Syllabus
GEOG 090: Quantitative Methods in Geography
Spring 2005
Instructor: David Tenenbaum
Course
Description:
This course provides an introduction to univariate and bivariate statistics. An emphasis is placed upon the application of statistical approaches to geographic problems. In the first section of the course, descriptive univariate statistics are developed and their advantages and disadvantages are discussed. Various types of spatial distributions are identified and spatial statistics utilized to quantitatively assess their pattern. The fundamentals of probability are discussed briefly and used to introduce probability distributions.
In the second section of the course, sampling designs are discussed and methods of hypothesis testing described. First, however, data quality issues are addressed, as well as the misuse of statistics in research. The central limit theorem is discussed and utilized to establish a theoretical basis for sampling and hypothesis testing. Various parametric tests are used to identify the significance levels of differences between sample means, deviations, distributions, etc. In the latter portions of the course, multivariate relationships are introduced and explored through the use of correlation and linear regression procedures. The influences of spatial autocorrelation, data aggregation and sample size on multivariate relationships are identified.
As the course emphasizes the application of geographic
problems, homework exercises using geographic data will be assigned
regularly. Each student will learn
to use the Data Analysis plug-in for Microsoft Excel to solve statistical problems.
Office
hours:
1:00 – 3:00 PM Monday
3:30 – 5:00 PM Tuesday
Other times by appointment only.
325 Saunders Hall
Email: davidten@email.unc.edu
Phone: 843-4762
Final course grades will be determined using the following formula:
Test 1 (Thursday, February 17, 2005): 15%
Test 2 (Thursday, March 31, 2005): 15%
Open Book Final Exam: 30%
Exercises: 40%
Note:
No make up tests are given. Any student failing to take an exam will score zero
for that test unless a legitimate, documented reason is presented. In the
latter case the student will have the average score from their other exams
added in place of the missing test.
Required
Textbook:
Peter A. Rogerson. 2001. Statistical
Methods for Geography. Great
Britain: Sage.
Topics (Tentative, subject
to change) Chapter:Pages
Introduction to the course and
statistical thinking 1:1-4,12-13
Data types, geographical primitives, and data portrayal Lecture
Special consideration for spatial data 1:13-15
Review of algebraic and sigma notation 2:18-23
Measures of central tendency (mean,
median, mode) 1:5-6
Measures of disperson (variance, standard
deviation etc.) 1:6-7
Skewness and kurtosis 1:7-8
Introduction to probability
distributions 2:23-25
Discrete (Uniform, Binomial & Poisson)
distributions 2:25-27
Continuous (normal) distributions & the
central limit theorem 2:27-31
Review for test on February 17, 2005
Sources of data, data quality, sampling
methods 3:57-58
Confidence intervals, hypothesis testing
(part 1) 2:30-31,
3:42-46
Hypothesis testing (part 2) and levels of
significance 3:42-46
Computing test statistics (Z-tests,
t-tests, F-test), (part 2) 3:46-54
Comparing groups of observations (ANOVA) 4:65-70
Review for test on March 31, 2005
Covariance and the Pearson Correlation
Coefficient 5:86-94
Nonparameteric correlation: Spearman’s rank corr. coef. 5:94-95
Regression Analysis: Least squares method, testing r2, 6:104-118
assumptions,
testing regression parameters
Point Pattern Analysis 8:154-164
Geographic Patterns in Areal Data 8:164-173
Final Exam Review (part1, part2)
Assignment #1: Scales of Measurement, Geographic Primitives and Data Portrayal
Due:
Thursday, February 3, 2005 at the beginning of class
Assignment #2: Simple Descriptive Statistics
Assignment #3: Probability, Discrete, and Continuous Distributions
Due: Thursday,
March 3, 2005 at the beginning of class
Assignment #4: Sampling, Confidence Intervals, and Hypothesis Testing
Assignment #5: More Hypothesis Testing
Assignment #6: Correlation and Regression