This is further reading for detecting outliers, adopted from http://www.ats.ucla.edu/stat/spss/webbooks/reg/chapter2/spssreg2.htm .

crime.sav

outliercheck.sps

get file = "DirectoryOfYourComputer\crime.sav".

descriptives
  /var=crime murder pctmetro pctwhite pcths poverty single.

		Descriptive Statistics
			N	Minimum	Maximum	Mean	Std. Deviation
violent crime rate	51	82	2922	612.84	441.100
murder rate		51	1.60	78.50	8.7275	10.71758
pct metropolitan	51	24.00	100.00	67.3902	21.95713
pct white		51	31.80	98.50	84.1157	13.25839
pct hs graduates	51	64.30	86.60	76.2235	5.59209
pct poverty		51	8.00	26.40	14.2588	4.58424
pct single parent	51	8.40	22.10	11.3255	2.12149
Valid N (listwise)	51				

graph
  /scatterplot(matrix)=crime murder pctmetro pctwhite pcths poverty single .

GRAPH /SCATTERPLOT(BIVAR)=pctmetro WITH crime BY state(name) .

GRAPH /SCATTERPLOT(BIVAR)=poverty WITH crime BY state(name) .

GRAPH /SCATTERPLOT(BIVAR)=single WITH crime BY state(name) .

regression
  /dependent crime
  /method=enter pctmetro poverty single.

		Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.916a	.840	.830	182.068
a. Predictors: (Constant), pct single parent, pct metropolitan, pct poverty

			ANOVA(b)
Model			Sum of Squares	df	Mean Square	F	Sig.
1	Regression	8170480.211	3	2723493.404	82.160	.000a
	Residual	1557994.534	47	33148.820		
	Total		9728474.745	50			
a. Predictors: (Constant), pct single parent, pct metropolitan, pct poverty
b. Dependent Variable: violent crime rate

			Coefficients(a)
		Unstandardized Coefficients		Standardized Coefficients
Model				B		Std. Error	Beta	t	Sig.
1	(Constant)		-1666.436	147.852			-11.271	.000
	pct metropolitan	7.829		1.255		.390	6.240	.000
	pct poverty		17.680		6.941		.184	2.547	.014
	pct single parent	132.408		15.503		.637	8.541	.000
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram.

		Model Summary(b)
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.916a	.840	.830	182.068
a. Predictors: (Constant), pct single parent, pct metropolitan, pct poverty
b. Dependent Variable: violent crime rate

			ANOVA(b)
Model			Sum of Squares	df	Mean Square	F	Sig.
1	Regression	8170480.211	3	2723493.404	82.160	.000a
	Residual	1557994.534	47	33148.820		
	Total		9728474.745	50			
a. Predictors: (Constant), pct single parent, pct metropolitan, pct poverty
b. Dependent Variable: violent crime rate

			Coefficients(a)
		Unstandardized Coefficients		Standardized Coefficients
Model				B		Std. Error	Beta	t	Sig.
1	(Constant)		-1666.436	147.852		-11.271	.000
	pct metropolitan	7.829		1.255		.390	6.240	.000
	pct poverty		17.680		6.941		.184	2.547	.014
	pct single parent	132.408		15.503		.637	8.541	.000
a. Dependent Variable: violent crime rate

		Residuals Statistics(a)
			Minimum		Maximum		Mean	Std.Deviation	N
Predicted Value	-30.51		2509.43		612.84	404.240		51
Residual		-523.013	426.111		.000	176.522		51
Std. Predicted Value	-1.592		4.692		.000	1.000		51
Std. Residual		-2.873		2.340		.000	.970		51
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid).

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid) id(state) outliers(sdresid).

see at Analyzing Residuals Document for sdresid (studentized deleted residuals).

		Residuals Statistics(a)
					Minimum		Maximum		Mean	Std. Deviation	N
Predicted Value				-30.51		2509.43		612.84	404.240		51
Std. Predicted Value			-1.592		4.692		.000	1.000		51
Standard Error of Predicted Value	25.788		133.343		47.561	18.563		51
Adjusted Predicted Value		-39.26		2032.11		605.66	369.075		51
Residual				-523.013	426.111		.000	176.522		51
Std. Residual				-2.873		2.340		.000	.970		51
Stud. Residual				-3.194		3.328		.015	1.072		51
Deleted Residual			-646.503	889.885		7.183	223.668		51
Stud. Deleted Residual		-3.571		3.766		.018	1.133		51
Mahal. Distance			.023		25.839		2.941	4.014		51
Cook's Distance			.000		3.203		.089	.454		51
Centered Leverage Value		.000		.517		.059	.080		51
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid) id(state) outliers(sdresid)
  /casewise=plot(sdresid) outliers(2)  .

