Table 4.1 sas&aelig;Œ‡&auml;&raquo;&curren;&egrave;&ordf;&ordf;&aelig;˜Ž

# Table 4.1 sasæŒ‡ä»¤èªªæ˜Ž

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OPTIONS PS = 62 LS = 66 nodate ; P PS=page sizes; LS=Length size; nodate=no date P DATA CLSROLL; ± 2 G · CLSROLL P /* this is table 4.1 in the textbook*/ P /* ² */ P comment P SAS P ± 2 G · 4.1 P INFILE 'c:\stat 4230\6230data\Chap4\realestate.dat' expandtabs ; P Data ³ * \ real estate data X INPUT num y x1 x2 x3; P Data ³ * PROC PRINT Data =CLSROLL; ´ ^ µ data P proc plot data =CLSROLL; plot (y x1 x2 x3)*(y x1 x2 x3); X \ ø W ¸ proc gplot data =CLSROLL; plot y*(x1 x2 x3) x1*(x2 x3) x2*x3; ¹ ~ \ ¤ f + º \ = ³ * \ PROC REG ; P regression line ;y=b0+b1*x1+b2*x2+b3*x3 P MODEL y= x1 x2 x3/ p r clb cli clm ; P p=predicte values=y_hat ; r=residuals=y-y_hat ; clb, cli, clm ³ * \ plot y* p. ; P plot y*p ; P PROC REG ; ´ ^

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Unformatted text preview: regression line ;y=b0+b1*x1+b2*x2+b3*x 3 & partial test model y=x1 x2 x3; a1: test x2=x3= ; a2: test x2,x3; a3: test x2= ,x3= ; a4: test x2=x3; a5: test x2=x3= 1 ; a6: test x2+x3= 1 ; RUN ; X ¶ “ ‰ “ \ & — ø W ¸ & SAS ‘ » & P computes predicted values R produces analysis of residuals CLB computes (1-alpha)% confidence limits for the parameter estimates CLI computes (1-alpha)% confidence limits for an individual predicted value CLM computes (1-alpha) % confidence limits for the expected value of the dependent variable...
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## This note was uploaded on 06/06/2011 for the course STAT 4230 taught by Professor Yin during the Spring '11 term at UGA.

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Table 4.1 sasæŒ‡ä»¤èªªæ˜Ž

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