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Within the mathematics program
exists sequence of pure and applied statistics
courses. They provide a sound basis for advanced
study in the discipline and can lead to a number
of interesting and rewarding career
options.
| Statistics
Courses |
| 125 Fundamentals of Statistics |
3 cr. |
| Exploratory
data analysis and statistical inference including
graphical summaries of data, sampling distributions,
confidence intervals, and hypothesis testing.
Credit not allowed for both 125/225. Example syllabus.
|
| 225 Introduction to Biostatistics |
3 cr. |
| Descriptive
statistics, sampling distributions, confidence
intervals, hypothesis testing, non-parametric
methods, chi-square tests, regression and
correlation methods, and analysis of variance.
Credit is not allowed for both 125/225.
Prerequisite: Evidence of college level algebra
skills. Example syllabus. |
| 301 Introduction to Probability and Statistics I |
3 cr. |
| Univariate
and multivariate probability distributions
of discrete and continuous random variables,
mathematical expectation, limit theorems.
Prerequisite: 116. Example syllabus.
|
| 302W Introduction to Probability and Statistics II |
3 cr. |
| A continuation
of 301 including
probability and sampling distributions of
random variables, confidence intervals, and
hypothesis testing. Prerequisite: 301.
|
| 325W Applied Statistics with Regression |
3 cr. |
| One-way, two-way
analysis of variance, Latin squares, methods
of multiple comparisons, analysis of covariance,
balanced and unbalanced designs, linear and
multiple regression. Prerequisite: 225, or 301, or
permission of instructor. |
| 335 Biostatistics II |
3 cr. |
|
This course is a continuation of
Math 225 (Introduction to
Biostatistics). Topics include
statistical issues in diagnostic
tests, contingency table analyses,
multiple two-by-two table analyses,
linear and multiple regression,
logistic regression, survival
analysis, and nonparametric
statistical procedures.
Example syllabus.
|
| 425W Experimental Design |
3 cr. |
| Factorial designs,
fixed and random effects models, nested and
nested-factorial designs, split-plot designs,
response surface designs. Prerequisite: 325W
or permission of instructor. |
Questions and Answers
A:
No. Currently, students may not major or minor
in statistics. However, there are a wide variety
of statistics courses offered in the department.
Many recent graduates with degrees in mathematics
have entered graduate school in statistics. Mathematics
majors who wish to attend graduate school in statistics
are not the only students who should consider
taking multiple upper division courses in statistics.
Students whose future job will involve data analysis,
students interested in research in a scientific
discipline, students who are required to take
research methods courses in their own disciplines,
and students who prefer applied mathematics over
theory may find the department's statistics courses
to be especially beneficial.
A:
The three introductory statistics courses are
quite different from one another. Fundamentals
of statistics, Math 125, is the least demanding
introductory statistics course and is aimed at
students in a variety of disciplines. Introductory
Biostatistics, Math 225, covers the material in
Math 125 and several other topics as well. Most
examples are from the health sciences and biology.
In addition to the regular lecture, Math 225 contains
a computer lab component in which students use
statistical software to augment learning. Each
of these two introductory courses has no prerequisites
beyond high school algebra.
The
third introductory statistics course, Introduction
to Probability and Statistics I, is a calculus-based
introduction to probability and statistics, focusing
primarily on probability and random variables.
Students majoring in mathematics, secondary mathematics
education, and computer science are the primary
target audience, but students in the physical
sciences may benefit. A year of calculus is a
prerequisite for the course.
A:
The table below summarizes the three regular upper
division statistics courses offered by the department.
| Course |
Topic |
Prerequisites |
|
|
|
|
| Math 302W
- Introduction to Probability and Statistics
II |
Mathematical
statistics |
Math 301 |
|
|
|
|
| Math 325W
- Applied Statistics with Regression |
Linear models
including ANOVA and regression |
Math 225
or Math 301 (or permission of instructor) |
|
|
|
|
| Math 425W
- Experimental Design |
Design of
experiments |
Math 325W
(or permission of instructor) |
Students
primarily interested in data analysis, especially
students from disciplines other than mathematics,
will likely be most interested in the Math 325W-425W
sequence. Students who are interesterd in graduate
school in the mathematical sciences may prefer
Math 302W. A student interested in graduate school
in statistics is encouraged to take all three
courses.
Furthermore,
the department occasionally offers special topics
courses in statistics. Within the recent past,
courses on Statistical Computing and the SAS programming
language have been offered.
A:
The Data Analysis Institute is directed by Dr.
Frank D'Amico. The Institute accepts projects
from both university and outside sources. Advanced
undergraduate students who have completed one
or more upper division course in statistics are
often hired by the Institute to assist in the
data analysis projects.
A:
Typically, graduate students in statistics (and
in all of the mathematical sciences) earn stipends
for working as teaching assistants (nominally
20 hours per week) that cover the cost of tuition,
fees, and books, with remaining money sufficient
to cover modest living expenses. The teaching
experience gained is invaluable for students who
desire careers in education, and is beneficial
for most others. A Master's program in statistics
typically requires two years of study, while a
doctoral program will likely require more than
four years of study and research.
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