Lecture 1 - ENGR-2600 Modeling and Analysis of Uncertainty...

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1 ENGR-2600 Modeling and Analysis of Uncertainty Instructor: Charles J. Malmborg, Professor, Decision Sciences and Engineering Systems Office: CII 5015, ( malmbc@rpi.edu ) Phone: 518-276-2935, Fax: X-8227 Office Hrs: TuF: 1:30 PM - 2:30 PM TA’s and TA Office Hours: TBA ENGR-2600 Modeling and Analysis of Uncertainty To Access the Detailed Course Outline you can sign on WebCT at: http://rpilms.rpi.edu/webct/entryPageIns.dowebct If you have trouble signing on, please request that your name be added to section 4 of ENGR-2600 by reporting a “RPI LMS Problem” at: http://lmssupport.rpi.edu/setup.do DeVore J.L., Probability and Statistics for Engineering and the Sciences , Seventh Edition, Thomson Learning, 2008. It will be helpful to have the text handy during class. You will not need your laptop. TEXT Course Objective This course is designed to provide an appreciation and hands-on introduction to the methodologies of statistics and probability needed by engineers. In addition to developing an understanding of underlying principles, the course seeks to develop the student's ability to apply these techniques. Grading in the course will be based on the following*: Exam 1 20% Exam 2 20% Exam 3 20% Final Exam 20% Course Project 20% Homework 10% *Least favorable exam counts as 10% Grading Exam/Homework Class Cancellation Policy: If there is a snow day or cancellation of classes for any reason on the class prior to an exam, that exam and the corresponding homework due date will be postponed until the class following the originally scheduled exam date. If there is a class cancellation on a scheduled exam day, that exam will be given and the corresponding assignment will be due during the next class. In the event of a class lost due to an unanticipated cancellation, the review lecture 25 may be used as a regular class to catch up with course material.
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2 Course Project: Working individually, students must identify a research problem related to their anticipated engineering or other professional discipline. This data can be generated as part of the study. For example, if a student anticipates working in an experimentalist discipline, he/she might utilize data from an experimental setting, (e.g., readings of lab instrumentation). If a student anticipates a career in a theoretical discipline of engineering, he/she might utilize data collected via direct observation, (e.g., number of vehicles per unit time arriving to the intersection of 15th Street and Peoples Avenue), or data retrieved from historical sources, (e.g., maximum snow loads recorded in the Albany area from 1956 through 2006). Course Project: Focusing on the selected problem, the objective of the project is to formulate meaningful hypotheses about relationships between variables that can be investigated through analysis of the data using appropriate probability and/or statistical methodologies. The analysis can focus on any of the techniques covered in the course as long as it is related to your anticipated engineering discipline and appropriate for the problem studied. The project will
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Lecture 1 - ENGR-2600 Modeling and Analysis of Uncertainty...

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