many journal articles, book chapters, and proceedings articles. UNT has recognized him with the Regents Professor rank, the Citation for Distinguished Service to International Education, and the Regents Faculty Lectureship. For more information, see Dr. Acevedos page at UNT.
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