Please advise me on the following problem.
I have attemped to conduct lienar regression analysis on a big sample dataset for several different variables. I have run into a problem, that on one of the variables the output is "No variables were entered into the equation." based on : REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT 18B /METHOD=STEPWISE 10 12 13A 13B 13C For the same type of variable with very simmilar frequencies on variable 18a it worked fine. And what is strange to me is  when I change it to "ORIGIN" with unselecting "Include constant in equation" it computes some output. I know that unselecting "Include constant in equation" is not an option. But can anyone guess what is the problem? What could I do? THX, TVP 
Check your variable frequencies (especially missing values). "LISTWISE" removes a case if any of the the variables has a missing value. Example: Variables 18B 10 12 13A 13B 13C Case 1 . 5 7 9 5 2 Case 2 3 . 5 8 4 5 Case 3 4 7 . 3 7 5 Case 4 5 9 5 . 2 1 Case 5 2 2 1 9 . 7 This list of cases will all be excluded from the analysis (they have a missing value "." for one of the variables used in the regression model). Jim Marks Sr Market Research Manager National Market Research Kaiser Foundation Health Plan of the MidAtlantic States, Inc. 2101 E. Jefferson St. Rockville, MD 20852 Phone: (301) 8166822 Cell Phone: (605) 9293262 NOTICE TO RECIPIENT: If you are not the intended recipient of this email, you are prohibited from sharing, copying, or otherwise using or disclosing its contents. If you have received this email in error, please notify the sender immediately by reply email and permanently delete this email and any attachments without reading, forwarding or saving them. Thank you. From: tvp <[hidden email]> To: [hidden email] Date: 03/22/2012 05:21 PM Subject: linear regression:No variables were entered into the equation. Sent by: "SPSSX(r) Discussion" <[hidden email]> Please advise me on the following problem. I have attemped to conduct lienar regression analysis on a big sample dataset for several different variables. I have run into a problem, that on one of the variables the output is "No variables were entered into the equation." based on : REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT 18B /METHOD=STEPWISE 10 12 13A 13B 13C For the same type of variable with very simmilar frequencies on variable 18a it worked fine. And what is strange to me is  when I change it to "ORIGIN" with unselecting "Include constant in equation" it computes some output. I know that unselecting "Include constant in equation" is not an option. But can anyone guess what is the problem? What could I do? THX, TVP  View this message in context: http://spssxdiscussion.1045642.n5.nabble.com/linearregressionNovariableswereenteredintotheequationtp5587824p5587824.html Sent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD 
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In reply to this post by tvp
You*might* notice in your output an error message to the effect that *ALL* of your variable names are *INVALID* ;)
From some manual somewhere (Syntax guide, universals section: REQUIRED reading IMNSHO). "Variable names can be up to 64 bytes long, and the first character must be a letter or one of the characters @, #, or $. Subsequent characters can be any combination of letters, numbers, nonpunctuation characters, and a period (.)."
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In reply to this post by Jim Marks
Thank you for your suggestion.
But I have a lot of cases N = 3069, of which valid = 2982 and missing= 87 for this variable 18b. And even when I included only 2 independent varibles (several different ones) there was the same Warning and no otuput. I don't think it is possible that with such a large database and only 2 independent variables (with each only around 40 missing) that is the problem. I'm still confused why it was computing the output when I unselected the inclusion of a constant... 
In reply to this post by David Marso
"You*might* notice in your output an error message to the effect that *ALL* of your variable names are *INVALID* ;)
From some manual somewhere (Syntax guide, universals section: REQUIRED reading IMNSHO). "Variable names can be up to 64 bytes long, and the first character must be a letter or one of the characters @, #, or $. Subsequent characters can be any combination of letters, numbers, nonpunctuation characters, and a period (.)." The variable names were only dummies for the discussion list, originally the names are BBC18A,.... 
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Perhaps have SPSS print the correlation matrix and post that along with means and SD?
Please post your actual syntax in the future. 
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"Perhaps have SPSS print the correlation matrix and post that along with means and SD?"
