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numerical investigations using Microsoft Excel. Much of the
material is highly technical.
Prerequisites: High comfort level with basic integral calculus,
and recent exposure to a formal course in probability and
statistics such as STAT 430 is strongly recommended.
STAT 510 / 430
Probability
Description: This course provides an introduction to the basic
tools needed to develop probability models that describe data.
The material of the course is best summarized with an example.
Suppose that we have just completed a marketing survey of
50 potential customers. As part of the survey, these customers
were asked if they would purchase a new product at a specific
price. Based on the results of this survey, the product manager
must arrive at a price for the product when national marketing
begins, and must also provide an estimate of ultimate sales.
This course develops models that provide a systematic approach
to this task and many other problems. The course seeks
to provide students with sufficient background so that they can
develop and apply probability models in varying domains. This
background includes understanding the essential ideas of probability
and randomness and the manipulation of and relationships
among various popular models.Topics in this course
include: Elements of matrix algebra; discrete and continuous
random variables and their distributions; moments and moment
generating functions; joint distributions; functions and
transformations of random variables; law of large numbers and
the central limit theorem; point estimation: sufficiency, maximum
likelihood, minimum variance, and confidence intervals.
Format: Lecture and discussion. Assigned and graded exercises,
midterm, and final exam.
Prerequisites: Students should be comfortable with basic
calculus.
STAT 520
Applied Econometrics I
Description: This is primarily a statistical methodology
course. Regression dominates the course, particularly multiple
regression, ANOVA and ANCOVA, and related models for
discrete-choice data, including logistic regression. Real data sets
will be used to demonstrate the methods and clarify the use of
the techniques. At the end of the semester, students are expected
to be able to analyze a data set (quantitative or qualitative)
wisely and make correct decisions. The course starts with an
introductory review of basis statistical techniques needed such
as the central limit theorem, confidence intervals, and hypotheses
tests. For computing, R and JMP will be used throughout
the course.
Topics in this course include: simple linear model, multiple
regression, ANOVA and ANCOVA, logistic regression,
and techniques for panel data.
Format: Exercises, midterm, and final exam.
Prerequisite: STAT 613.
STATISTICS
STAT 521
Applied Econometrics II
Description: This is a course in econometrics for graduate
students. The goal is to prepare students for empirical research
by studying econometric methodology and its theoretical foundations.
Students taking the course should be familiar with
elementary statistical methodology and basic linear algebra,
and should have some programming experience. Topics include
ordinary least squares estimation, the bootstrap and jackknife,
instrumental variables, systems of equations, M-estimation,
maximum likelihood, the generalized method of moments, discrete
response models, and time series analysis.
Prerequisites: STAT 520.
STAT 613
Regression Analysis for Business
Description: This course provides the fundamental methods
of statistical analysis, the art and science if extracting information
from data. The course will begin with a focus on the basic
elements of exploratory data analysis, probability theory and
statistic inference. With this as a foundation, it will proceed
to explore the use of the key statistical methodology known as
regression analysis for solving business problems, such as the
prediction of future sales and the response of the market to
price changes. The use of regression diagnostics and various
graphical displays supplement the basic numerical summaries
and provides insight into the validity of the models. Specific
important topics covered include least squares estimation, residuals
and outliers, tests and confidence intervals, correlation
and autocorrelation, collinearity, and randomization. The presentation
relies upon computer software for most of the needed
calculations, and the resulting style focuses on construction
of models, interpretation of results, and critical evaluation of
assumptions.
Format: Lecture and discussion, assigned exercises, data analysis
project, quizzes and a final exam.
Prerequisites: The basic mathematical skills covered in
STAT 611.
STAT 622 (.5 cu)
Statistical Modeling
Description: This six-week, elective MBA course continues
the required MBA statistics course, STAT 613. It expands the
material covered in STAT 613 in several ways, adding both
breadth (e.g., logistic regression) and depth to the coverage of
regression (e.g., more diagnostics, model selection). The course
emphasizes the models for decision making from large data
sets, as common in data-mining. Lectures feature extensive
analysis of large data sets from marketing, personal finance,
and management. The course presumes that students are familiar
with the inferential methods covered in STAT 613 (including
hypothesis tests, confidence intervals, p-values) as well as
the use and interpretation of least squares regression models.
The course also uses JMP as in STAT 613. Beginning with a
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