1. 用图表检验Analyze -> regression -> Linear-> Plots
( o* S+ S( j' Q& {Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable! n! {; u5 Q1 [7 v
- C# f$ w6 M" o1 s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。& T: |% T" K( t- f
2. 用统计检验
: y0 l$ g+ c0 S6 [0 gHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
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Levene’s Test
6 B/ z2 k& Z% i3 m9 F7 RGoldfeld-Quandt Test
9 [0 x8 U+ x. A9 i# VBreusch-Pagan Test
( y, I! G' r7 E, Y& G: VWhite‘s Test (比较常用来检验异方差)$ M7 j) A* U; |/ s: B1 M5 E
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) & x* i2 u X8 K! Q
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5 e$ \7 [* }8 c K% R White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. # M0 K- N6 f) M- f0 X2 c0 T
· Compare this value with c2 (n), i.e.with c2 (2016) : ]7 |# E7 F8 z$ ]: \8 B, T
(c2 is the symbol for theChi-Square distribution) 7 k/ K) W3 @9 [; R2 X4 d* Z; K
c2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. g v; m8 p2 q! Q& X: f0 g& S2 ~$ B
. a: h9 x; t3 Z6 T0 l- y请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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