Result Detail – Glm

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Result Information
Result ID
190
Type
Glm
Status
completed
Dataset ID
708
Created
2026-09-21 02:16:53.486314
Statistical Test Result
Test
glm — glm
Statistic
—
p-value
4.918e-15
Interpretation
significant Negative Binomial GLM: count ~ predictor — smallest predictor p = 4.918e-15
Value Column
count
AIC
593.50 pseudo-R² = 0.7891
Dispersion (Pearson φ)
0.139 — deviance GOF p = 1
Observations
100
Model coefficients
TermCoefSE95% CIzp
const 0.4956 0.2161 [0.072, 0.919] 2.293 0.02185
predictor 0.2969 0.0379 [0.223, 0.371] 7.829 4.918e-15
Figures
Coefficient estimates (95% CI) Download PNG
Coefficient estimates (95% CI)
Observed vs fitted Download PNG
Observed vs fitted
Residuals vs fitted Download PNG
Residuals vs fitted
Pathway Analysis (same dataset) Help
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Method & Provenance

Method
Count GLM (Poisson / Negative Binomial)
Method key
glm (result type: statistical_test, result #190)
Engine
Python · statsmodels v0.14.6
Dataset(s)
#708 gallery — example_glm_counts.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/d9e9c78bde024ef2898ba52fc898c183 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:16:53.486314

Parameters used

ParameterValue
test_type glm
value_col count
predictor_cols predictor

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

import statsmodels.api as sm
model = sm.GLM(y_counts, X_predictors, family=sm.families.Poisson())
fit = model.fit()