Statistical Analysis
statisticsCompute descriptive statistics, quartiles, and IQR-based outlier detection for numerical data. Obtain count, mean, standard deviation, min, max, median, Q1, Q2, Q3, IQR, and outlier values. Select analysis types as needed.
Instructions
Comprehensive statistical analysis using Polars.
Analysis types: - describe: Count, mean, std, min, max, median - quartiles: Q1, Q2, Q3, IQR - outliers: IQR-based detection (values beyond Q1-1.5×IQR or Q3+1.5×IQR)
Examples:
DESCRIPTIVE STATISTICS: data=[1,2,3,4,5,100], analyses=["describe"] Result: {count:6, mean:19.17, std:39.25, min:1, max:100, median:3.5}
QUARTILES: data=[1,2,3,4,5], analyses=["quartiles"] Result: {Q1:2, Q2:3, Q3:4, IQR:2}
OUTLIER DETECTION: data=[1,2,3,4,5,100], analyses=["outliers"] Result: {outlier_values:[100], outlier_count:1, lower_bound:-1, upper_bound:8.5}
FULL ANALYSIS: data=[1,2,3,4,5,100], analyses=["describe","quartiles","outliers"] Result: All three analyses combined
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional annotation to label this calculation (e.g., 'Bond A PV', 'Q2 revenue'). Appears in results for easy identification. | |
| output_mode | No | Output format: full (default), compact, minimal, value, or final. See batch_execute tool for details. | full |
| data | Yes | List of numerical values (e.g., [1,2,3,4,5,100]) | |
| analyses | Yes | Types of analysis to perform |
Output Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |