# DMtutorial6 - Tutorial 7 1. Suppose we use model Y= a0 + b0...

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Tutorial 7 1. Suppose we use model Y = ± a 0 + b 0 x 1 + c 0 x 2 + ε 0 , if x 1 + x 2 < 0 , a 1 + b 1 x 1 + c 1 x 2 + ε 1 , if x 1 + x 2 0 . to ﬁt data ( x i 1 , x i 2 , Y i ) , i = 1 , 2 , ..., n . Write the procedure to calculate the (delete- one-out) CV value. 2. For model Y = 4 x 1 x 2 + ε, ﬁnd two PPR models for it. (In other word, the representations of PPR model is not unique.) 3. For data B (the ﬁrst 3 columns are independent variables, the last one response variable). Find the best model among Linear regression I: Y = a + b x 1 + ε Linear regression I: Y = a + b x 1 + c x 2 + ε Linear regression I: Y = a + b x 1 + c x 2 + d x 3 + ε PPRA: Y = φ 1 ( α 1 x 1 + α 2 x 2 + α 3 x 3 ) + ε PPRB: Y = φ 1 ( α 1 x 1 + α 2 x 2 + α 3 x 3 ) + φ 2 ( β 1 x 1 + β 2 x 2 + β 3 x 3 ) + ε PPRC: Y = φ 1 ( α 1 x 1 + α 2 x 2 + α 3 x 3 ) + φ 2 ( β 1 x 1 + β 2 x 2 + β 3 x 3 ) + φ 3 ( γ 1 x 1 + γ 2 x 2 + γ 3 x 3 ) + ε PPRD: Y = φ 1 ( α 1 x 1 + α 3 x 3 ) + ε PPRE: Y = φ 1 ( α 1 x 1 + α 3 x 3 ) + φ 2 ( β 1 x 1 + β 3 x 3 ) + ε PPRF: Y = φ 1 ( α 1 x 1 + α 3 x 3 ) + φ 2 ( β 1 x 1 + β 3 x 3 ) + φ 3 ( γ 1 x 1 + γ 3 x 3 ) + ε 4. Consider the Swiss banknotes again. For training data apply CART to built the CART tree. Based on this tree, check the
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## This note was uploaded on 10/04/2010 for the course STAT ST4240 taught by Professor Xiayingcun during the Fall '09 term at National University of Singapore.

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