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(a) Briefly discuss two ways to directly extend the linear regression model to accommodate non-linear relationship.[8 marks] (b) In a linear regression setting, explain how F-statistic and R² statistic can be used.[4 marks] (c) Consider the following binary data set with one attribute: What is the linear discriminant analysis (LDA) prediction for a new point 0.9? (d) Briefly explain quadratic discriminant analysis (QDA). How are the assumptions of QDA different from those of LDA? (e) An estate agent in Egham wants to build a system that can estimate sale price of a new property using a database of past sale prices. Is this a supervised or unsupervised learning task? Is it classification or regression?

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