Output - Aster Analytics

Teradata Aster Analytics Foundation User Guide
Product
Aster Analytics
Release Number
6.21
Published
November 2016
Language
English (United States)
Last Update
2018-04-14
dita:mapPath
kiu1466024880662.ditamap
dita:ditavalPath
AA-notempfilter_pdf_output.ditaval
dita:id
B700-1021
lifecycle
previous
Product Category
Software

The output table shows the model statistics.

GLM Example 1 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.

For categorical variables, the model selects a reference category. In this example, the Advanced category was used as a reference for the stats variable.

The query below returns the output shown in the following two tables:

SELECT * FROM glm_admissions_model ORDER BY attribute;
GLM Example 1 Output Table (Columns 1-4)
attribute predictor category estimate
-1 Loglik   -19.4519
0 (Intercept)   1.0775
1 masters yes  
2 masters no 2.21655
3 gpa   -0.113935
4 stats Advanced  
5 stats Novice 0.0406848
6 stats Beginner 0.526618
7 programming Advanced  
8 programming Beginner -1.76976
9 programming Novice -0.98035
GLM Example 1 Output Table (Columns 5-8)
std_err z_score p_value significance
40 6 0  
2.92076 0.368914 0.712192  
       
1.01999 2.17311 0.0297719 *
0.802573 -0.141962 0.88711  
       
1.11567 0.0364667 0.97091  
1.2229 0.430631 0.666736  
       
1.069 -1.65553 0.0978177 .
1.14004 -0.859923 0.389831