1. 用图表检验Analyze -> regression -> Linear-> Plots5 F3 ?4 X& A9 S: B' b# j9 g s
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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: x; ~ T3 w; x* M9 D7 W; }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。3 \9 D, q8 ]2 i; A! g
2. 用统计检验
/ W3 \: @# B1 d3 L3 Q: u9 SHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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3 q+ S2 Q% |& v+ @0 _! P9 ELevene’s Test
4 S: g. t$ e3 p5 J9 gGoldfeld-Quandt Test4 }- ?; J$ Y; p% f; F3 H' r7 }& K
Breusch-Pagan Test
; n H6 J" T0 nWhite‘s Test (比较常用来检验异方差)
5 |8 I. e5 [& _9 ?/ B4 z/ `7 P. {5 dAssume 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) # q3 A/ w9 x% r' u& [ N
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
9 R0 L2 V4 C3 y, o· Compare this value with c2 (n), i.e.with c2 (2016)
3 @/ A% C# I& Q5 K, D0 G(c2 is the symbol for theChi-Square distribution) 3 t2 W i# z. S7 l6 L$ W
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
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* n5 z d% C) U$ [请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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