概率统计及SPC基础培训教材.pptx

概率统计及SPC基础;資料及數據;;;;離散型資料 (通常) 分組 / 分類 是 /否, 合格 / 不合格 不能計算 離散型資料 分級 很少用 很難加以計算 連續型資料 最常見的尺規 計算時要很小心 連續型資料 比例關係 可應用演算法的多數公式 ;;;完全同意 有點同意 既不同意也不反對 有點反對 完全反對 ;間距和比例衡量工具;基礎統計運用概念;變異(Variation) ;;變異(Variation) ;變異(Variation) ;變異(Variation) ;變異(Variation) ;控制變異(Variation) ;因為用抽樣統計,其結果只是估計, 和真實可能有差異. 適當的抽樣可使統計分析更準確.;;抽樣方法;一般準則;均值: 一組值的算術平均均值: - 反映所有值的影響 - 受極值影響嚴重 中位數: 反應 50% 的序一組數排序後居中的數 - 在計算中不必包含所有值 - 相對於極值具有 “可靠性” 眾數值: - 在一組資料中最常發??的值 ;全距: 在一組資料中，最高值和最低值 間的數值距離 變異 (s2): 每個資料點與均值的平均平方偏差 標準偏差 (s): 變異數的平方根. 量化變動最常用的量 ;The ? Rule states how ? and ? can be used to describe the entire distribution: ·??????? Roughly 60-75% of the data are within ?1? of ?. ·??????? Roughly 90-98% of the data are within ?2? of ?. ·??????? Roughly 99-100% of the data are within ?3? of ?.;The shape of a distribution can be described by skewness 歪斜 (denoted by ?1) and by kurtosis凹凸平坦 (denoted by ?2). ;母體均值;The most important and useful distribution shape is called the Normal distribution, which is symmetric(對稱), uni-modal(單峰), and free of outliers (沒有特異點): ;A Normal probability plot is a cumulative distribution plot where the vertical scale is changed in such a way that data from a Normal distribution will form a straight line: ;第一個屬性: 只要知道下面兩項就可以完全描述常態分配: 均值 標準差 ;常態曲線和其概率 ;常態概率圖 ;資料收集時的重點;Continuous Manufacturing (連續)occurs when an operation is performed on one unit of product at a time. An assembly line is typical of a continuous manufacturing environment, where each unit of product is worked on individually and a continuous stream of finished products roll off the line. The automotive industry is one example of Continuous Manufacturing. Other examples of continuously manufactured product are: ·??????? television sets, ·??????? fast food hamburgers, ·??????? computers. ;In Continuous Manufacturing the most important variation is between parts In Lot/Batch Manufacturing, the variation can occur between the parts in a lot and b