STA 567 - Applied Regression Models

[4 credit(s)]

Prerequisite: Student enrolled in MS in Mathematics with Specialization in Applied Statistics or Predictive Analytics Certificate programs or permission of instructor. Students will learn techniques, ideas, and concepts associated with linear regression. In the context of linear regression, they will learn how to use specific statistical methods and general modes of statistical thinking to make inferences from data. The emphasis is on being able to build an appropriate regression model, on being able to assess the adequacy of a proposed model, and on drawing and formulating conclusions about the fitted model. They will also learn how to assess the relative merits and applicability of competing statistical techniques. Students will learn how to perform the techniques covered in this course by using a statistical software package. Topics may also include transformations, matrix representation, non-linear regression and other topics as time allows. Credit cannot be earned for this course if the student has already taken STA 467.

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