Basic Statistics for Environmental Studies: A DistanceLearning Course
Richard Cellarius, Ph.D.,
email: rcellarius@prescott.edu
; Phone:
9287786724; Web:
http://myweb.cableone.net/rcellarius/statistics
Texts:
Triola, Mario F. (2001).
Elementary Statistics Using Excel . (1^{st}
Ed,)
[This edition is specifically required.]
Moore,
David S.(2001). Statistics:
Concepts and Controversies
(5^{th} Ed.).
[This edition is specifically required.]
Tools: Hand calculator with statistical functions (graphing calculator not required); Microsoft Excel spreadsheet program
Credit: 23 semester credits, depending on number of topics completed, Minimum 2 credits for Topics 13; 1 credit additional for Topics 45 combined.
I have prepared a series of five reading and problem assignments based on the above texts. In each assignment, I provide a brief overview of the essential points to focus on. I also provide a series of PowerPoint presentations and video files that assist in the elaboration of the concepts and problemsolving techniques. The problems from the texts are to be worked either on paper and hand calculator or using Excel. For the full 3credit course, these will be due as indicated on the attached schedule, usually about 3 weeks from when the assignment is made, based on a time commitment of about 910 hours per week. Some folks will find a slower pace will fit their other obligations better; however, completion of Topics 13 is essential for a basic understanding of the statistical approach to data analysis. The instructor will review your work and return it promptly. Specific details with be worked out on an individual basis. Contact the Prescott College Master of Arts Program (MAP) office or Environmental Studies Faculty for information on setting up the course and access to the course materials.
IMPORTANTPLEASE
NOTE:
The recommended process is for a student to
include the course on his or her enrollment form and in his or
her semester's study plan. It is up to the student and advisor
to determine the process for evaluation. A set of pdf answer files
is available from me. They are password protected, and once
you
have finished one of
the five topics, the student or advisor can contact me for the
specific file and the password to the
answer file(s) for that topic. The
recommended evaluation process is when the student has
completed a topic, the student and advisor evaluate the
student's answers using the answer key(s). The
student's final selfevaluation for the course should
summarize what the student learned and how the
learning was accomplished. The advisor's evaluation could support that
conclusion based on the student's reporting of the student's overall
performance. One simple alternative for stronger evaluation is to have
your GA review and correct your answers to the problems specifically
identified as "selfevaluation" at the end of Topics 3 etc. 
NOTE: Right click on topic number to download a pdf file
with the topic instructions; right click on supplementary file names to
download
those needed files.
Topic
1 : Overview; Descriptive Statistics  Sampling,
the Nature of Data,
Displaying Data
This first installment introduces some of the initial concepts of data acquisition and description . This topic covers a great many topics that might be review of previous learning, at least in part. The fundamental issue here is to understand the different types of data and how data should properly be obtained and displayed. Data can often be displayed in a way that misleads or hides critical information, and it is important to be able to recognize this and evaluate those data more correctly. Contact
Topic 2 : Probability and Probability Distributions; Sampling Distributions and the Central Limit Theorem
Probability is one of the most fundamental concepts underlying statistical analysis: given a number of events, what is the probability that a specific event will occur or, if there are repetitive events, what is the proportion of a specific kind of event that is likely to occur. For statistics, probability can be approached with a fullblown coverage of probability analysis or with a survey of the basic fundamentals sufficient to demonstrate that the statistical procedures encountered later have an adequate mathematical basis. I have chosen a route closer to the latter, which does not require learning elaborate notations or doing excessive calculations. The ultimate conclusions to focus on are the nature of probability distributions including the Normal Distribution, sampling distributions , and the Central Limit Theorem , which provides the fundamental rationale for relying on random samples of a population to make statistical conclusions about the entire population. (Supplementary files The Uniform Distribution.pdf , ExcelCentral Limit Theorem.pdf , and 16Sheets.xls )
Topic 3 : Introduction to Inferential Statistics  Confidence Intervals; Hypothesis Testing and Tests of Significance
The
two parts of this Topic
introduce the essential concepts and tools of Inferential
Statistics
, the science and art of making conclusions about populations
from statistical samples of
those populations. In
the first part, the task is to determine the probable
range within which the "true" population value of
the quantity in question
lies based on analysis of the sample data.
In the
second part, the process is extended to make comparative statements or
conclusions
based on hypotheses about the population, again on
the basis of the
sample data from that population.
Topic 4 : More Tests of Hypotheses: Inferences from Two Samples; Multinomial Experiments and Contingency Tables
The
two tools covered in
this Topic are among the most widely used statistical procedures. The first, "Inferences from Two Samples," generally follows
quite directly and quite simply from the hypothesis testing of a single
sample
that was the subject of the previous topic; here one of two questions
is
asked: (1) "are the means of the populations represented by
two different
samples the same or different?" or (2) "has the
population changed as the
result of the treatment?"
The second tool involves making inferences from data
gathered when there are a number of categories,
for example races of people or varieties of trees.
Again
we will consider two different types of questions, (1) "how
does the distribution
in the categories compare with an assumed distribution?"
which applies when
you're dealing with a sample from a single populationa multinomial
experimentoften referred to as a ChiSquare
Test by biologists,
or (2) "are the distributions among the various categories
the same or different
in the different samples?" which involves analysis of contingency
tables.
Topic
5 : Two Important Analysis Tools: Analysis of Variance
(ANOVA) and Correlation
& Regression
The two tools described here are again two of the most important tools used in statistical analysis, and for the purposes of this course, they complete our study of basic statistical analysis techniques. W cover them in the reverse order that they are discussed in the text, primarily because Analysis of Variance (ANOVA) is a logical extension to multiple samples of the hypothesis testing of sample data encountered in one and two sample tests (Topics 3 and 4). In contrast Correlation and Regression deal with the analysis of a different type of data: pairs of sample data, such as weight and height of individuals, weight and girth of bears, or time and distance for a mode of travel or a race. (Supplementary file Chs 11 & 9.xls )

Click here to view
possible schedule (pdf file).

Posted January 24, 2002, by Richard Cellarius
New
links added March 17,
2002 and August 16, 2002; Minor editing changes made August 5, 2003;
February
10, 2004., May 4, 2006; January 25, 2007, November 8, 2007,
March 1, 2011