1. 用图表检验Analyze -> regression -> Linear-> Plots2 s- |, K% q: Y& O, I7 E9 I; @( {
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable
% m+ D, f+ R' i! o7 c6 k( x
7 @" @2 C, D; P8 X3 H( ?1 h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。& W$ H4 G, d: t% k8 K+ Y6 g
2. 用统计检验% Q L# h; O+ x" d# F7 R
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
9 @( M5 N/ n" b7 O5 g
9 C) l5 e+ j6 V: y7 h8 B* D/ B2 M- w0 h: t5 w4 c
Levene’s Test
3 G7 d" l# r4 wGoldfeld-Quandt Test
# E9 U+ s. E2 cBreusch-Pagan Test1 J1 [3 e% R' V* q/ M1 [
White‘s Test (比较常用来检验异方差)
7 V3 O: o. t/ \- HAssume 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) 6 u" t) [6 w0 Q. C' l5 b
5 H: t0 t! W: q0 h$ t1 T. T( l% B% \# _* {& v. ~5 B( n+ F
7 `( h. w5 ^2 g4 o8 n White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. K6 ^6 @* _; w6 R
· Compare this value with c2 (n), i.e.with c2 (2016) 1 B8 X2 D2 x% i+ b& Y/ V5 R" ~
(c2 is the symbol for theChi-Square distribution)
$ o3 u7 a9 d2 G: W) L% h, fc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
& |* B8 N& h8 p! B4 w, ]( f4 A! F6 t- ~1 [' g1 D
请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
: }1 C- L' w7 Z( i6 f/ M { |