sta108_handout16

# sta108_handout16 - H andout1 6 Apartment Building data: Y:...

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Handout 16 Apartment Building data: Y: sale price, X1:age of structure, X2:nurn6er of apartment units, of on-site parking spaces, Xs:gross building area. NOTE: Any stepwise regression method (forward or backward) can p-to-remove" values instead of "F-to-enter and F-to-r:emove" values. Stepwise regression (backward) : n-to-enter:4, F-to-re Step 1. Variables in the model Xt,Xz,Xs,Xa,Xs. Fit: Y : 93.035 - .8546X1+ 4.1506X2 * .00093Xs * 2.6936Xq -r Exclusion step Step 2. Variables in the model: X1,X2,Xq,Xs. 99.963 .8864X1 + 4.444X2 + 2.6067X4+.01549X5. Inclusion step Step 3. Variables in the model: X1,X2,X5. Fit: ? 1L4.37 1.0569X1 + 5.0356& +,01496Xs. Variable Par-est F o-value x3 9.014 x 10-5 .0009 .976 xa 2.6067 2.94 .t02 p-rralue intercept 93.035 x1 -.85463 8,20 .010 x2 4.1506 7.74 ,0t2 xs .000929 .104 ,75r Xa 2.6936 2.92 .104 x5 ,015543 tt2.9 .000 p-\ralue intercept 99.963 X1 -.88637 10.35 .004 4.4440 14.77 .001 xa, 2.6067 2.94 .102 .0t5487 119.0 size (in sq. ft.), Xe:number ried out using "p-to-enter and

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Exclusion step No variabie can be entered. Variable Par-est F p-value intercept LL4.37 xr -1.0569 L5.49 .001 x2 5.0356 19.06 .000 x5 0.014961 106,6 ,000 According to model is can be backward stepwise regression with F-to-enter:4;0 and t : 114.37 - 1.0569X1 -f 5.0356X2 + .0i4
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## This note was uploaded on 09/28/2011 for the course STA STA 108 taught by Professor Jiang during the Summer '09 term at UC Davis.

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sta108_handout16 - H andout1 6 Apartment Building data: Y:...

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