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Cpk/Histogram

Assess process capability by visualizing your data distribution against specification limits.

How-To

Create your first Cpk/Histogram in less than two minutes →

What is a Cpk/Histogram?

A Cpk/Histogram combines a histogram with process capability statistics. It shows your data distribution as bars, overlays normal curves (using within and overall standard deviation), and displays specification limit lines (USL, LSL, and optionally a Target value). Quantum XL calculates capability indices including Cp, Cpk, Pp, and Ppk, along with PPM (parts per million) defect rates.

The tool also supports distribution fitting for non-normal data (14 distributions compared, 11 selectable as the fitted distribution), data transformations (Box-Cox, Johnson), and confidence intervals for capability indices.

One tool, two ribbon buttons

The Capability Analysis and Histogram ribbon buttons open this same tool. Capability Analysis starts with the capability view (statistics and PDF curve, no bars), Histogram starts with the histogram view (bars, no curve), and either view can be changed afterward. The statistics and options are identical.

When to Use a Cpk/Histogram

  • When you need to determine if a process meets its specification limits
  • When you want to quantify process capability with Cpk and Ppk indices
  • When you need to visualize where your data falls relative to USL and LSL
  • When you want to assess the normality of your process data
  • When you need to fit non-normal distributions and calculate capability from them

Example Output

A typical Cpk/Histogram shows bars representing the data distribution, red vertical lines at USL and LSL, overlaid normal curves, and a statistics table alongside with capability indices (Cpk, Ppk), PPM defect rates, and confidence intervals.

Learn More

  • How-To: Step-by-step tutorials for different scenarios
  • Preparing Your Data: Data types, specification limits, and subgroup options
  • Options: Capability settings, chart options, distribution fitting, and more
  • Math Details: The exact formulas behind every statistic