Result Detail – Glm

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Result Information
Result ID
189
Type
Glm
Status
completed
Dataset ID
708
Created
2026-09-21 02:16:49.944469
Statistical Test Result
Test
glm — glm
Statistic
—
p-value
1.309e-112
Interpretation
significant Poisson GLM: count ~ predictor — smallest predictor p = 1.309e-112
Value Column
count
AIC
459.45 pseudo-R² = 0.8638
Dispersion (Pearson φ)
0.926 — deviance GOF p = 0.5992
Observations
100
Model coefficients
TermCoefSE95% CIzp
const 0.5233 0.0952 [0.337, 0.710] 5.496 3.889e-08
predictor 0.2921 0.0130 [0.267, 0.317] 22.551 1.309e-112
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 #189)
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/775627731dca4c13a90b35a39d9a2c5e Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:16:49.944469

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()