7.00.02 - Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Release Date
September 2017
Content Type
Programming Reference
User Guide
Publication ID
B700-1022-700K
Language
English (United States)

The model starts with 33 degrees of freedom and then consecutively increases the degrees of freedom to 39, at which point the response is modeled with only the intercept. The model parameters are obtained progressively by dropping one predictor variable.

GLM Example 2 Model Statistics
predictor estimate std_error z_score p_value significance
(Intercept) 1.07751 2.92076 0.368914 0.712192  
masters.no 2.21655 1.01999 2.17311 0.0297719 *
gpa -0.113935 0.802573 -0.141962 0.88711  
stats.Novice 0.0406848 1.11567 0.0364667 0.97091  
stats.Beginner 0.526618 1.2229 0.430631 0.666736  
programming.Beginner -1.76976 1.069 -1.65553 0.0978177 .
programming.Novice -0.98035 1.14004 -0.859923 0.389831  
ITERATIONS # 4 0 0 0 Number of Fisher Scoring iterations
ROWS # 40 0 0 0 Number of rows
Residual deviance 38.9038 0 0 0 on 33 degrees of freedom
Pearson goodness of fit 37.7905 0 0 0 on 33 degrees of freedom
AIC 52.9038 0 0 0 Akaike information criterion
BIC 64.726 0 0 0 Bayesian information criterion
Wald Test 9.89642 0 0 0.19452  
Dispersion parameter 1 0 0 0 Taken to be 1 for BINOMIAL and POISSON.
.... .... .... .... .... ....
Residual deviance 44.7694 0 0 0 on 34 degrees of freedom
Pearson goodness of fit 39.895 0 0 0 on 34 degrees of freedom
AIC 56.7694 0 0 0 Akaike information criterion
BIC 66.9027 0 0 0 Bayesian information criterion
.... .... .... .... .... ....
Residual deviance 41.8984 0 0 0 on 35 degrees of freedom
Pearson goodness of fit 41.8616 0 0 0 on 35 degrees of freedom
AIC 51.8984 0 0 0 Akaike information criterion
BIC 60.3428 0 0 0 Bayesian information criterion
... .... .... .... .... ....
Residual deviance 39.1062 0 0 0 on 36 degrees of freedom
Pearson goodness of fit 37.9515 0 0 0 on 36 degrees of freedom
AIC 47.1062 0 0 0 Akaike information criterion
BIC 53.8617 0 0 0 Bayesian information criterion
.... .... .... .... .... ....
Residual deviance 45.6566 0 0 0 on 37 degrees of freedom
Pearson goodness of fit 40 0 0 0 on 37 degrees of freedom
AIC 51.6566 0 0 0 Akaike information criterion
BIC 56.7232 0 0 0 Bayesian information criterion
... .... .... .... .... ....
Residual deviance 42.8744 0 0 0 on 38 degrees of freedom
Pearson goodness of fit 40 0 0 0 on 38 degrees of freedom
AIC 46.8744 0 0 0 Akaike information criterion
BIC 50.2522 0 0 0 Bayesian information criterion
.... .... .... .... .... ....
(Intercept) 0.619039 0.331497 1.86741 0.0618448 .
ITERATIONS # 3 0 0 0 Number of Fisher Scoring iterations
ROWS # 40 0 0 0 Number of rows
Residual deviance 51.7958 0 0 0 on 39 degrees of freedom
Pearson goodness of fit 40 0 0 0 on 39 degrees of freedom
AIC 53.7958 0 0 0 Akaike information criterion
BIC 55.4847 0 0 0 Bayesian information criterion
Wald Test 3.48721 0 0 0.0618447 .
Dispersion parameter 1 0 0 0 Taken to be 1 for BINOMIAL and POISSON.

The query below returns the output shown in the following table:

SELECT * FROM glm_admissions_model1 ORDER BY attribute;
GLM Example 2 Output Table
attribute predictor category estimate std_err z_score p_value significance
-1 Loglik   -20.9492 40 4 0  
-1 Loglik   -19.5493 40 4 0  
-1 Loglik   -19.4621 40 5 0  
-1 Loglik   -22.8283 40 2 0  
-1 Loglik   -19.4519 40 6 0  
-1 Loglik   -25.8979 40 0 0  
-1 Loglik   -21.4127 40 2 0  
-1 Loglik   -19.5531 40 3 0  
-1 Loglik   -22.3847 40 5 0  
-1 Loglik   -21.4372 40 1 0  
-1 Loglik   -22.8158 40 3 0  
0 (Intercept)   1.07751 2.92076 0.368914 0.712192  
0 (Intercept)   1.3463 2.81931 0.477529 0.632985  
0 (Intercept)   0.676415 0.722718 0.935932 0.349308  
0 (Intercept)   0.989811 2.90188 0.341094 0.733033  
0 (Intercept)   0.666854 2.75228 0.242292 0.808554  
0 (Intercept)   1.04008 2.7457 0.378802 0.704835  
... ... ... ... ... ... ... ...