Predictive Modelling of Undergraduate Student Intake - aair

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Applications of regression • Historical datasets available from UAC are large • Many possible explanatory variables present • Bio & demo data (age, gender, location) • Education data (UAI, prior studies) • Preference information (which courses, what order) • What are the observations? • Hard to tell where to start! t! AAIR Forum 2008 18 Anatoli Lightfoot – ANU
Applications of regression • Conversion of preferences to offers depends only on type of course (eg. arts, science, etc.) • Model equation: • Results: logit(Y) = α + β 1 X 1 + ε • 1 st preferences for B Arts will result in the same proportion p of enrolments as 5 th preferences for B Arts AAIR Forum 2008 19 Anatoli Lightfoot – ANU
Page 1 and 2: Predictive Modelling of Undergradua
Page 3 and 4: Why is this important? • Load man
Page 5 and 6: Things this presentation does not c
Page 7 and 8: Regression • Relationship between
Page 9 and 10: The regression equation - example
Page 11 and 12: The regression equation • The ε
Page 13 and 14: Transformations • The variables u
Page 15 and 16: Predictions • Model is fit on obs
Page 17: Applications of regression • Many
Page 21 and 22: Model refinement • Iterative proc
Page 23 and 24: Reliable models • Simple models a
Page 25 and 26: Detailed example - acceptance rates
Page 27 and 28: Detailed example - acceptance rates

Predictive Modelling of Undergraduate Student Intake - aair

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