1. 用图表检验Analyze -> regression -> Linear-> Plots
- ?- I. F3 x+ v* ]! QScatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable! }7 Y9 J: N, G# N0 z; o% i
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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。
+ ], G1 H; ?% {, W2. 用统计检验- \$ e; }/ K" f& o
Heteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
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Levene’s Test
% `+ n' @, _6 }0 P' I$ U6 b5 WGoldfeld-Quandt Test
! D& I; V$ {7 @$ ?' z3 [Breusch-Pagan Test3 A* f" |) o- P( }' ?
White‘s Test (比较常用来检验异方差)
8 R0 v7 w/ F9 ~5 Z2 E) ZAssume 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)
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2 c! T, Z$ [) u8 E1 v: ]) T White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6. , P" E0 ?: J) l$ D8 a+ a6 n
· Compare this value with c2 (n), i.e.with c2 (2016) 7 u) X+ i4 a8 c- }% U# O
(c2 is the symbol for theChi-Square distribution)
: A3 F$ ^. Z/ @0 Uc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. $ ^1 e0 C2 _, d1 t! U1 v. m
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请参考:regression_explained_SPSS
regression_explained_SPSS.doc
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