1. 用图表检验Analyze -> regression -> Linear-> Plots
' ~. `3 P1 ?2 D2 S( R% m' ` j9 ^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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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。( y; o# m" \' O3 T4 V( n" P
2. 用统计检验$ P( ], [% P! P" r$ }9 a
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
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" Z( ]% L4 u, G1 d1 a! L3 e eLevene’s Test
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Breusch-Pagan Test; _8 f7 R- i# f
White‘s Test (比较常用来检验异方差)* T' T: p. Y# S q* X
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) # H# J3 W, Q& V2 Y
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. & I1 W( g1 P# ? t; I6 o* M
· Compare this value with c2 (n), i.e.with c2 (2016)
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* I+ S; a' Z7 ?5 Yc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
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请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
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