Glm run #189 · statistical_test
1.309e-112
p value
poisson
family
459.4
aic
93.87
deviance
completed
status
2026-09-21 02:16
created
Figures
Coefficient estimates (95% CI)
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Observed vs fitted
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Residuals vs fitted
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Coefficients 2 rows
| term | coef | se | z | p | ci_lower | ci_upper |
|---|---|---|---|---|---|---|
| const | 0.5233421134781986 | 0.09522560198593588 | 5.495813127602935 | 3.889141824193585e-08 | 0.3367033631796184 | 0.7099808637767788 |
| predictor | 0.2920514985807683 | 0.01295064077366805 | 22.55112342970576 | 1.3091962755490587e-112 | 0.266668709087663 | 0.3174342880738736 |
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
| Parameter | Value |
|---|---|
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 smmodel = sm.GLM(y_counts, X_predictors, family=sm.families.Poisson())fit = model.fit()