A study of statistical problem solving and methodology including
organization and
presentation of data, probability, statistical distributions, confidence
intervals and
hypothesis testing. Emphasis will be on conceptual understanding,
computational
procedures and interpretation of results rather than theoretical development.
Statistical software will be utilized in the analysis of data.
Prerequisite: MAT 1010 or equivalent.
(NUMERICAL DATA; COMPUTER)
A supervised experience in the instructional process on the university
level through
direct participation in a classroom situation. Grading will be on a
satisfactory/unsatisfactory
basis only.
Prerequisite: Junior or senior standing. May be repeated for a total
credit of three
semester hours.
A continuation of STT 2810. A study of both parametric and
non-parametric statistical methods and inferential procedures. Topics
include robust procedures for single parameter inference; techniques for
comparing two distributions; inference in the simple regression model based on
least squares analysis; robust alternatives to least squares line fitting;
error rates and power. Emphasis is on a non-theoretical development of
statistical techniques and on the interpretation of statistical results.
Statistical software will be utilized in the analysis of data. Prerequisite:
STT 2810 or equivalent. (NUMERICAL DATA; COMPUTER)
A continuation of STT 3820. A study of both parametric and
non-parametric statistical methods and inferential procedures. Topics
include: contingency table analysis; analysis of variance; experimental
design; selected topics from multiple regression; error rates and power.
Emphasis is on a non-thoeretical development of statistical techniques and on
the interpretation of statistical results. Statistical software will be
utilized in the analysis of data.
Prerequisite: STT 3820 or permission of the
instructor.
(NUMERICAL DATA; COMPUTER)
The course begins with an introduction to discrete probabilities and
related
applications. In particular, the application of probability to sampling is
studied in detail.
The remainder of the course is devoted to the theory of sampling and
sampling techniques.
Applications are highlighted through examples and illustrated problems.
Prerequisite: STT 2810 or permission of the
instructor.
(WRITING)
An introduction to
statistical methods and probability modeling. Topics include data
analysis, axiomatic probability, random variables, standard discrete and
continuous random variables, sampling distributions, and statistical
inference. Statistical software will be utilized for analyzing data and
for simulating probability and sampling distributions.
Prerequisite: MAT 1020.
(NUMERICAL DATA; COMPUTER)
An introduction to probability modeling. Topics include a study of
sample spaces,
counting rules, conditional probability and independence, random variables
and their
properties, and applications.
The course begins with a review of sampling, sampling distributions,
and simple
comparative experiments. Single factor experiments with both fixed and
random effects are
considered. Designs illustrated include randomized blocks, Latin squares,
and factorial
experiments. Mixed models and rules for expected mean square are
presented. Model
adequacy, sample size considerations, power determinations and restrictions on
randomization procedures are discussed. The use of statistical software
packages is
integrated throughout the course.
Prerequisite: STT 3820 or permission of instructor.
(WRITING)
An introduction to least squares estimation in simple and multiple
regression
models. The matrix approach is used in the more general multiple
regression model.
Considerable attention is given to the analysis of variance, aptness of the
model tests,
residual analysis, the effects of multicollinearity, and variable selection
procedures.
Prerequisites: MAT
2240 and STT 3820. (NUMERICAL DATA;
COMPUTER; WRITING)
An introduction to the mathematical principles of statistical
inference. Topics
include a study of sampling theory, point and interval estimation, and
hypothesis testing.
A study of mathematical statistics to include such topics as sampling
distributions,
consistency, best asymptotic normal estimators, sufficiency, maximum
likelihood
estimation, Bayes' estimators, confidence intervals and tests of hypotheses.
A course designed to provide majors in statistics and other related
fields the
opportunity to study statistics problems from a variety of sources and to
examine their
statistical analyses. The emphasis will be on the oral and written
presentations of statistical
results. The course should prepare the student for making the transition
from academic
courses to statistical practice. Students taking this course should have
completed most of
the courses offered in the statistics curriculum.
Prerequisite: permission of instructor. (WRITING, SPEAKING)