## Basic Statistical Tools for Improving QualityThis book is an introductory book on improving the quality of a process or a system, primarily through the technique of statistical process control (SPC). There are numerous technical manuals available for SPC, but this book differs in two ways: (1) the basic tools of SPC are introduced in a no-nonsense, simple, non-math manner, and (2) the methods can be learned and practiced in an uncomplicated fashion using free software (eZ SPC 2.0), which is available to all readers online as a downloadable product. The book explains QC7 Tools, control charts, and statistical analysis including basic design of experiments. Theoretical explanations of the analytical methods are avoided; instead, results are interpreted through the use of the software. |

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### Contents

of Quality | |

Chapter | |

Exercises | |

Chapter | |

Process Improvement | |

Summarizing Data | |

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References Glossary | |

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### Common terms and phrases

alternative hypothesis Analysis tab ANOVA ANOVA table assignable cause average bellshaped box plot canbe capability index centerline Chapter computed confidence interval considered control limits correlation coefficient CUSUM control chart daily number distance measurements effect empty bags estimate experiment experimental design factor levels factorial design Figure frequency Gage R&R Goldilocks graph graphical histogram Ifthe input factors interaction inthe isthe median menu nonconforming nonparametric normal distribution normal probability plot null hypothesis number of defects observations ofthe following onthe outofcontrol Pareto Pareto chart Pareto principle predicted problem procedure process capability analysis process capability index process improvement process manager process mean process output pvalue quality characteristic radar chart regression represents residual sample mean scatter plot specification limits standard deviation statistical process control target value thedata theprocess tothe treatment means Type I error uncontrollable factors variability variation weight xMR