1. 用图表检验Analyze -> regression -> Linear-> Plots
5 r8 P. P1 r7 D8 C3 u; TScatter 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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, ] b. n( |2 O' S* E' u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。) o. @. m7 h1 _8 b) O& M
2. 用统计检验! U9 G+ n( P: S/ l+ p. b" O0 m
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
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Levene’s Test
4 g& T3 v* E1 c* a1 F, ^0 D! ?Goldfeld-Quandt Test3 ?0 V" b) O- K$ S
Breusch-Pagan Test) k) @# R3 {- [( E+ a1 ~/ E
White‘s Test (比较常用来检验异方差)
* P J$ t5 h9 l% AAssume 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) ; ?; ]+ F1 @" p
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White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. 3 y- C1 x0 ~- |4 j" P
· Compare this value with c2 (n), i.e.with c2 (2016) + d- c/ p9 P7 W; [+ z
(c2 is the symbol for theChi-Square distribution) P _/ Q* S/ p5 ?
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 0 O$ H' o m8 f3 V
: k) _7 ^$ O* P) |# R请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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