Poisson Regression Model Results for New York Rainfall Impact on Test Counts

New York model results from a study analyzing the effect of rainfall on test counts. The dataset contains results from various Poisson regression model setups, including Prophet configurations and weekly components, with AIC values and effect estimates. It was authored by Susanna Caruso and last updated on 2026-04-29.

Use Cases

• Comparing model performance based on Akaike Information Criterion (AIC) values mentioned in the description
• Estimating the percentage change in test counts associated with 25mm of rainfall based on the reported effect column
• Analyzing the influence of different Prophet setups (additive or multiplicative) and weekly components on model outcomes
• Investigating spatial autocorrelation and area-level random effects as reported in the last two columns

Strengths

• Includes Akaike Information Criterion (AIC) for direct model comparison
• Reports effect sizes for rainfall with 95% confidence intervals
• File size is 5.5 KB, ensuring quick download and inspection

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download

Provenance

Source

figshare

Collection Method

Results from statistical modeling (Poisson regression) with Prophet components.

Time Range

null

Freshness

Last updated 2026-04-29 17:31:51; freshness should be verified

Geography

New York