Department of Engineering Management
and Systems Engineering
School of Engineering and Applied Science
Syllabus: EMSE 171/271
Semester: Fall 2008
Page 1 of 4
EMSE 171/271 – Data Analysis
for Engineers and Scientists
Instructor Information:
Dr. J. Ren
é
van Dorp  Professor
Office Address: 1776 G Street, Office 135, Washington DC 20052
Telephone Number: 2029946638
Fax Number: 2029940245
Email:
[email protected]
Office Hours:
Tuesday
1:00PM to 3:00PM
Course Description:
A detailed statistical review is provided in the first four lectures. Topics that will be discussed include
point estimation, confidence intervals, hypothesis testing and goodnessoffit testing. These methods
perform statistical inference in a single dimension (also known as univariate data analysis).
Discussion of multivariate data analysis requires the introduction of matrices and vectors. One class
will review rules of matrixvector algebra and provides intuitive geographical interpretations of these
operations. Multivariate data analysis will be introduced by first discussing the classical Hotelling T
2
hypothesis test, which is a natural extension of the univariate T test. Next, we introduce regression
nalysis (in matrixvector format), principal component analysis and analysis of variance (ANOVA).
The introduction of these topics will be cursory and their application will be facilitated by the use of
the MINITAB software program. Discussion of these multivariate techniques will concentrate on
intuition, not a rigorous derivation of their methodologies.
Prerequisite Requirement:
ApSc 115: Engineering Analysis III (or any other undergraduate Applied Statistics course from a
physical or natural sciences program).
Course Objectives:
Each student should: (1) Get a solid refresher on classical statistical inference techniques, (2)
become familiar with matrixvector operations, (3) get an intuitive feel for a variety of multivariate
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 Spring '09
 Statistics, Multivariate statistics, LN Session, AMD LN Session, Applied Science Semester, Systems Engineering School

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