BABS 508 provides students with a theoretical understanding of the basis of regression techniques, as well as practice with the application of generalized linear models. These techniques are widely used in a variety of fields including business, economics, finance and operations research. The skills taught in this course are essential for any business analyst.
In this course, students will learn multiple linear regression, logistic regression and Poisson regression, extending these models to include categorical variables. In general, these models relate response variables to potential predictor / explanatory variables, and can be used to estimate parameters, make predictions or statistically control for certain variables. Students will become familiar with when and how to construct different models, how to assess the assumptions and goodness of fit, how to interpret results, and how to present these results in text and graphical form. In this course, students will also learn principal component analysis (PCA), which can reduce the number of variables and account for a high amount of variability. Students will use the program R for statistical computing for the statistical analysis of real data.
The teaching methodology would be problem based and would encourage students to continue to employ R.
Instructor – Martha Essak
Course Outline – Class of 2018 (Updated November 8, 2017)
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