1. 用图表检验Analyze -> regression -> Linear-> Plots3 `8 r+ O' b) b, {& y. X2 O q
Scatter plot of the standardised residuals on the standardised predicted values (ZRESID as the Y variable, and ZPRED as the X variable. A: z: C3 Q+ }5 l
0 u' ^! O+ a! ~! g0 L$ B
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。
- B/ \. D6 P! i' X1 X/ |2. 用统计检验
9 k" @: R6 g% W* n! N4 Q4 T) WHeteroscedasticity——Testing and Correcting in SPSS.pdf
Gwilym Pryce March 2002.doc
(172.5 KB, 下载次数: 4)
, t) j, [% X+ }; W& y% D4 C! G" c+ V6 P( c% j {
4 O! G4 }6 W z M1 QLevene’s Test- O# R( m; e$ g
Goldfeld-Quandt Test
$ S+ y3 a i- I: x% H( s5 eBreusch-Pagan Test
V I _% I: l! h$ T# h/ LWhite‘s Test (比较常用来检验异方差)) R7 P1 T" n2 U
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)
( D8 Q! h$ C& x/ T) C$ ]( T2 U5 s2 N, _ N7 p6 b+ b
0 T# E' _9 Y, o) Y6 w$ ?
# K6 J2 `/ X1 @' q2 ?( \, w; }, m; E
White’s Test · Calculate n*R2 à R2 = 0.037, n=2016 à Thus, n*R2 = .037*2016 = 74.6.
* Z6 O/ m; J; S+ h! P; a/ [+ k: i· Compare this value with c2 (n), i.e.with c2 (2016) ! q3 t5 d' L0 e
(c2 is the symbol for theChi-Square distribution)
# G& O+ w2 ^5 O0 ^2 N# A1 u, |6 Tc2 (2016) = 124obtained from c2 table. (For 955 confidence) As n*R2 < c2 ,heteroskedasticity can not be confirmed. 5 z5 i" P$ ^7 i
% O; Q6 W1 | E( ^请参考:regression_explained_SPSS
regression_explained_SPSS.doc
(368 KB, 下载次数: 0)
! y" ?, s) J! f$ M! m2 a2 L |