1. 用图表检验Analyze -> regression -> Linear-> Plots, }! y- M6 l) C& G7 c" ?# X
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 Z. A+ h4 b( i# S. E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。* l& a$ [5 O: e1 v" p; E
2. 用统计检验! E5 B' g- b3 f" t1 R
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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- @; O! |! m& u* C3 N( v7 MLevene’s Test
, Y) v, ^ e; L* qGoldfeld-Quandt Test! ], B+ J1 l4 {$ f
Breusch-Pagan Test" ]5 N3 J3 X$ ^7 q; A0 W
White‘s Test (比较常用来检验异方差)+ F) n) y! Z) p3 A" ]0 A; A
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) ) F! V% L& b+ l4 D7 x* H
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
) W( v2 q, ?8 f9 u6 W: g$ `· Compare this value with c2 (n), i.e.with c2 (2016)
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: ^4 \1 J! i, n. Zc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 1 e0 h3 {: e: X6 @: u
9 H0 h+ T) [( l2 y- p2 O请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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