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Viewing: EMSE 4765 : Data Analysis for Engineers and Scientists

Last approved: Fri, 14 Apr 2017 08:02:10 GMT

Last edit: Sun, 05 Mar 2017 21:57:29 GMT

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School of Engineering and Applied Sciences
Engineering Management and Systems Engineering (EMSE)
EMSE
4765
Data Analysis for Engineers and Scientists
Data Analysis Engrs/Scientists
Spring 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade
undergraduate students majoring in systems engineering or with the permission of the instructor
No
No
APSC 3115
Corequisites

25
J. van Dorp
Frequency of Offering
Every Year
Term(s) Offered
Spring
Are there Course Equivalents?
No
 
No
Fee Type


No


Inference methods in a single dimension: estimation, confidence intervals, hypothesis testing and goodness-of-fit testing; multivariate data analysis techniques using matrices and vectors: the Hotelling T-squared test, multiple linear regression and principle component analysis.
Students who complete this course should be able to: (1) accurately apply classical univariate statistical inference techniques, (2) accurately apply a variety of multivariate analysis methods (3) execute the statistical analyses discussed using Microsoft EXCEL and MINITAB, (4) clearly and concisely present the insights obtained from the statistical analysis in a report.
Uploaded a Course Syllabus

Course Attribute
CCAS - Professional

riffat (Wed, 15 Feb 2017 16:28:17 GMT): Rollback: Please include Independent Learning statement in syllabus.
Key: 2738