Predictive Modelling of Undergraduate Student Intake - aair

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Regression – basic assumptions • The ε are independent • The ε are identically distributed • In particular, ε ~ N(0,σ 2 ) where σ 2 is a constant • The sample is representative <strong>of</strong> the population • Vital for useful predictions AAIR Forum 2008 12 Anatoli Lightfoot – ANU
Transformations • The variables used need not be “as measured” • Variables can be transformed: • Using square, square root, or higher order polynomial • Using inverse, logarithm, or exponential function • Using another function • By multiplying l i them together th (“interaction” ti terms) • Transformations are <strong>of</strong>ten used on response variables which are not defined on (-∞,∞) AAIR Forum 2008 13 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: The regression equation • The ε
Page 15 and 16: Predictions • Model is fit on obs
Page 17 and 18: Applications of regression • Many
Page 19 and 20: Applications of regression • Conv
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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