jan12 - STA 414/2104 Jan 12, 2010 Administration Please...

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STA 414/2104 Jan 12, 2010 Administration I Please check web page regularly for updates http://www.utstat.utoronto.ca/reid/414S10.html I Blackboard is used only for email and grades I You should by now have have R on your PC, or be planning to go your own route re software I Printing slides from web page (Acrobat: page setup (horizontal); expand to fit) I Thursday : TA Li Li will answer your questions about R I Project: check course information handout from last week I More data sets: see Applied Statistics (Journal of the Royal Statistical Society, Series C); articles may have links to data sets used, at I http://www.blackwellpublishing.com/rss/ default.htm I e.g. “Spatiotemporal smoothing and sulphur dioxide trends over Europe” by Bowman et al (December, 2009) 1 / 23
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STA 414/2104 Jan 12, 2010 Polynomial regression I See R code from last week I lm10 = lm ( y ˜ x + I(xˆ2) + I(xˆ3) + . .. + I(xˆ10) ) I i.e. E ( y ) = β 0 + β 1 x + β 2 x 2 + ... + β 10 x 10 y = X β + ± I fm10 = lm ( y ˜ poly(x, 10) ) I E ( y ) = α 0 + α 1 P 1 ( x ) + ··· + α 10 P 10 ( x ) y = X * α + ± I P j ( x ) = a 0 j + a 1 j x + a 2 j x 2 + ··· + a jj x j I coefficients a 0 j , a 1 j , etc. to be determined I so that columns of X * are orthogonal 2 / 23
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STA 414/2104 Jan 12, 2010 ... polynomial regression >x [1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 > model.matrix(lm10) (Intercept) x I(xˆ2) I(xˆ3) I(xˆ4) I(xˆ5) I(xˆ6) . .. 1 1 0.0 0.00 0.000 0.0000 0.00000 0.000000 2 1 0.1 0.01 0.001 0.0001 0.00001 0.000001 3 1 0.2 0.04 0.008 0.0016 0.00032 0.000064 4 1 0.3 0.09 0.027 0.0081 0.00243 0.000729 5 1 0.4 0.16 0.064 0.0256 0.01024 0.004096 6 1 0.5 0.25 0.125 0.0625 0.03125 0.015625 7 1 0.6 0.36 0.216 0.1296 0.07776 0.046656 8 1 0.7 0.49 0.343 0.2401 0.16807 0.117649 9 1 0.8 0.64 0.512 0.4096 0.32768 0.262144 10 1 0.9 0.81 0.729 0.6561 0.59049 0.531441 11 1 1.0 1.00 1.000 1.0000 1.00000 1.000000 > model.matrix(fm10) (Intercept) poly(x, degree)1 poly(x, degree)2 poly(x, degree)3 . .. 1 1 -4.767313e-01 0.51209156 -4.580286e-01 2 1 -3.813850e-01 0.20483662 9.160572e-02 3 1 -2.860388e-01 -0.03413944 3.358876e-01 4 1 -1.906925e-01 -0.20483662 3.511553e-01 5 1 -9.534626e-02 -0.30725493 2.137467e-01 6 1 -1.323195e-17 -0.34139437 6.621275e-17 7 1 9.534626e-02 -0.30725493 -2.137467e-01 8 1 1.906925e-01 -0.20483662 -3.511553e-01 9 1 2.860388e-01 -0.03413944 -3.358876e-01 10 1 3.813850e-01 0.20483662 -9.160572e-02 11 1 4.767313e-01 0.51209156 4.580286e-01 3 / 23
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STA 414/2104 Jan 12, 2010 ... polynomial regression I same ˆ y = X ˆ β = X * ˆ α I > lm10$fitted.values 1 2 3 4 5 0.023170726 0.600194667 0.953585931 0.613647096 0.015437840 6 7 8 9 10 0.000285572 -0.107110688 -1.329937671 -0.743709343 -0.625900416 11 0.249038261 > fm$fitted.values 1 2 3 4 5 0.023170726 0.600194667 0.953585931 0.613647096 0.015437840 6 7 8 9 10 0.000285572 -0.107110688 -1.329937671 -0.743709343 -0.625900416 11 0.249038261 0.0 0.2 0.4 0.6 0.8 1.0 -1.0 0.0 1.0 x y 4 / 23
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STA 414/2104 Jan 12, 2010 Bias-variance trade-off
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jan12 - STA 414/2104 Jan 12, 2010 Administration Please...

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