1. 用图表检验Analyze -> regression -> Linear-> Plots/ q) `* p/ W- T4 E( }: @1 ?
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable
9 p' n0 I# V! X, ^
2 ?: O( i+ D- Q+ Y9 y7 L: I; i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, X7 x0 a" Z2. 用统计检验
' O8 |; I! V/ T1 q9 _Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
* w2 @9 p3 [, X+ Q3 ^1 Y, ~( L
* d4 ?( l2 O6 s+ x, s' g& J
. t: @2 U4 y2 B) X, T3 jLevene’s Test* }. |* E/ c; v2 e6 O: I
Goldfeld-Quandt Test
, j$ r4 [% o; X# k" S6 CBreusch-Pagan Test; x6 y3 @6 O/ {6 H. H/ c& n, H
White‘s Test (比较常用来检验异方差)( e5 W" X+ @: V' w& f
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) ( U1 d3 x L' t
: K4 _) |$ Y4 u+ }6 a C
# i) ^% p L! N5 _. x& S
+ W. ~: f; o# v* ]) L
White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. 7 C! M1 h+ j& s6 H2 ]
· Compare this value with c2 (n), i.e.with c2 (2016)
/ m5 Q+ i/ O2 x) J* P/ B- D(c2 is the symbol for theChi-Square distribution)
7 A% |8 I" e1 vc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed.
4 I! b4 L2 f* |3 y! c& q( [% ~1 s5 ]8 I( O n
请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
) H4 J+ ?; L% a& K2 K$ q4 D |