1. 用图表检验Analyze -> regression -> Linear-> Plots. Q1 B% j% b! R5 ^
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。' H/ e4 G, v$ k5 ^* F, D
2. 用统计检验
1 w/ h- f8 v9 h. X& LHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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: q% o) Z; ?$ ]1 w% i2 CLevene’s Test; Z! ^7 [1 W# T$ K2 ?" p" x
Goldfeld-Quandt Test
: i3 S2 G3 x. K/ TBreusch-Pagan Test, H0 o$ k, Y$ V8 w' i4 z
White‘s Test (比较常用来检验异方差)
( p2 u, b, @1 w- OAssume 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) - U" W5 k9 f4 p/ d7 e
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. - c! J9 q/ q9 P3 j1 C) u
· Compare this value with c2 (n), i.e.with c2 (2016) ! l0 @ S+ i3 T7 d, j/ x9 y9 P& P8 d
(c2 is the symbol for theChi-Square distribution)
7 F1 Q6 d1 J! U) N/ G+ L* 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
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