# Briefly about control charts

The control chart is a proven tool to continuously monitor the stability of a process that produces anything that is measurable. The purpose of the control chart is 1) to alarm as early as possible if the process starts to behave irregularly or unpredictably, and 2) to avoid false alarms that disrupt production. Prompted by a control chart alarm the process operator should immediately investigate the process, diagnose the root cause of the problem, and try to fix it. Then the operator resumes the process and continues with the control charting. The goal is to stepwise improve the process until it works reliably and predictably, and within specification. Diligently done, control charting saves money and boosts customer satisfaction.

A basic Shewhart type control chart for individual data points may look like this:

This I chart (I for individual data points) is a graph where the individual data points are plotted one by one as they are sampled from the monitored process. The chart has three horizontal lines; one center line (green) around which two control limits, the upper control limit and the lower control limit (red lines), are symmetrically spaced. In the case that a newly added data point falls outside of a control limit, the control chart sounds the alarm (the red data point in the above image) alerting the operator that the process doesn’t behave predictably anymore. The distance between the center line and the two control lines is calculated from the actual variation of the data points ensuring that the control chart is well balanced, which means that the control chart should be enough sensitive to detect unpredictable process behavior, and still not trigger too many false alarms.

A process that for some time has behaved predictably, as indicated by an absence of control chart alarms,
is said to run under **statistical control**.

## ControlFreak List of Features

Control Charts: | Individuals (I) Control Charts (Shewhart) for location of individual values Moving Range (MR) Control charts for scale (variation) of individual values J (Zone) Control Chart for location Cusum control chart for location Cusum control chart for scale Cusum control chart with overlaid I chart Self-starting Cusum control chart for location Self-starting Cusum control chart for scale EWMA control chart for location EWMA control chart for scale (M-EWMV chart) EWMA control chart with overlaid I chart The button to make a quick and dirty Shewhart control chart |

ARL calculations: | For I and MR control charts For Cusum control charts For EWMA control charts |

Versatile data point selection: | Control charted data points Data points used for statistical calculations |

Robustification of data: | By trimming By winsorization |

Box-Cox transform: | Transforming non-normal data |

Detecting of outliers: | According to Dixon According to Grubbs According to Rosner According to Iglewicz and Hoaglin |

Goodness-of-fit tests: | According to Kolmogorov-Smirnov According to Anderson-Darling According to Shapiro-Wilk According to Jarque-Bera According to Lilliefors-van Soest According to Cramér-von Mises According to Ryan-Joiner According to d'Agostino-Pearson |

Capability Indices: | 14 different capability indices Point estimates, confidence intervals, lower confidence bounds Two different stability indices |

Statistical plots: | Histogram Normal probability plot Scatterplot Box-and-Whisker plot "Lag 1" plot for correlation Autocorrelation plot Pearson distribution diagram Graphs illustrating the Box-Cox transform (if performed) |

Reports: | Distribution Statistical point estimates Process capability indices Non-conforming output |