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linear regression:No variables were entered into the equation.

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tvp
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linear regression:No variables were entered into the equation.

tvp
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 un-selecting "Include constant in equation" it computes some output.

I know that un-selecting "Include constant in equation" is not an option. But can anyone guess what is the problem? What could I do?

THX,
TVP
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Re: linear regression:No variables were entered into the equation.

Jim Marks

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 Mid-Atlantic States, Inc.
2101 E. Jefferson St.
Rockville, MD 20852
Phone: (301) 816-6822
Cell Phone: (605) 929-3262

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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
un-selecting "Include constant in equation" it computes some output.

I know that un-selecting "Include constant in equation" is not an option.
But can anyone guess what is the problem? What could I do?

THX,
TVP

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Re: linear regression:No variables were entered into the equation.

David Marso
Administrator
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 (.)."
 
tvp wrote
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 un-selecting "Include constant in equation" it computes some output.

I know that un-selecting "Include constant in equation" is not an option. But can anyone guess what is the problem? What could I do?

THX,
TVP
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me.
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Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?"
tvp
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Re: linear regression:No variables were entered into the equation.

tvp
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 un-selected the inclusion of a constant...
tvp
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Re: linear regression:No variables were entered into the equation.

tvp
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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Re: linear regression:No variables were entered into the equation.

David Marso
Administrator
Perhaps have SPSS print the correlation matrix and post that along with means and SD?
Please post your actual syntax in the future.
--
tvp wrote
"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,....
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me.
---
"Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis."
Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?"
tvp
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Re: linear regression:No variables were entered into the equation.

tvp
"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 User-defined 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 User-defined 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: Probability-of-F-to-enter <= ,050, Probability-of-F-to-remove >= ,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 User-defined 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.


        Inter-Item 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

        Inter-Item 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
Inter-Item Covariances -,086 -,243 ,003 ,246 -,013 ,015 3
Inter-Item 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 User-defined 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.


        Inter-Item 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

        Inter-Item 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
Inter-Item Covariances -,118 -,239 ,022 ,261 -,094 ,014 3
Inter-Item 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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Re: linear regression:No variables were entered into the equation.

David Marso
Administrator
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.
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me.
---
"Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis."
Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?"
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Re: linear regression:No variables were entered into the equation.

Poes, Matthew Joseph-2
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 a-priori 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: 217-265-4576
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://spssx-discussion.1045642.n5.nabble.com/linear-regression-No-variables-were-entered-into-the-equation-tp5587824p5589689.html
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Re: linear regression:No variables were entered into the equation.

David Marso
Administrator
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
Please reply to the list and not to my personal email.
Those desiring my consulting or training services please feel free to email me.
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"Nolite dare sanctum canibus neque mittatis margaritas vestras ante porcos ne forte conculcent eas pedibus suis."
Cum es damnatorum possederunt porcos iens ut salire off sanguinum cliff in abyssum?"
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Re: linear regression:No variables were entered into the equation.

Poes, Matthew Joseph-2
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 two-step 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: 217-265-4576
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

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