Geog 5190 Quantitative Techniques

Geog 5190 Quantitative Techniques (3 credit hrs)

Miguel F. Acevedo, University of North Texas

Syllabus, Spring 07

Group one core course in the Environmental Science MS and PhD program
Required core course for the MS in Applied Geography

This course is now on WebCT Vista.
See below for Calendar by week

Objectives

Understand the theoretical background underlying statistical and data analysis techniques
Understand the assumptions and validity conditions for statistical tests and data analysis techniques
Employ software for statistics and data analysis
Practice how to compile data sets from disparate sources and make them ready for analysis
Understand methods and techniques to work with spatial data and time series

General Information

Class Meetings: Thursdays 6-7:20 pm; EESAT (ENV) 391, 7:30-8:50 pm EESAT 336 (CSAM)
Instructor: Miguel F. Acevedo, Office hours: EESAT 310H, Th 4:30-5:30 pm or by appointment.

Phone: 565-2381 Email acevedo@unt.edu

Teaching Assistant: Heinrich Goetz, Office hours: EESAT 334 Tu 4-5 pm, Wd 3-3:50 pm, Thur 4:30-5:30 pm

Phone 565-4775 E-mail hegoetz@unt.edu

Grade: 70% graded homework assignments. 30% project.

All grades for the course will be final. No extra credit assignments or work will be considered after the final grade has been recorded.
Project: Hands-on data analysis. Topic is student's choice in consultation with the instructor.
Evaluation of the project is based on a written report and a brief oral presentation.
Homework assignments will be submitted by 6pm every thursday. This can be submitted using Vista or hard copy.

Policy on Cheating and Plagiarism. See below.
Required Background: algebra, functions, statistics, probability, calculus, computer skills. We will review some of these topics.

Software:

To use class notes and lab guides, you would need a web browser, internet access and the adobe acrobat reader.
For data analysis we will mainly use R, a GNU open-source software, and the RCmdr package
R is available in the CSAM labs and CAS software links.
If you want to use R off-campus or from your office: Download R from the Comprehensive R Archive Network (CRAN) web site http://cran.us.r-project.org/
You can use SPPS if you are familiar with this software, but some exercises cannot be completed in SPSS
If you want to use SPSS off-campus or from your office: abuy SPPS student license from univesity bookstore

Textbooks:

Required: Acevedo. Lecture notes. Provided in pdf format.
pdf files are portable document files; they can be viewed and printed using the Acrobat Reader
Software Manuals: Online manuals for the R program available via the help menu item
Optional readings: (in reserve at the library http://www.library.unt.edu/ search on course reserves)

Davis J.C. 2002. Statistics and Data Analysis in Geology. Third Edition. Wiley.
Rogerson P.A. 2001. Statistical Methods for Geography. Second Edition. Sage Publications.
Griffith D.A. and C.G. Amrhein. 1991. Statistical Analysis for Geographers. Prentice Hall
Carr J.R. 1995. Numerical Analysis for the Geological Sciences. Prentice Hall

Software manuals are very useful to supplement the computer sessions:

CRAN. 2003. An introduction to R: Online manual with the R program available via the help menu item.
Deutsch C.V. and A.G. Journel. 1992. GSLIB Geostatistical Software Library and User's Guide. Oxford University Press.
MathSoft Inc. 1999. S-PLUS 2000 Guide to Statistics Volume 1 and Volume 2.

Several introductory tests can be used for review. For example:

Drake A. 1967. Fundamentals of applied probability theory. MacGraw Hill.
Mann Prem S. 1998. Introductory Statistics. Third Edition. Wiley.
Sullivan M. 2004. Statistics, Informed Decisions using data. Prentice Hall.
Stockburger D.W. 1996. Introductory Statistics: Concepts, Models, and Applications A web based book

Remote access to the course and distance learning:

To take this class remotely for credit: enroll regularly but you will not be required to attend class.
You will study the book and conduct the computer exercises on your own.
The final grade will be calculated using the same system as those taking the class in the conventional manner.
The homework assignments will be submitted by 6pm every thursday in electronic form using Vista.
Homework assignments will be provided in the Vista system announcements.
You will be required to progress at the same rate as the calendar given below.
To access the lab files you would need a web browser and Internet access.
To write assignments you will need to scan your handwritten exercises and type the computer exercises.
You will be able to run on your own machine the GNU free software (R system), and software that you most likely already have.
If you want to enroll in another Thursday evening class you will have to get help from Tami or Eva in the Geog. Dept to override the time conflict.
Please notify me by E-mail. after you enroll in the class and decide to follow this distance learning option and let me know whether you are enrolled in another Thursday evening class.

