1. 用图表检验Analyze -> regression -> Linear-> Plots+ h4 G9 [+ J' t |
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable
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7 ^3 t- K4 ~2 `/ g5 ]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。
$ f1 E! i1 t7 `8 U0 m* P2. 用统计检验+ J* G' E2 O+ q6 G9 {& X1 g
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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, u4 z& M/ q- k/ P6 lLevene’s Test6 m7 P6 e, v( J: t
Goldfeld-Quandt Test& N+ S+ T+ ^. H+ E$ ~: X2 ^
Breusch-Pagan Test( r, a& o- P; S: C
White‘s Test (比较常用来检验异方差)
( g! b, d0 M; X2 VAssume 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)
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
T: O4 A% E# Z% y7 t· Compare this value with c2 (n), i.e.with c2 (2016)
" r5 X4 o: d7 V# C(c2 is the symbol for theChi-Square distribution) + U# K+ Y0 e7 {
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
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; ~ a& C7 ?: f2 P请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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