LECTURE
SCHEDULE
Spring 2008
Class Meets:
Tuesday, 2:00 - 4:40 p.m., LD 004
Text: Statistics
and Data Analysis in Geology, 3rd Edition, by John C. Davis, Wiley, 2002.
Questions?
Comments? Send e-mail.
Date Topic Chapter (pages)
|
January 8 |
No Class - Download/Review Software | |
| 15 | Introduction + Descriptive Statistics | 1 (-10) 2 (11-54) |
| 22 | Normal Distribution and T-test | 2 (55-74) |
|
29 |
F-test and ANOVA | 2 (75-91) |
| February 5 | Chi-Square Test and Data Transformations | 2 (92-102) |
| 12 | Nonparametric Tests | 2 (102-111) |
| 19 | Runs Tests | 4 (185-190) |
| 26 | Regression Analysis | 4 (191-227) |
| March 4 | Auto- and Cross-Correlation & Runs Tests | 4 (243-253) |
| 11 | NO CLASS - SPRING BREAK | |
|
18 |
Discriminant Function & Cluster Analyses | 6 (471-499) |
|
25 |
Factor and Principal Components Analysis | 6 (500-547) |
| April 1 | Multiple Regression | 6 (461-470) |
| 8 | Distribution of Points on a Plane | 5 (293-315) |
| 15 | Analysis of Directional Data + Shape Analysis | 5(316-329; 355-358) |
| 22 | Research Reports |
Virtually every branch of science rigorously evaluates the statistical validity of patterns seen in data. The geological sciences have been somewhat slow in adopting statistical analyses. The goal of this course is to:
1. provide an review of basic statistics.
2. introduce you to data analysis using geoscience examples.
3. illustrate the importance of having a statistical model in mind prior to collecting your data – the reverse (data first, then statistical testing) isn’t always possible.
4. cover a broad range of techniques applicable to geoscience data, including non-parametric techniques and multivariate statistical analyses.
Program:
We will use a freely available, but very powerful, statistical analysis program called PAST (PAlaeontological STatistics). You can download it, along with program documentation, using this Link. I suggest that you subscribe to the PAST Mailing List by using the appropriate link on the main program page. You will receive frequent program updates and correspondence from other users regarding the application of particular statistical techniques that often are useful in your data analysis applications.
Web-Based Resources:
Please take advantage of the links to statistical resources. These include basic information on analytical techniques, software, and even statistical jokes! Many of the current links provide additional links to a variety of statistical topics. If you discover useful web pages for which links are not included on the web page, please let me know & I’ll add them.
Grading:
You should start to think about a data set that will be conducive to statistical analyses. The data could come from your advisor or, preferably, be data that you have collected for a project or (ideally) for your thesis. Having familiarity with the data increases the value of the techniques and analytical results. You will analyze the data and write a research report on your findings. The report should include all of the standard sections of a scientific paper, including an abstract, introduction, methods, results, and a conclusions or discussion section. If time permits, we’ll have a mini-GSA session where you’ll present your findings to the class using PowerPoint.
I will also supply questions or problem sets and ask you to apply specific tests to real data sets. These will also contribute to your final grade.
An Example of statistical inference - The Dead Grandmother/Exam Syndrome and the Potential Downfall of American Society
TUTORIAL DATA = Iris Data from SYSTAT