ADA:

The Department of Geography, in cooperation with the Office of Disability Accommodation, complies with the Americans with Disabilities Act in making reasonable accommodations for qualified students with disabilities. Please present your written accommodation request before the 12th class day.

Statement on Cheating and Plagiarism Students caught cheating or plagiarizing will receive a "0" for that particular assignment or exam. Additionally, the incident will be reported to the Office of Student Rights and Responsibilities for further penalty. According to the UNT catalog, the term "cheating" includes, but is not limited to:

use of any unauthorized assistance in taking quizzes, tests, or examinations
dependence upon the aid of sources beyond those authorized by the instructor in writing papers, preparing reports, solving problems, or carrying out other assignments
the acquisition, without permission, of tests or other academic material belonging to a faculty or staff member of the university
dual submission of a paper or project, or resubmission of a paper or project to a different class without express permission from the instructor(s); or
any other act designed to give a student an unfair advantage.

The term "plagiarism" includes, but is not limited to:

the knowing or negligent use by paraphrase or direct quotation of the published or unpublished work of another person without full and clear acknowledgment; and
the knowing or negligent unacknowledged use of materials prepared by another person or agency engaged in the selling of term papers or other academic materials.

Calendar. We will cover one unit per week

Book chapters will be updated during the semester.

Unit 1

Introduction to the course, Probability Theory. Events, trees, Bayes theorem.
Introduction to the lab. Use of R system: running programs, file management, text editor, windows, and libraries.
Some basic notions of SPSS.

Unit 2

Descriptive statistics. Random variables (RV), Distributions and density. Histograms.
Distributions and its moments, discrete and continuous RV, functions of RV.
Law of large numbers, central limit theorem, statistics, sample mean and variance.
Random Number Generation.

Unit 3

Exploratory Data Analysis, Univariate. Bivariate. Quantile-quantile plots, boxplots, auto-correlation
Inferential statistics, Normal distribution.
Hypothesis testing. Parametric (e.g., t and F tests), and nonparametric (Wilcoxon, Spearman).
Correlation. Pearson

Unit 4

Inferential statistics (continued); Goodness of Fit (GOF), classical (chi square) and nonparametric (Kolmogorov-Smirnov) methods.
Review of counts and proportions, contingency tables.
Analysis of Variance (ANOVA), classical and nonparametric (Kruskal-Wallis and Friedman) methods

Unit 5

Spatial Point Pattern analysis uniform, clustered.
Testing: quadrat analysis, chi-square, Poisson
Testing: nearest-neighbors, G and K functions
Spatial analysis: Geostatistics. Variograms: covariance and semivariance. Variogram models.

Unit 6

Matrix algebra. Review of linear algebra. Operations, determinant, inverse. Solving linear equations.
Simple regression analysis. Bivariate regression. Linear and nonlinear. Polynomial regression

Unit 7

Multivariate regression Least squares multiple regression. Stepwise.
Discriminant analysis, MANOVA.
Time series: auto-regressive models. AR

Unit 8

Geostatistics: ordinary and universal kriging and trends.

Unit 9

Spatial analysis: Lattice data.
Neighbor structure
Spatial autocorrelation (Moran and Geary)
Spatial regression models (SAR).

Unit 10

Time series analysis: auto-regressive and moving avergae models ARMA.
Spectral analysis.

Unit 11

Eigenvalues and eigenvectors. Singular value decomposition
Multivariate analysis eigenvector methods, Principal components (PCA)

Unit 12

Factor analysis (FA)
Correspondence analysis.

Unit 13

Multivariate analysis (continued). Multiple discriminant functions.
Canonical analysis: canonical correlation, canonical correspondence.
Cluster analysis.