1. 用图表检验Analyze -> regression -> Linear-> Plots' P2 n- o) r8 E! n
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable9 p- Z( E' ?! @2 p& g6 k) z
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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。8 H% y4 _: @; v! U# o
2. 用统计检验
! V5 w5 k v' iHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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/ C5 Q2 B: x2 C0 ~$ g; u$ rLevene’s Test/ N4 i% w! d9 v3 {& R
Goldfeld-Quandt Test2 F. u- P D2 B* u, S0 S
Breusch-Pagan Test: K! w2 N/ m- C8 C" V4 k
White‘s Test (比较常用来检验异方差)1 L3 [4 I6 B6 N: r
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) + g- q& b: _) i9 a6 [8 c& K9 t. e
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
. o; P: q4 ~8 s· Compare this value with c2 (n), i.e.with c2 (2016) / n4 ]' C! k9 h" A" v4 t8 P
(c2 is the symbol for theChi-Square distribution)
- Z1 u" @6 i9 Q |/ V0 Bc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 0 h- C% e# c9 n1 R; |8 O2 _/ R: u
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请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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