1. 用图表检验Analyze -> regression -> Linear-> Plots
# I# B, W. s; m& w0 [9 @) s* g/ BScatter 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。/ ~4 g. v1 P6 P9 F% b; G4 p8 ?
2. 用统计检验3 Q Y' p4 K" }5 a2 t
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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% C R4 Q. g4 K# tLevene’s Test
0 n; e$ R: e% F, u2 }Goldfeld-Quandt Test' @# L/ g O- ]$ O
Breusch-Pagan Test
4 X( X. P- Q! u1 [White‘s Test (比较常用来检验异方差)8 h' k s& x0 B# N) R6 [2 d
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) 9 n% D& j2 I+ g" k$ k; ~
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0 s* Z9 B4 T6 c" o White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. 5 L- t! W' \) z' ]( f- D1 a
· Compare this value with c2 (n), i.e.with c2 (2016) ( I% Y2 n2 u' ^$ X3 x6 l! H
(c2 is the symbol for theChi-Square distribution)
/ C( o9 G* Z% H; o% N: [4 ec2 (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
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