What is the difference between Z-Score and Robust Z-Score?

Standard Z-Score uses mean and standard deviation, which are sensitive to outliers. Robust Z-Score uses median and Median Absolute Deviation (MAD), making it more reliable when your data contains extreme values.

Can I process non-numeric columns?

Yes, the tool is designed to preserve non-numeric columns, ensuring your original data structure remains intact while only transforming the selected numerical fields.

How does the tool handle missing values?

You can choose to skip rows containing missing values or fill them using the mean, median, mode, or zero to maintain dataset consistency.

What is the outlier threshold?

The threshold defines how many standard deviations a value must be from the mean to be flagged as an outlier. The default is 2, but you can adjust this based on your specific requirements.

Can I scale the output to a specific range?

Yes, you can provide a custom output range to map your standardized data to a specific interval, such as 0 to 1, if required for your analysis.

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Data Processing

Z-Score Standardizer

Standardize numerical data using Z-score (standard score) normalization to transform values with mean=0 and standard deviation=1. Perfect for statistical analysis, machine learning feature preprocessing, outlier detection, and data comparison across different scales. Features: - Z-score standardization (mean=0, std=1) - Robust Z-score option (using median and MAD) - Custom scaling to target range - Multiple column selection - Automatic data type detection - Handles missing values intelligently - Preserves non-numeric columns - Comprehensive statistical summary - Outlier detection and reporting Common Use Cases: - Machine learning feature preparation - Statistical hypothesis testing - Outlier detection and removal - Data comparison across different units - Principal Component Analysis (PCA) preprocessing

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10 options

ContentPaste or type the main input values.3

CSV DatatextareaRequiredTarget ColumnstextareaOptionalOutput Range (Optional)textOptionalOptional: Scale standardized values to target range. Leave empty for standard z-score output.

SettingsAdjust formats, ranges, numbers, and modes.4

Standardization TypeselectOptionalHandle Missing ValuesselectOptionalDecimal PlacesnumberOptionalOutlier Threshold (Std Dev)numberOptionalValues beyond this many standard deviations will be flagged as outliers

TogglesEnable or disable optional behavior.3

Preserve Original ColumnscheckboxOptionalEnabled when checkedInclude StatisticscheckboxOptionalEnabled when checkedDetect OutlierscheckboxOptionalEnabled when checked

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Z-Score Standardizer

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1. Preparing ML Features

2. Outlier Detection in Sensor Data

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