Linear discriminant analysis thesis

For example, a school district might be interested in predicting which pre-kindergarten students are likely to have difficulty learning to read by second grade. A prediction rule would be generated using such predictors as scores on a kindergarten readiness test, ratings on age at which developmental milestones were reached, family socio-economic status, and gender. Predictor weights for two linear combinations, one associated with each group, are determined (Huberty, 1994). Two probabilities of group membership can be calculated for subsequent students based on the two linear combinations; the student is assigned to the group with the larger linear combination score.

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While a scatterplot allows you to check for autocorrelations, you can test the linear regression model for autocorrelation with the Durbin-Watson test.  Durbin-Watson's d tests the null hypothesis that the residuals are not linearly auto-correlated.  While d can assume values between 0 and 4, values around 2 indicate no autocorrelation.  As a rule of thumb values of < d < show that there is no auto-correlation in the data. However, the Durbin-Watson test only analyses linear autocorrelation and only between direct neighbors, which are first order effects.

Linear discriminant analysis thesis

linear discriminant analysis thesis

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