OLS and Quantile Regression Coefficients for Patient Delay Intervention

A 9.5 KB Excel file contains regression coefficient estimates for an intervention's impact on patient delay. Ridhima Sodhi authored this dataset, which was last updated in April 2026. The data presents estimated coefficients and their 95% confidence intervals from OLS and quantile regression models.

Use Cases

• Compare OLS and quantile regression coefficient estimates for the intervention variable to understand central vs. distributional effects.
• Analyze the 95% confidence intervals for each coefficient to assess statistical significance of the intervention's impact.
• Use the estimated coefficients from the patient delay model to simulate intervention outcomes under different assumptions.

Strengths

• Dataset is openly licensed under CC-BY-4.0 for flexible reuse.
• Provides specific statistical outputs (coefficients and 95% C.I.) for direct analysis.

Limitations

• Extremely small scale at 9.5 KB, indicating a summary table, not raw patient-level data.
• Lacks raw observational data, preventing model validation or alternative analysis.
• Unknown sample size and geographic origin limit generalizability of findings.

Provenance

Source

figshare, author Ridhima Sodhi.

Collection Method

Results from statistical regression analysis (OLS and quantile) on an unspecified underlying dataset.

Time Range

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Freshness

Last updated in April 2026.

Geography

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