1. 用图表检验Analyze -> regression -> Linear-> Plots
' @+ z' u6 x5 ?9 S( X8 gScatter 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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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。
" M0 w1 u3 r* ^7 w2. 用统计检验
# _7 | g- k& g4 q2 F& ?Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test7 O; C$ F, Y6 s; X2 d8 V# p: b
Goldfeld-Quandt Test0 V% Q9 i0 S, z
Breusch-Pagan Test* o- Q0 O- u' x) M5 ?9 q
White‘s Test (比较常用来检验异方差)$ f& U8 l3 X& J* {
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) 8 z; |$ n% D8 @: \- x
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. ( C9 y# |* Q) ~- k# s9 |9 P
· Compare this value with c2 (n), i.e.with c2 (2016)
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c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 9 m, p+ c& n5 W/ t; ^0 G' l
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regression_explained_SPSS.doc
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