Please find the matrixes for two dependent (very simmilar) variables and each with two independent and also the regression for both dependent (it works for 21B, but not for 21A even though their characteristics look very much alike). Thanks in advance, TVP REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21A /METHOD=STEPWISE BTG02 BTG07. Regression Notes Comments Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 3069 Missing Value Handling Definition of Missing Userdefined missing values are treated as missing. Cases Used Statistics are based on cases with no missing values for any variable used. Syntax REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21A /METHOD=STEPWISE BTG02 BTG07. Resources Processor Time 00 00:00:00,093 Elapsed Time 00 00:00:00,109 Memory Required 8116 bytes Additional Memory Required for Residual Plots 0 bytes Warnings No variables were entered into the equation. REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21B /METHOD=STEPWISE BTG02 BTG07. Regression Notes Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 3069 Missing Value Handling Definition of Missing Userdefined missing values are treated as missing. Cases Used Statistics are based on cases with no missing values for any variable used. Syntax REGRESSION /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21B /METHOD=STEPWISE BTG02 BTG07. Resources Processor Time 00 00:00:00,047 Elapsed Time 00 00:00:00,048 Memory Required 8116 bytes Additional Memory Required for Residual Plots 0 bytes Variables Entered/Removed(a) Model Variables Entered Variables Removed Method 1 BACKGROUND/AGE GROUP . Stepwise (Criteria: ProbabilityofFtoenter <= ,050, ProbabilityofFtoremove >= ,100). a. Dependent Variable: APPRFED/FREQ/COLLEAGUES Model Summary Change Statistics Model R R Square Adjusted R Square Std. Error of the Estimate R Square Change F Change df1 df2 Sig. F Change 1 ,060a ,004 ,003 2,340 ,004 10,560 1 2922 ,001 a. Predictors: (Constant), BACKGROUND/AGE GROUP ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 57,804 1 57,804 10,560 ,001a Residual 15994,493 2922 5,474 Total 16052,297 2923 a. Predictors: (Constant), BACKGROUND/AGE GROUP b. Dependent Variable: APPRFED/FREQ/COLLEAGUES Coefficients(a) Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) 4,606 ,169 27,293 ,000 BACKGROUND/AGE GROUP ,142 ,044 ,060 3,250 ,001 a. Dependent Variable: APPRFED/FREQ/COLLEAGUES Excluded Variables(b) Collinearity Statistics Model Beta In t Sig. Partial Correlation Tolerance 1 BACKGROUND/HIGHEST LEVEL OF EDUCATION ,010a ,491 ,623 ,009 ,835 a. Predictors in the Model: (Constant), BACKGROUND/AGE GROUP b. Dependent Variable: APPRFED/FREQ/COLLEAGUES Coefficient Correlations(a) Model BACKGROUND/AGE GROUP 1 Correlations BACKGROUND/AGE GROUP 1,000 Covariances BACKGROUND/AGE GROUP ,002 a. Dependent Variable: APPRFED/FREQ/COLLEAGUES RELIABILITY /VARIABLES=BTG02 BTG07 BTG21A /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=CORR COV ANOVA /SUMMARY=MEANS VARIANCE COV CORR. Reliability Notes Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 3069 Matrix Input Missing Value Handling Definition of Missing Userdefined missing values are treated as missing. Cases Used Statistics are based on all cases with valid data for all variables in the procedure. Syntax RELIABILITY /VARIABLES=BTG02 BTG07 BTG21A /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=CORR COV ANOVA /SUMMARY=MEANS VARIANCE COV CORR. Resources Processor Time 00 00:00:00,031 Elapsed Time 00 00:00:00,031 Scale: ALL VARIABLES Case Processing Summary N % Cases Valid 2969 96,7 Excludeda 100 