EMSE 6765
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DATA
ANALYSIS FOR ENGINEERS AND SCIENTISTS:
Course Description:
Students
are expected to review prerequisite probability
calculus on their own. Some statistical review is provided in the
first three lectures. Topics that will be discussed include estimation,
confidence intervals, hypothesis testing and goodness-of-fit testing.
These methods perform statistical inference in a single dimension (also
known as univariate data analysis). Discussions of multivariate data analysis
utilize matrices and vectors. One class will review rules of matrix-vector
algebra and provides some intuitive geographical interpretations of these
operations. Multivariate data analysis will be introduced by first discussing
the classical Hotelling T2 hypothesis test, which is a natural extension
of the univariate T test. Next, we introduce regression nalysis (in matrix-vector
format), and analysis of variance. 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.
Required Textbooks:
None - Only Lecture Notes
Required Software:
MINITAB,
MicroSoft
Excel. Student are encouraged to learn R and run the R-scripts
made available on the left.
Recommended
Text Books: Neither
textbooks below are required. However, electronic lecture notes used throughout
this course are developed from these course texts. Reading accompanying
chapter from these texts may further enhance understanding.
- "A
Modern Introduction to Probability and Statistics, Understanding Why
and How" by F.M. Dekking, C. Kraaikamp, H.P. Lopuhaä and
L.E. Meester, Springer-Verlag, 2005.
- "Analyzing
Multivariate Data” by Lattin, Carroll and Green.
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