1. 用图表检验Analyze -> regression -> Linear-> Plots. v: v+ v. }( X* q: I
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable2 E P3 ^- b5 W4 m; D3 W
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如果图表显示有可能存在异方差,需要用统计检验来进一步检测异方差是否确实存在。
, D& h6 }: O6 a) N: X3 m( l2. 用统计检验
5 s! w3 E/ d1 C. \" n7 C, n, y6 uHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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' z: W j; e0 iLevene’s Test0 _+ {# O5 \) J" I
Goldfeld-Quandt Test1 z0 k$ ]0 b1 \/ t8 w
Breusch-Pagan Test/ C2 [6 K4 c5 }5 x8 {- z6 M
White‘s Test (比较常用来检验异方差). A& O2 R- R* p; u# I& e2 z
Assume you want to run a regression of wage on age, work experience,education, gender, and a dummy for sectorofemployment (whether employed in the public sector). wage = function(age, workexperience, education, gender, sector) or, as your textbook will have it, wage = b1 + b2*age + b3*work experience+ b4*education + b5*gender + b6*sector The White’s test is usually used as a test for heteroskedasticity. In this test, a regression of the squares ofthe residuals is run on the variables suspected of causing theheteroskedasticity, their squares, and cross products. (residuals)2 = b0 + b1 educ + b2 work_ex+ b3 (educ)2 + b4 (work_ex)2 + b5(educ*work_ex) 9 o/ h2 `- i0 _
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
2 i3 m" f1 k5 f1 S w· Compare this value with c2 (n), i.e.with c2 (2016)
2 I$ h: w8 k0 S! o! b(c2 is the symbol for theChi-Square distribution)
1 D. C; \4 L. H5 fc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. ' }4 M& D1 @5 k4 X. F2 W, | v; G
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请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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