Output - Aster Analytics

Teradata Aster® Analytics Foundation User GuideUpdate 2

Product

Aster Analytics

Release Number

7.00.02

Published

September 2017

Language

English (United States)

Last Update

2018-04-17

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uce1497542673292.ditamap

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AA-notempfilter_pdf_output.ditaval

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B700-1022

lifecycle

Product Category

Software

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
...	...	...	...	...	...	...	...