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This course focuses on the building of empirical models through data in order to predict, explain, and interpret scientific phenomena. Regression modeling is the most widely used method for analyzing and predicting a response data and for understand the relationship with explanatory variables. This course provides both theoretical and practical training in statistical modeling with particular emphasis on simple linear, logistic and multiple regression, using R to develop and diagnose models. The course covers the theory of multiple regression and diagnostics from a linear algebra perspective with emphasis on the practical application of the methods to real data sets. The data sets will be taken from a wide variety of disciplines.
Grading: no pass/fail option,
no fifth course option
exams, homework, and a project
MATH 250 and at least one of STAT 201, 202 or 302. Or permission of instructor
This course prepares students in the use of quantitative methods for the modeling, prediction and understanding of scientific phenomena.