3,3 Total 3069 100,0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics Cronbach's Alphaa Cronbach's Alpha Based on Standardized Itemsa N of Items ,200 ,596 3 a. The value is negative due to a negative average covariance among items. This violates reliability model assumptions. You may want to check item codings. InterItem Correlation Matrix BACKGROUND/AGE GROUP BACKGROUND/HIGHEST LEVEL OF EDUCATION APPRFED/FREQ/PRINCIPAL BACKGROUND/AGE GROUP 1,000 ,411 ,002 BACKGROUND/HIGHEST LEVEL OF EDUCATION ,411 1,000 ,017 APPRFED/FREQ/PRINCIPAL ,002 ,017 1,000 InterItem Covariance Matrix BACKGROUND/AGE GROUP BACKGROUND/HIGHEST LEVEL OF EDUCATION APPRFED/FREQ/PRINCIPAL BACKGROUND/AGE GROUP ,984 ,243 ,003 BACKGROUND/HIGHEST LEVEL OF EDUCATION ,243 ,354 ,018 APPRFED/FREQ/PRINCIPAL ,003 ,018 3,042 Summary Item Statistics Mean Minimum Maximum Range Maximum / Minimum Variance N of Items Item Means 3,546 2,538 4,365 1,828 1,720 ,862 3 Item Variances 1,460 ,354 3,042 2,688 8,582 1,976 3 InterItem Covariances ,086 ,243 ,003 ,246 ,013 ,015 3 InterItem Correlations ,142 ,411 ,002 ,413 ,005 ,043 3 ANOVA Sum of Squares df Mean Square F Sig Between People 3823,831 2968 1,288 Within People Between Items 5116,961 2 2558,481 1654,907 ,000 Residual 9177,039 5936 1,546 Total 14294,000 5938 2,407 Total 18117,831 8906 2,034 Grand Mean = 3,55 RELIABILITY /VARIABLES=BTG02 BTG07 BTG21B /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=CORR COV ANOVA /SUMMARY=MEANS VARIANCE COV CORR. Reliability Notes Active Dataset DataSet1 Filter <none> Weight <none> Split File <none> N of Rows in Working Data File 3069 Matrix Input Missing Value Handling Definition of Missing Userdefined missing values are treated as missing. Cases Used Statistics are based on all cases with valid data for all variables in the procedure. Syntax RELIABILITY /VARIABLES=BTG02 BTG07 BTG21B /SCALE('ALL VARIABLES') ALL /MODEL=ALPHA /STATISTICS=CORR COV ANOVA /SUMMARY=MEANS VARIANCE COV CORR. Resources Processor Time 00 00:00:00,016 Elapsed Time 00 00:00:00,015 Scale: ALL VARIABLES Case Processing Summary N % Cases Valid 2924 95,3 Excludeda 145 4,7 Total 3069 100,0 a. Listwise deletion based on all variables in the procedure. Reliability Statistics Cronbach's Alphaa Cronbach's Alpha Based on Standardized Itemsa N of Items ,174 ,642 3 a. The value is negative due to a negative average covariance among items. This violates reliability model assumptions. You may want to check item codings. InterItem Correlation Matrix BACKGROUND/AGE GROUP BACKGROUND/HIGHEST LEVEL OF EDUCATION APPRFED/FREQ/COLLEAGUES BACKGROUND/AGE GROUP 1,000 ,406 ,060 BACKGROUND/HIGHEST LEVEL OF EDUCATION ,406 1,000 ,016 APPRFED/FREQ/COLLEAGUES ,060 ,016 1,000 InterItem Covariance Matrix BACKGROUND/AGE GROUP BACKGROUND/HIGHEST LEVEL OF EDUCATION APPRFED/FREQ/COLLEAGUES BACKGROUND/AGE GROUP ,977 ,239 ,139 BACKGROUND/HIGHEST LEVEL OF EDUCATION ,239 ,355 ,022 APPRFED/FREQ/COLLEAGUES ,139 ,022 5,492 Summary Item Statistics Mean Minimum Maximum Range Maximum / Minimum Variance N of Items Item Means 3,447 2,541 4,076 1,535 1,604 ,647 3 Item Variances 2,275 ,355 5,492 5,137 15,462 7,859 3 InterItem Covariances ,118 ,239 ,022 ,261 ,094 ,014 3 InterItem Correlations ,150 ,406 ,016 ,422 ,040 ,040 3 ANOVA Sum of Squares df Mean Square F Sig Between People 5955,790 2923 2,038 Within People Between Items 3783,075 2 1891,538 790,440 ,000 Residual 13989,591 5846 2,393 Total 17772,667 5848 3,039 Total 23728,456 8771 2,705 Grand Mean = 3,45 
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OK, we apparently have a failure to communicate.
