Use the EMP statistics to determine whether the measurement system is good enough to use for process improvement activities. The Classification Guidelines table gives the probabilities of warning for an X-bar chart to have an out-of-control point within ten subgroups for test 1 or for tests 1, 5, 6 and 8. First-class and second-class measurement systems are usually good enough to use for process improvement activities with an X-bar chart that uses test 1. Third-class measurement systems are usually good enough to use for process improvement activities with an X-bar chart that uses rules 1, 5, 6, and 8.

Statistic | Value | Classification |
---|---|---|

Test-Retest Error | 0.1999 | |

Degrees of Freedom | 78.0000 | |

Probable Error | 0.1349 | |

Intraclass Correlation (no bias) | 0.9645 | First Class |

Intraclass Correlation (with bias) | 0.9224 | First Class |

Bias Impact | 0.0421 |

Classification | Intraclass Correlation | Attenuation of Process Signals | Probability of Warning, Test 1* | Probability of Warning, Tests* |
---|---|---|---|---|

First Class | 0.80 - 1.00 | Less than 11% | 0.99 - 1.00 | 1.00 |

Second Class | 0.50 - 0.80 | 11 - 29% | 0.88 - 0.99 | 1.00 |

Third Class | 0.20 - 0.50 | 29 - 55% | 0.40 - 0.88 | 0.92 - 1.00 |

Fourth Class | 0.00 - 0.20 | More than 55% | 0.03 - 0.40 | 0.08 - 0.92 |

In these results, the classification guidelines show that the measurement system is first class. The measurements contain a hundredths place, but the probable error is over one tenth. The results recommend that the measurements go to the tenths place instead of the hundredths place.

Use the charts to identify opportunities to improve the measurement system.

- Repeatability chart by operator
- Shows whether any points fall above the upper control limit.
If the operators measure consistently, the points will fall within the control limits.

- Signal-to-noise chart by operator
- Shows whether most points fall beyond the control limits.
The parts that you choose for a gage study should represent the typical part-to-part variability. Thus, you should expect more variation between part averages, and the graph should show that most points fall beyond the control limits.

- Parallelism plot
- Shows whether the lines that connect the measurements from each operator are
similar or whether the lines cross each other.
Lines that are coincident indicate that the operators measure similarly. Lines that are not parallel or that cross indicate that an operator's ability to measure a part consistently depends on which part is being measured. A line that is consistently higher or lower than the others indicates that an operator adds bias to the measurement by consistently measuring high or low.

- Analysis of main effects (ANOME)
- Shows whether differences between operators are small compared to the
differences between parts.
Points within the decision limits indicate that the mean measurements for each operator are similar.

- Analysis of mean ranges (ANOMR)
- Shows whether operators measure consistently compared to each
other.
Points within the decision limits indicate that the mean ranges for each operator are similar.