1. 用图表检验Analyze -> regression -> Linear-> Plots
4 \+ a, W7 O* ^0 h6 P2 OScatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable8 `2 b( U" c/ w! b5 i6 H* p9 j
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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。. t O1 I, G+ p( R" F* R+ ]4 k, M
2. 用统计检验
5 o( T7 m5 ~9 f8 L$ |Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test
* y1 z2 X1 T" p# y5 `, j6 F: y$ |+ oGoldfeld-Quandt Test: c, f9 l8 L* h. v
Breusch-Pagan Test
5 E7 P y, U+ N8 |5 y( JWhite‘s Test (比较常用来检验异方差)- r" i, A! t+ O2 c5 ]/ l/ f
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) & \! d, p& t' j
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. ' u' [+ _ C5 d; M# [) W
· Compare this value with c2 (n), i.e.with c2 (2016) 3 N) |, C3 n* W% ]- a- F
(c2 is the symbol for theChi-Square distribution)
- Z( d3 P8 a {8 I$ fc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
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: ] N& P: Z) R2 |$ p6 z请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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