Plugins 〉SPC Histogram


Developer
Kenso Software


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SPC Histogram

  • Overview
  • Installation
  • Change log
  • Related content

SPC Histogram

Main

Welcome to the KensoBI SPC Histogram panel for Grafana. This plugin enables you to easily create statistical process control (SPC) histograms, including Xbar-R, XbarS, and XmR charts. It automatically calculates and displays control limits on the histogram as vertical lines, with options to add your own custom limits. Additionally, you can group your samples into subgroups and aggregate them using methods such as moving range, range, mean, or standard deviation.

Features

  • Xbar-R, XbarS, and XmR Charts: Create various types of SPC charts.
  • Automatic Control Limits: LCL, UCL and mean control limits are automatically calculated and displayed.
  • Custom Limits: Add your own limits for more tailored analysis.
  • Subgrouping: Group your samples into subgroups and aggregate it.
  • Aggregation: Aggregate your data by moving range, range, mean, or standard deviation.
  • Histogram Bell Curve Visualize the distribution of your data with a histogram bell curve overlay.
  • Gaussian Bell Curve Add a Gaussian (normal) distribution curve to your histogram for comparison and analysis.

Histogram Curve

Histogram curve

The histogram bell curve provides a smoothed visualization of your data distribution, making it easier to observe overall patterns and trends in the dataset. This curve is created by simply connecting the midpoints of each histogram bin, offering a straightforward representation of the data distribution.

Gaussian Curve

Gaussian bell curve

The Gaussian bell curve fits a normal distribution to your data, allowing for a direct comparison between actual data and the ideal normal distribution. This highlights deviations from normality, aiding in process analysis and improvement opportunities.

The Gaussian fit is performed using the Levenberg-Marquardt algorithm, a popular method for solving non-linear least squares problems. This algorithm iteratively adjusts the parameters of the Gaussian function (amplitude, mean, and standard deviation) to minimize the difference between the fitted curve and the actual histogram data.

The implementation uses the ml-levenberg-marquardt library to perform the fitting process, ensuring an accurate representation of the Gaussian distribution that best matches your data.

Getting Help

If you have any questions or feedback, you can:

Your feedback is always welcome!

License

This software is distributed under the AGPL-3.0-only.

Notes

Copyright (c) 2024 Kenso Software

Installing SPC Histogram on Grafana Cloud:

For more information, visit the docs on plugin installation.

Changelog