統(tǒng)計基礎(chǔ)及質(zhì)量統(tǒng)計-全文預(yù)覽

　　

【正文】 from a Normal distribution will form a straight line: Histogram Cumulative Distribution Normal Probability Plot 常態(tài)概率圖 Normal Distribution常態(tài)分佈 6s 28 第一個屬性 : 只要知道下面兩項就可以完全描述常態(tài)分配 : 均值 標(biāo)準(zhǔn)差 常態(tài)分配 的好處 簡化 第一個分佈 第二個分佈 第三個分佈 這三個分佈有什麼不同 ? 6s 29 常態(tài)曲線和其概率 4 3 2 1 0 1 2 3 4 40% 30% 20% 10% 0% % 第二個屬性 : 曲線下方的面積可以用於估計某“事件”發(fā)生的累積概率 95% 68% 樣本值的概率 距離均值的標(biāo)準(zhǔn)偏差數(shù) 得到兩值之間的值的累積概率 6s 30 練習(xí) 6s 31 常態(tài)概率圖 1 3 0 1 2 0 1 1 0 1 0 0 9 0 8 0 7 0 6 0 3 0 0 2 0 0 1 0 0 0 C 2 常態(tài)概率圖 頻率 1 1 0 1 0 0 9 0 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 0 5 0 0 C 1 常態(tài)概率圖 頻率 8 0 7 0 6 0 5 0 4 0 3 0 2 0 1 0 0 3 0 0 2 0 0 1 0 0 0 C 3 常態(tài)概率圖 頻率 1 3 0 1 2 0 1 1 0 1 0 0 9 0 8 0 7 0 6 0 . 9 9 9 . 9 9 . 9 5 . 8 0 . 5 0 . 2 0 . 0 5 . 0 1 . 0 0 1 平均： 70 標(biāo)準(zhǔn)偏差： 10 資料個數(shù)： 500 AndersonDarling常態(tài)測試 A平方 : P值 : 正偏斜分佈 概率 正偏斜 1 0 6 9 6 8 6 7 6 6 6 5 6 4 6 3 6 2 6 . 9 9 9 . 9 9 . 9 5 . 8 0 . 5 0 . 2 0 . 0 5 . 0 1 . 0 0 1 常態(tài)分配 常態(tài) 概率 平均值： 70 標(biāo)準(zhǔn)偏差 :10 資料個數(shù)： 500 AndersonDarling常態(tài)測試 A平方 : P值 : 我們可以用常態(tài)概率圖檢驗一組給定的資料是否可以描述爲(wèi)“常態(tài)” 如果一個分佈接近常態(tài)分配，則常態(tài)概率圖將爲(wèi)一條直線。的 95%信賴區(qū)間 s的 95%信賴區(qū)間 中值的 95%信賴區(qū)間 6s 34 資料收集時的重點 How the data are collected affects the statistical appropriateness and analysis of a data set(資料如何收集可影響統(tǒng)計的適切性 ). Conclusions from properly collected data can be applied more generally to the process and output. Inappropriately collected data CANNOT be used to draw valid conclusions about a process. Some aspects of proper data collection that must be accounted for are: The manufacturing environment(製程環(huán)境 )from which the data are collected. When products are manufactured in batches or lots, the data must be collected from several batches or lots. Randomization(隨機 ). When the data collection is not randomized, statistical analysis may lead to faulty conclusions. 6s 35 Continuous Manufacturing (連續(xù) )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, ? puters. Lot/Batch Manufacturing (批次 ) occurs occurs when operations are performed on products in batches, groups, or lots. The final product es off the line in lots, instead of a stream of individual parts. Product within the same lot are processed together, and receive the same treatment while inprocess. Lot/Batch Manufacturing is typical of the semiconductor industry and many of its suppliers. Other examples of lot/batch manufactured product include: ? chemicals, ? semiconductor packages, ? cookies. Manufacturing Environment製造環(huán)境 6s 36 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 between the lots: ? Product within the same lot is manufactured together. ? Product from different lots are manufactured separately. Because of this, each lot has a different distribution. This is important because Continuous Manufacturing is a basic assumption for many of the standard statistical methods found in most textbooks or QC handbooks. These methods are not appropriate for Lot/Batch Manufacturing. Different statistical methods need to be used to take into account the several sources of variation in Lot/Batch Manufacturing. 要注意 : 連續(xù)和批量生產(chǎn)所用的統(tǒng)計方法有些不同 6s 37 With Lot/Batch Manufacturing, each lot has a different mean. Due to random processing fluctuations, these lots will vary even though the process may be stable. This results in several “l(fā)evels” of distributions, each level with its own variance and mean: ? A distribution of units of product within the same lot. ? A distribution of the means of different lots. ? The total distribution of all units of product across all lots. Lot X 1 2 3 4 5 * * * * * * * * * * Distribution of Individual Lot Distribution of Lot Means Overall Distribution of Combined Lots Variation Within Each Lot Variation Between Lots Total Variation 6s 38 The different variances of a Lot/Batch Manufacturing process form a hierarchy called nesting. Data collected from such processes usually have what is called a nested data structure. 1 1 2 1 2 3 4 5 1 2 3 4 5 LOTS 班 2 1 2 1 2 3 4 5 1 2 3 4 5 Each of the levels in the nested structure corresponds to a single variance. With a nested data set from this process, we need to take each source of variation into account when collecting data to ensure the total process variation is represented in our data set: 222Lot2Total 線班 sss ???生產(chǎn)線 6s 39 2 2 2 2 2 2 2 X 1 2 X 2 2 1 2 1 2 1 , , 。 按照節(jié)拍對泵的實際流量進行了 100次獨立測量 。泵流量點落入指定區(qū)間的次數(shù)決定區(qū)間條的高度。 紅線 (在螢?zāi)簧峡梢钥吹?) 表示累積百分比，而直方圖表示每類缺陷的頻率 (占總量的百分比 ) 。 缺陷的 Pareto圖 缺陷 計數(shù) 百分比 累積百分比％ 6000 5000 4000 3000 2023 1000 0 100 80 60 40 20 0 品質(zhì)統(tǒng)計圖表 柏拉圖 (Pareto Diagrams) 6s 57 層別 Pareto圖 : 解釋分組資料 上圖使用了一個 By Variable（ 從屬變數(shù)），所有的圖都在一頁上。 這些圖表明 ， 70%的記錄缺陷是刮傷和剝落的 (下部 )， 約有一半的缺陷是夜班人員記錄的 (上右圖 )。ve discu