1. 用图表检验Analyze -> regression -> Linear-> Plots5 ]% i' u8 n7 y, e1 p- N
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable; \) Z) u* `6 ?3 l& V, P3 G% H
7 N$ f7 A1 \! {, m/ ]7 w# N
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。+ ?, }) m) N. c4 R/ @9 X- q
2. 用统计检验
+ e# R, Q- v8 k0 Y* U: rHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
/ ~' Y O$ g4 t: r+ ?, W
& p( h. z4 G& J# v( o1 E' `
' u8 g% a3 ^% eLevene’s Test
% \& [+ j8 v: i: R+ G3 NGoldfeld-Quandt Test' B8 f: H9 X, r! z5 d
Breusch-Pagan Test
4 M8 n6 Q, Y, W! F$ P% C$ WWhite‘s Test (比较常用来检验异方差) t* g1 g5 L- b% Q0 H: t9 Y9 x
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)
3 E1 z0 e. J6 O2 ^. m& ~( n) a& m; K9 \. _3 T5 C
- F; u' @. V, ]" B3 h8 ?
, a; n( g5 L v: X3 m White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
, q* w% C a2 {, b6 g% K' [! c· Compare this value with c2 (n), i.e.with c2 (2016) 4 W, Q$ _% ]/ b9 U$ ^
(c2 is the symbol for theChi-Square distribution) : ?- {& m/ b8 c7 J3 k; k
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. - y; g V4 O6 @) f/ C
* W7 e1 d, x9 P6 I4 R# t请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
" e/ q& O; E& o: [3 D9 i
|