		Casewise Diagnostics(a)
Case Number	state	Stud. Deleted 	violent crime 	Predicted 	Residual
			Residual	rate		Value
9		fl	2.620		1206		779.89		426.111
25		ms	-3.571		434		957.01		-523.013
51		dc	3.766		2922		2509.43		412.566
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid lever) id(state) outliers(sdresid lever)
  /casewise=plot(sdresid) outliers(2).

		Outlier Statistics(a)
				Case 	state	Statistic
				Number
Stud. Deleted Residual	1	51	dc 	3.766
			2	25	ms 	-3.571
			3	9	fl 	2.620
			4	18	la 	-1.839
			5	39	ri 	-1.686
			6	12	ia 	1.590
			7	47	wa 	-1.304
			8	13	id 	1.293
			9	14	il 	1.152
			10	35	oh 	-1.148
Centered Leverage Value	1	51	dc 	.517
			2	1	ak 	.241
			3	25	ms 	.171
			4	49	wv 	.161
			5	18	la 	.146
			6	46	vt 	.117
			7	9	fl 	.083
			8	26	mt 	.080
			9	31	nj 	.075
			10	17	ky 	.072
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid lever) id(state) outliers(sdresid, lever)
  /casewise=plot(sdresid)  outliers(2)
  /scatterplot(*lever, *sdresid).

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid lever) id(state) outliers(sdresid, lever, cook)
  /casewise=plot(sdresid)  outliers(2) cook dffit
  /scatterplot(*lever, *sdresid).

		Casewise Diagnostics(a)
Case Number	state	Stud. 		violent 	Cook's		DFFIT
			Deleted		crime		Distance	
			Residual	rate
9	fl		2.620		1206		.174		48.507
25	ms		-3.571		434		.602		-123.490
51	dc		3.766		2922		3.203		477.319
a. Dependent Variable: violent crime rate

		Outlier Statistics(a)
		Case Number	state	Statis	Sig. F
Stud.  		1	51	dc 	3.766	
Deleted		2	25	ms 	-3.571	
Residual	3	9	fl 	2.620	
		4	18	la 	-1.839	
		5	39	ri 	-1.686	
		6	12	ia 	1.590	
		7	47	wa 	-1.304	
		8	13	id 	1.293	
		9	14	il 	1.152	
		10	35	oh 	-1.148	
Cook's 		1	51	dc 	3.203	.021
Distance	2	25	ms 	.602	.663
		3	9	fl 	.174	.951
		4	18	la 	.159	.958
		5	39	ri 	.041	.997
		6	12	ia 	.041	.997
		7	13	id 	.037	.997
		8	20	md 	.020	.999
		9	6	co 	.018	.999
		10	49	wv 	.016	.999
Centered  	1	51	dc 	.517	
Leverage	2	1	ak 	.241	
Value		3	25	ms 	.171	
		4	49	wv 	.161	
		5	18	la 	.146	
		6	46	vt 	.117	
		7	9	fl 	.083	
		8	26	mt 	.080	
		9	31	nj 	.075	
		10	17	ky 	.072	
a. Dependent Variable: violent crime rate

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid lever) id(state) outliers(sdresid, lever, cook)
  /casewise=plot(sdresid)  outliers(2) cook dffit
  /scatterplot(*lever, *sdresid)
  /save sdbeta(sdfb).

list
  /variables state sdfb1 sdfb2 sdfb3
  /cases from 1 to 10.

state       sdfb1       sdfb2       sdfb3

ak        -.10618     -.13134      .14518
al         .01243      .05529     -.02751
ar        -.06875      .17535     -.10526
az        -.09476     -.03088      .00124
ca         .01264      .00880     -.00364
co        -.03705      .19393     -.13846
ct        -.12016      .07446      .03017
de         .00558     -.01143      .00519
fl         .64175      .59593     -.56060
ga         .03171      .06426     -.09120

Number of cases read:  10    Number of cases listed:  10

VARIABLE LABLES sdfb1 "Sdfbeta pctmetro"
                              /sdfb2 "Sdfbeta poverty"
                              /sdfb3 "Sdfbeta single" .

GRAPH
  /SCATTERPLOT(OVERLAY)=sid sid sid  WITH sdfb1 sdfb2 sdfb3 (PAIR) BY state(name)
  /MISSING=LISTWISE .

 PRED
  Unstandardized predicted values.
 RESID
  Unstandardized residuals.
 DRESID
  Deleted residuals.
 ADJPRED
  Adjusted predicted values.
 ZPRED
  Standardized predicted values.
 ZRESID
  Standardized residuals.
 SRESID
  Studentized residuals.
 SDRESID
  Studentized deleted residuals. 
 SEPRED
  Standard errors of the predicted values.
 MAHAL
  Mahalanobis distances.
 COOK
  Cook’s distances.
 LEVER
  Centered leverage values. 
 DFBETA
  Change in the regression coefficient that results from the deletion of the ith case. A DFBETA value is computed for each case for each regression coefficient generated by a model.
 SDBETA
  Standardized DFBETA. An SDBETA value is computed for each case for each regression coefficient generated by a model. 
 DFFIT
  Change in the predicted value when the ith case is deleted. 
 SDFIT
  Standardized DFFIT. 
 COVRATIO
  Ratio of the determinant of the covariance matrix with the ith case deleted to the determinant of the covariance matrix with all cases included. 
 MCIN
  Lower and upper bounds for the prediction interval of the mean predicted response. A lowerbound LMCIN and an upperbound UMCIN are generated. The default confidence interval is 95%. The confidence interval can be reset with the CIN subcommand. (See Dillon & Goldstein
 ICIN
  Lower and upper bounds for the prediction interval for a single observation. A lowerbound LICIN and an upperbound UICIN are generated. The default confidence interval is 95%. The confidence interval can be reset with the CIN subcommand. (See Dillon & Goldstein

