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.

1. "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.
2. "Analyzing Multivariate Data” by Lattin, Carroll and Green.