Mplus VERSION 7.3 MUTHEN & MUTHEN 06/08/2017 12:03 PM INPUT INSTRUCTIONS TITLE: this is an example of a simple linear regression for a continuous observed dependent variable with two covariates DATA: FILE IS regression.dat; VARIABLE: NAMES ARE y1 x1 x2; ANALYSIS: ESTIMATOR is ML; MODEL: y1 on x1 x2; OUTPUT: stand sampstat cinterval; PLOT: TYPE = PLOT2; INPUT READING TERMINATED NORMALLY this is an example of a simple linear regression for a continuous 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 Continuous Y1 Observed independent variables X1 X2 Estimator ML Information matrix OBSERVED Maximum number of iterations 1000 Convergence criterion 0.500D-04 Maximum number of steepest descent iterations 20 Input data file(s) regression.dat Input data format FREE SAMPLE STATISTICS SAMPLE STATISTICS Means Y1 X1 X2 ________ ________ ________ 1 0.485 0.001 -0.042 Covariances Y1 X1 X2 ________ ________ ________ Y1 2.408 X1 1.078 1.094 X2 0.648 0.028 0.957 Correlations Y1 X1 X2 ________ ________ ________ Y1 1.000 X1 0.665 1.000 X2 0.427 0.028 1.000 UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS Variable/ Mean/ Skewness/ Minimum/ % with Percentiles Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median Y1 0.485 -0.012 -4.116 0.20% -0.768 0.070 0.429 500.000 2.408 -0.136 5.111 0.20% 0.777 1.894 X1 0.001 -0.133 -3.145 0.20% -0.922 -0.235 0.023 500.000 1.094 -0.162 2.920 0.20% 0.304 0.876 X2 -0.042 -0.057 -3.139 0.20% -0.921 -0.353 -0.040 500.000 0.957 -0.357 2.875 0.20% 0.274 0.859 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 4 Loglikelihood H0 Value -694.334 H1 Value -694.334 Information Criteria Akaike (AIC) 1396.667 Bayesian (BIC) 1413.526 Sample-Size Adjusted BIC 1400.830 (n* = (n + 2) / 24) Chi-Square Test of Model Fit Value 0.000 Degrees of Freedom 0 P-Value 0.0000 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 469.585 Degrees of Freedom 2 P-Value 0.0000 SRMR (Standardized Root Mean Square Residual) Value 0.000 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Y1 ON X1 0.969 0.042 23.356 0.000 X2 0.649 0.044 14.626 0.000 Intercepts Y1 0.511 0.043 11.765 0.000 Residual Variances Y1 0.941 0.060 15.811 0.000 STANDARDIZED MODEL RESULTS STDYX Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value Y1 ON X1 0.653 0.024 26.863 0.000 X2 0.409 0.028 14.516 0.000 Intercepts Y1 0.329 0.030 11.081 0.000 Residual Variances Y1 0.391 0.027 14.326 0.000 STDY Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value Y1 ON X1 0.625 0.022 28.069 0.000 X2 0.418 0.027 15.597 0.000 Intercepts Y1 0.329 0.030 11.081 0.000 Residual Variances Y1 0.391 0.027 14.326 0.000 STD Standardization Two-Tailed Estimate S.E. Est./S.E. P-Value Y1 ON X1 0.969 0.042 23.356 0.000 X2 0.649 0.044 14.626 0.000 Intercepts Y1 0.511 0.043 11.765 0.000 Residual Variances Y1 0.941 0.060 15.811 0.000 R-SQUARE Observed Two-Tailed Variable Estimate S.E. Est./S.E. P-Value Y1 0.609 0.027 22.316 0.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.483E+00 (ratio of smallest to largest eigenvalue) CONFIDENCE INTERVALS OF MODEL RESULTS Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Y1 ON X1 0.863 0.888 0.901 0.969 1.038 1.051 1.076 X2 0.535 0.562 0.576 0.649 0.722 0.736 0.763 Intercepts Y1 0.399 0.426 0.440 0.511 0.582 0.596 0.623 Residual Variances Y1 0.788 0.825 0.843 0.941 1.039 1.058 1.095 CONFIDENCE INTERVALS OF STANDARDIZED MODEL RESULTS STDYX Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Y1 ON X1 0.591 0.606 0.613 0.653 0.693 0.701 0.716 X2 0.337 0.354 0.363 0.409 0.456 0.464 0.482 Intercepts Y1 0.253 0.271 0.280 0.329 0.378 0.388 0.406 Residual Variances Y1 0.321 0.337 0.346 0.391 0.436 0.444 0.461 STDY Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Y1 ON X1 0.567 0.581 0.588 0.625 0.661 0.668 0.682 X2 0.349 0.366 0.374 0.418 0.462 0.471 0.487 Intercepts Y1 0.253 0.271 0.280 0.329 0.378 0.388 0.406 Residual Variances Y1 0.321 0.337 0.346 0.391 0.436 0.444 0.461 STD Standardization Lower .5% Lower 2.5% Lower 5% Estimate Upper 5% Upper 2.5% Upper .5% Y1 ON X1 0.863 0.888 0.901 0.969 1.038 1.051 1.076 X2 0.535 0.562 0.576 0.649 0.722 0.736 0.763 Intercepts Y1 0.399 0.426 0.440 0.511 0.582 0.596 0.623 Residual Variances Y1 0.788 0.825 0.843 0.941 1.039 1.058 1.095 PLOT INFORMATION The following plots are available: No plots are available DIAGRAM INFORMATION Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram. If running Mplus from the Mplus Diagrammer, the diagram opens automatically. Diagram output c:\users\lionb\dropbox\exercises - naomi\bayes - 1\1. mplus\solution folder\exercise 1\regressio Beginning Time: 12:03:54 Ending Time: 12:03:55 Elapsed Time: 00:00:01 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2014 Muthen & Muthen