regression
  /dependent crime
  /method=enter pctmetro poverty single
  /residuals=histogram(sdresid lever) id(state) outliers(sdresid, lever, cook)
  /casewise=plot(sdresid)  outliers(2) cook dffit
  /scatterplot(*lever, *sdresid)
  /partialplot.  

regression
  /dependent crime
  /method=enter pctmetro poverty single.

			Coefficients(a)
		Unstandardized Coefficients		Standardized Coefficients
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	-1666.436	147.852		-11.271	.000
	pct metropolitan	7.829	1.255	.390	6.240	.000
	pct poverty	17.680	6.941	.184	2.547	.014
	pct single parent	132.408	15.503	.637	8.541	.000
a. Dependent Variable: violent crime rate

compute filtvar = (state NE "dc").
filter by filtvar.
regression
  /dependent crime
  /method=enter pctmetro poverty single . 

			Coefficients(a)
		Unstandardized Coefficients		Standardized Coefficients
Model		B	Std. Error	Beta	t	Sig.
1	(Constant)	-1197.538	180.487		-6.635	.000
	pct metropolitan	7.712	1.109	.565	6.953	.000
	pct poverty	18.283	6.136	.265	2.980	.005
	pct single parent	89.401	17.836	.446	5.012	.000
a. Dependent Variable: violent crime rate

eg2 참조

elemapi2.sav

r.api00.outlierdetection.sps

descriptives /var= ALL .

graph
  /scatterplot(matrix)=api00 ell acs_k3 avg_ed meals .

This graph does not give any suspicious cases.

GRAPH /SCATTERPLOT(BIVAR)=ell with api00 .
GRAPH /SCATTERPLOT(BIVAR)=acs_k3 with api00  .
GRAPH /SCATTERPLOT(BIVAR)=avg_ed with api00 .
GRAPH /SCATTERPLOT(BIVAR)=meals with api00  .

We speculate that the second IV (average class size) is not quite related to DV (api00). And, there seems no particular suspicious data.

REGRESSION
  /DEPENDENT api00
  /METHOD=ENTER ell acs_k3 avg_ed meals 
   /residuals=histogram(sdresid lever) id(snum) outliers(sdresid, lever, cook)
  /casewise=plot(sdresid)  outliers(2) cook dffit
  /scatterplot(*lever, *sdresid)
  /save sdbeta(sdfb) 
   /partialplot.  

Let's say, we decide to opt out cases whose studentized deleted residual value exceed normal. We set the criterion as ABS(sdresid) > 2. These cases which meet this criterion will filtered out.

We need to save some residual statistics first, with regression method. Saved values include:
PRED
ZPRED
MAHAL
COOK
LEVER
RESID
ZRESID
SDRESID
DFBETA
Among them, we take a look at SDRESID, whose variable name will be SDR_1 in spss data set.

For the referece,

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA COLLIN TOL CHANGE ZPP
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT api00
  /METHOD=ENTER meals ell acs_k3 avg_ed
  /residuals=histogram(sdresid lever) id(snum) outliers(sdresid, lever, cook) Durbin
  /casewise=plot(sdresid)  outliers(2) cook dffit
  /SCATTERPLOT=(*ZRESID ,*ZPRED)
  /SAVE PRED ZPRED MAHAL COOK LEVER RESID ZRESID SDRESID DFBETA.

Then, we need to filter out cases whose SDR_1 value exceed:
abs(SDR_1) > 2
with the below command.

USE ALL.
COMPUTE filterVar=(abs(SDR)_1 < 2).
FILTER BY filterVar.
EXECUTE.

Then, we do regression again, excluding the suspicious cases. But, this time we do not save the residuals.

REGRESSION
  /DESCRIPTIVES MEAN STDDEV CORR SIG N
  /MISSING LISTWISE
  /STATISTICS COEFF OUTS R ANOVA CHANGE ZPP
  /CRITERIA=PIN(.05) POUT(.10)
  /NOORIGIN 
  /DEPENDENT api00
  /METHOD=ENTER ell avg_ed acs_k3 meals
  /SCATTERPLOT=(*ZRESID ,*ZPRED) .

Compare the ouptput between the previous and this regression.