1. 用图表检验Analyze -> regression -> Linear-> Plots# Z- l3 d! t ]3 J7 D8 n, Q
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable- I [# M* Y, o) X
7 I7 i. E2 M( n5 _) L) ]! g4 q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。
& k) \. r) `: H9 w( G2. 用统计检验- n* D: f7 U$ r6 n$ P
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test# f& g* e1 t6 z: O- V
Goldfeld-Quandt Test
& r& v+ `3 |- v* wBreusch-Pagan Test
. E* p7 @% V. E. B C1 F/ H# VWhite‘s Test (比较常用来检验异方差)% B8 w/ w! P1 i
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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( t9 |! b( I" C* X White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. 7 c- y6 L C# f" s: F
· Compare this value with c2 (n), i.e.with c2 (2016) 6 N. u T5 ^2 b! `- [1 D2 z
(c2 is the symbol for theChi-Square distribution) 4 [( |! ^7 X/ h/ h; A& v1 r
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 2 |+ N: m8 U) W
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请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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