1. 用图表检验Analyze -> regression -> Linear-> Plots
7 O4 I; O$ B* A4 b- i# ]9 M7 FScatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable9 _* f/ A3 m( C' ?9 W5 N' F! q/ k
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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。
" K& k9 q" }* k# [, D2. 用统计检验
. [* T# I; W2 l0 X* ^Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test
6 x5 k0 s& N- S0 Z TGoldfeld-Quandt Test) a2 @/ o, e4 y
Breusch-Pagan Test
+ Z1 X! j4 ^4 QWhite‘s Test (比较常用来检验异方差)& r) n! e# n! o# k! {5 Q
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) ( D0 A% N: z _* }, l& T* _$ C2 W
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8 m9 r/ w. a& t% V9 r0 g5 c White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
- M6 \; v9 o* P. J· Compare this value with c2 (n), i.e.with c2 (2016) ( H- t5 a8 C2 O9 M c' |3 H
(c2 is the symbol for theChi-Square distribution)
( x. ^, k. D1 r# jc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
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0 M8 ^/ a/ S" {% O/ `( F+ A% w请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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