statistical-distribution-and-outlier-analysis

IQR outlier detection computes quartiles and bounds to flag abnormal numeric values in your Excel spreadsheet. By loading data into a DataFrame and applying the 1.5×IQR rule, you generate a summary table of anomalies for quality checks.

Regex pattern matching extracts numeric error terms from text containing the ± symbol in your spreadsheet. The extracted values are then visualized using a histogram to display their frequency distribution.

Yes, you can generate boxplots for all numeric columns in a spreadsheet. The analysis creates high-resolution boxplot PNG artifacts comparing dispersion, median, and overall spread across columns for reporting workflows.

The best way to audit data quality and visualize distribution in Excel is to generate per-column boxplots alongside IQR-based outlier detection. This combination flags abnormal values and exports summarized CSV and PNG artifacts for anomaly auditing.

Yes, outlier detection works with laboratory reporting workflows where values and ±-formatted error strings coexist. It supports anomaly auditing by extracting error terms via regex and generating boxplots for numeric measurements.