We ONLY need to see the CORRELATION matrix, Means and SD, NOT everything else. Also, STEPWISE methods seem to be universally frowned upon by the data analysis community. For the data that 'worked' your R2 is .003 . Doesn't that tell you anything ? REGRESSION /DESCRIPTIVES CORR /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21A /METHOD=STEPWISE BTG02 BTG07.
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I'm not clear I totally understand what this lister is trying to do, but the first time I saw it, my first thought was that maybe none of the variables were significant, and the stepwise procedure didn't include any of the variables. Since you all had commented on other aspects I assumed I misunderstood the problem. Now having read this, I suspect that is the problem.
Given how poorly this model seems to fit, I think a back step should be taken, and the following addressed: Do all variables in the model meet minimum assumption requirements? Was the model based on an apriori theory? Do the variables differ substantially from those expected in the theory? While still an assumption, is the regression or correlation modeling approach appropriate for the nature of the data? Normally I would just say, this sounds like it didn't pan out, you failed to find evidence, tough. However, with a dataset of that size, its large enough that even spurious correlations are likely, and so to have such a low R squared makes me question if a more fundamental data problem exists. Matthew J Poes Research Data Specialist Center for Prevention Research and Development University of Illinois 510 Devonshire Dr. Champaign, IL 61820 Phone: 2172654576 email: [hidden email] Original Message From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of David Marso Sent: Friday, March 23, 2012 9:53 AM To: [hidden email] Subject: Re: linear regression:No variables were entered into the equation. OK, we apparently have a failure to communicate. We ONLY need to see the CORRELATION matrix, Means and SD, NOT everything else. Also, STEPWISE methods seem to be universally frowned upon by the data analysis community. For the data that 'worked' your R2 is .003 . Doesn't that tell you anything ? REGRESSION /DESCRIPTIVES CORR /MISSING LISTWISE /STATISTICS COEFF OUTS BCOV R ANOVA CHANGE /CRITERIA=PIN(.05) POUT(.10) /NOORIGIN /DEPENDENT BTG21A /METHOD=STEPWISE BTG02 BTG07.  View this message in context: http://spssxdiscussion.1045642.n5.nabble.com/linearregressionNovariableswereenteredintotheequationtp5587824p5589689.html Sent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD 
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Something *DODGY* is going on since the regression results *ARE NOT* consistent with the Correlations and such reported from the reliability procedure (or I am going blond from sleep deprivation).
Consider: MATRIX DATA /VARIABLES BTG21B BTG02 BTG07 /FORMAT LOWER /CONTENTS CORR STDDEV MEAN N_SCALAR. BEGIN DATA 1.000 .406 1.000 .060 .016 1.000 .9884 .5958 2.3435 5 5 5 2924. END DATA. REGRESSION /MATRIX IN(*) / DEP BTG21B / STEPWISE BTG02 BTG07. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .406(a) .165 .165 .9034267 2 .410(b) .168 .167 .9020310 a Predictors: (Constant), BTG02 b Predictors: (Constant), BTG02, BTG07 ANOVA(c) Model Sum of Squares df Mean Square F Sig. 1 Regression 470.702 1 470.702 576.714 .000(a) Residual 2384.877 2922 .816 Total 2855.580 2923 2 Regression 478.879 2 239.440 294.275 .000(b) Residual 2376.701 2921 .814 Total 2855.580 2923 a Predictors: (Constant), BTG02 b Predictors: (Constant), BTG02, BTG07 c Dependent Variable: BTG21B Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 8.368 .141 59.251 .000 BTG02 .674 .028 .406 24.015 .000 2 (Constant) 8.473 .145 58.478 .000 BTG02 .672 .028 .405 23.998 .000 BTG07 .023 .007 .054 3.170 .002 a Dependent Variable: BTG21B MATRIX DATA /VARIABLES BTG21A BTG02 BTG07 /FORMAT LOWER /CONTENTS CORR STDDEV MEAN N_SCALAR. BEGIN DATA 1.000 .411 1.000 .002 .017 1.000 .992 .595 1.744 0 0 0 2969. END DATA. REGRESSION /MATRIX IN(*) / DEP BTG21A / STEPWISE BTG02 BTG07. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .411(a) .169 .169 .9044946 a Predictors: (Constant), BTG02 ANOVA(b) Model Sum of Squares df Mean Square F Sig. 1 Regression 493.368 1 493.368 603.058 .000(a) Residual 2427.334 2967 .818 Total 2920.702 2968 a Predictors: (Constant), BTG02 b Dependent Variable: BTG21A Coefficients(a) Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .000 .017 .000 1.000 BTG02 .685 .028 .411 24.557 .000 a Dependent Variable: BTG21A
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First admitting that I did not read all of the output carefully, but looking at the correlations, covariance, and negative alphas, the only thing I can think of is maybe a suppression effect. It's very hard for me to follow this as is though, I would really need to have the lister give me all his variables, indicating which are IV and which are DV, and then a complete correlation table with all variables. I would then want to look at partial correlations and a twostep regression, and I may be able to see if its anything spooky like suppression effects.
