1. 用图表检验Analyze -> regression -> Linear-> Plots
) X) P, ?4 x, aScatter 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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" Y U: @! _- U3 _/ j; b2 m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。* J, ^6 ^3 _' `. H% D1 p
2. 用统计检验
* L- [' B2 Q" M+ X% _% EHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test
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Breusch-Pagan Test
4 E. r0 Y0 _. Q) V8 J+ rWhite‘s Test (比较常用来检验异方差)8 z! F2 z2 O0 ~* h) w. 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)
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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' x8 Y0 p8 e3 t( B6 w
· Compare this value with c2 (n), i.e.with c2 (2016) ! Y1 b% d8 @) f3 e# g
(c2 is the symbol for theChi-Square distribution) 8 Y) ?( t' z/ n0 K
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. & x, t7 \; B1 W
, p, {/ j; v9 W& D$ R3 {7 u请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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