# Bell Curve

The SPC histogram can overlay a Bell Curve (also known as a normal distribution curve) on the histogram. This feature helps in visualizing how well the data fits a normal distribution and in identifying any deviations from normality.

## Gaussian Curve

The Gaussian bell curve fits a normal distribution to your data. It lets you compare your actual data to an ideal normal distribution. This helps spot differences from normal, which can help improve processes.

We use the Levenberg-Marquardt algorithm to fit the curve. This method solves non-linear least squares problems. It works by slowly changing the Gaussian function's values (height, center, and spread) to make the curve match the histogram data as closely as possible.

We use the ml-levenberg-marquardt library for this. It ensures we get an accurate Gaussian distribution that best fits your data.

## Histogram Curve

The histogram bell curve shows a smooth view of how your data is spread out. It makes it easier to see overall patterns in your data. We make this curve by simply connecting the middle points of each histogram bar. This gives a simple picture of how your data is distributed.

# Visualization

You can change how a curve line looks:

- Fit - How to fit a curve to histogram data
- Series - Select the series for which to calculate the control (if you have more than one)
- Line width - Line thickness from 0 to 10
- Color