Matthew J Poes Research Data Specialist Center for Prevention Research and Development University of Illinois 510 Devonshire Dr. Champaign, IL 61820 Phone: 2172654576 email: [hidden email] Original Message From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of David Marso Sent: Friday, March 23, 2012 12:56 PM To: [hidden email] Subject: Re: linear regression:No variables were entered into the equation. Something *DODGY* is going on since the regression results *ARE NOT* consistent with the Correlations and such reported from the reliability procedure (or I am going blond from sleep deprivation). Consider: MATRIX DATA /VARIABLES BTG21B BTG02 BTG07 /FORMAT LOWER /CONTENTS CORR STDDEV MEAN N_SCALAR. BEGIN DATA 1.000 .406 1.000 .060 .016 1.000 .9884 .5958 2.3435 5 5 5 2924. END DATA. REGRESSION /MATRIX IN(*) / DEP BTG21B / STEPWISE BTG02 BTG07. *Model Summary* Model R R Square Adjusted R Square Std. Error of the Estimate 1 .406(a) .165 .165 .9034267 2 .410(b) .168 .167 .9020310 a Predictors: (Constant), BTG02 b Predictors: (Constant), BTG02, BTG07 *ANOVA(c)* Model Sum of Squares df Mean Square F Sig. 1 Regression 470.702 1 470.702 576.714 .000(a) Residual 2384.877 2922 .816 Total 2855.580 2923 2 Regression 478.879 2 239.440 294.275 .000(b) Residual 2376.701 2921 .814 Total 2855.580 2923 a Predictors: (Constant), BTG02 b Predictors: (Constant), BTG02, BTG07 c Dependent Variable: BTG21B *Coefficients(a)* Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 8.368 .141 59.251 .000 BTG02 .674 .028 .406 24.015 .000 2 (Constant) 8.473 .145 58.478 .000 BTG02 .672 .028 .405 23.998 .000 BTG07 .023 .007 .054 3.170 .002 a Dependent Variable: BTG21B MATRIX DATA /VARIABLES BTG21A BTG02 BTG07 /FORMAT LOWER /CONTENTS CORR STDDEV MEAN N_SCALAR. BEGIN DATA 1.000 .411 1.000 .002 .017 1.000 .992 .595 1.744 0 0 0 2969. END DATA. REGRESSION /MATRIX IN(*) / DEP BTG21A / STEPWISE BTG02 BTG07. *Model Summary* Model R R Square Adjusted R Square Std. Error of the Estimate 1 .411(a) .169 .169 .9044946 a Predictors: (Constant), BTG02 *ANOVA(b)* Model Sum of Squares df Mean Square F Sig. 1 Regression 493.368 1 493.368 603.058 .000(a) Residual 2427.334 2967 .818 Total 2920.702 2968 a Predictors: (Constant), BTG02 b Dependent Variable: BTG21A *Coefficients(a)* Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) .000 .017 .000 1.000 BTG02 .685 .028 .411 24.557 .000 a Dependent Variable: BTG21A  View this message in context: http://spssxdiscussion.1045642.n5.nabble.com/linearregressionNovariableswereenteredintotheequationtp5587824p5590304.html Sent from the SPSSX Discussion mailing list archive at Nabble.com. ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD ===================== To manage your subscription to SPSSXL, send a message to [hidden email] (not to SPSSXL), with no body text except the command. To leave the list, send the command SIGNOFF SPSSXL For a list of commands to manage subscriptions, send the command INFO REFCARD 
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