1. 用图表检验Analyze -> regression -> Linear-> Plots6 C4 `' \5 x4 l; V& e# o" X
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable
0 D4 `! Z1 i; S' K: A9 S; n9 {$ v. X6 J( A$ O
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。
|) {' n! F5 b( T* L2. 用统计检验
/ B# t4 \! c' S" |1 a4 MHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
8 P4 [4 Q$ {0 B8 b8 J
7 o8 {) g/ W: |: Y \4 B2 Y) n# [
3 ^2 a% i) n$ U! \+ z$ |Levene’s Test, k* f1 A" o5 t" I, G9 j
Goldfeld-Quandt Test; F5 ^6 P% U& {2 A
Breusch-Pagan Test6 t$ E" Z8 t2 u/ b* A0 A3 G
White‘s Test (比较常用来检验异方差)
# c8 p7 I$ o" y" `/ O* {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)
. ~! j& y# ~; g7 g& w
! w6 Q& J' U8 V1 F, a
- |" x' {- ~# Y1 T- S$ B1 G; ]5 ^0 O' V$ Q/ E- C
White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. , S7 Y# `/ g2 n4 A/ T
· Compare this value with c2 (n), i.e.with c2 (2016) 1 B, X1 G9 l, Y( z% q
(c2 is the symbol for theChi-Square distribution)
: [( i* k' N- F& t) t* I0 Cc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. & b& M, t* z' q( n# _. N# A) A' e
8 I) l! L X4 h( I4 L请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
5 j2 _2 c9 j' X3 F6 ?/ s1 x
|