Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:51 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a probit regression
          for a binary or categorical observed
          dependent variable with two covariates
  DATA:	FILE IS ex3.4.dat;
  VARIABLE:	NAMES ARE u1 x1 x3;
  	CATEGORICAL = u1;
  MODEL:	u1 ON x1 x3;



INPUT READING TERMINATED NORMALLY



this is an example of a probit regression
for a binary or categorical observed
dependent variable with two covariates

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    1
Number of independent variables                                  2
Number of continuous latent variables                            0

Observed dependent variables

  Binary and ordered categorical (ordinal)
   U1

Observed independent variables
   X1          X3


Estimator                                                    WLSMV
Maximum number of iterations                                  1000
Convergence criterion                                    0.500D-04
Maximum number of steepest descent iterations                   20
Parameterization                                             DELTA

Input data file(s)
  ex3.4.dat

Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.642      321.000
      Category 2    0.358      179.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        3

Chi-Square Test of Model Fit

          Value                              0.000*
          Degrees of Freedom                     0
          P-Value                           0.0000

*   The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
    for chi-square difference testing in the regular way.  MLM, MLR and WLSM
    chi-square difference testing is described on the Mplus website.  MLMV, WLSMV,
    and ULSMV difference testing is done using the DIFFTEST option.

RMSEA (Root Mean Square Error Of Approximation)

          Estimate                           0.000
          90 Percent C.I.                    0.000  0.000
          Probability RMSEA <= .05           0.000

CFI/TLI

          CFI                                1.000
          TLI                                1.000

Chi-Square Test of Model Fit for the Baseline Model

          Value                            193.243
          Degrees of Freedom                     2
          P-Value                           0.0000

WRMR (Weighted Root Mean Square Residual)

          Value                              0.001



MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

 U1       ON
    X1                 1.023      0.121      8.459      0.000
    X3                 2.474      0.224     11.029      0.000

 Thresholds
    U1$1               0.984      0.119      8.299      0.000


R-SQUARE

    Observed                   Residual
    Variable        Estimate   Variance

    U1                 0.877      1.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.427E+00
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:51:47
        Ending Time:  22:51:47
       Elapsed Time:  00:00:00



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