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Instructor: Chris Mack
Course Objective: Standard undergraduate treatments of data analysis and modeling include important basic ideas of regression and goodness of fit. However, several significant real-world issues are rarely addressed adequately in these introductory courses. This course will discuss many of the problems scientists and engineers routinely encounter when dealing with data and will provide rigorous methods for handling them: measurement uncertainty analysis, data flyer removal, impact of sampling on model quality, dealing with correlated inputs, and residual analysis. In the end, students should have the tools necessary to answer one of the foundational problems in science: given two competing scientific models (theories), does the data contain sufficient information to choose one over the other?
Review of Undergraduate Probability and Statistics
Here is an online review for a typical introductory undergraduate probability and statistics course.
Some optional textbooks that relate well to the topics of this course:
Douglas Montegomery and Elizabeth Peck, Introduction to Linear Regression Analysis, any edition, Wiley Series in Probability and Statistics.
Samprit Chatterjee and Ali S. Hadi, Regression Analysis by Example, any edition, Wiley Series in Probability and Statistics.
Some online texts that may prove useful:
Penn State course Stat501: Regression MethodsNIST Engineering Statistics Handbook
Course materials and video lectures
Course materials: click here.
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