品質統(tǒng)計經典培訓教程ppt課件

1、單元(4)-A統(tǒng)計基礎及品質統(tǒng)計資料數(shù)據(jù)基礎統(tǒng)計學生產製造環(huán)境品質統(tǒng)計圖表製程才干分析SPC統(tǒng)計製程控制資料及數(shù)據(jù)他想瞭解什麼?資訊源:分組離散型名義型順序型間距型“資料本身並不能提供資訊 必須對資料加以處理以後才干得到資訊, 而處理資料的工具就是統(tǒng)計學. 衡量連續(xù)型比率型 文字的 (A to Z) 圖示的 口頭的 數(shù)位的 (0-9)數(shù)據(jù)FAILPASS計時器 NO-GOGO數(shù)量 單價 說明 總價1$10.00$10.003$1.50$4.5010$10.00$10.002$5.00$10.00裝貨單 離散型資料和連續(xù)型資料電氣電路溫度溫度計連續(xù)型離散型卡尺錯誤$連續(xù)資料的優(yōu)勢 連續(xù)的離散的信

2、息量少信息量多離散型資料 (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(xù)型資料 最常見的尺規(guī) 計算時要很小心 連續(xù)型資料 比例關係 可應用演算法的多數(shù)公式 分類 標簽 第一、第二、第三 相對高度 字母順序 123 0g1 = 0g1 0g2 = 0g2 Basic Statistics Display Descriptive StatisticsGraphs Graphical SummaryA2 27.11描畫性統(tǒng)計圖形分析總結變數(shù)：奧秘中值的95%信賴區(qū)間 的95%信賴區(qū)間 Anderson-Darling常態(tài)測試P值 0.00均值

3、 100.00標準偏向 32.38變異數(shù) 1048.78偏度 0.01峰度 -1.63資料量 500.00最小值 41.77第一象限 68.69中值 104.20第三象限 130.81最大值 162.82的95%信賴區(qū)間97.5 102.85s的95%信賴區(qū)間30.49 34.53中值的95%信賴區(qū)間 82.78 117.66資料搜集時的重點How the data are collected affects the statistical appropriateness and analysis of a data set(資料如何搜集可影響統(tǒng)計的適切性). Conclusions from

4、 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

5、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.Continuous Manufacturing (連續(xù))occurs when an operation is performed

6、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 exam

7、ples of continuously manufactured product are: television sets, fast food hamburgers, computers.Lot/Batch Manufacturing (批次) occurs occurs when operations are performed on products in batches, groups, or lots. The final product comes off the line in lots, instead of a stream of individual parts. Pro

8、duct within the same lot are processed together, and receive the same treatment while in-process. 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.Manufactu

9、ring Environment製造環(huán)境In Continuous Manufacturing the most important variation is between partsIn 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 separat

10、ely.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 statisti

11、cal methods need to be used to take into account the several sources of variation in Lot/Batch Manufacturing.要留意: 連續(xù)和批量生產所用的統(tǒng)計方法有些不同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 res

12、ults 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.LotX12345*Distribution ofIndividual LotDistributi

13、on ofLot MeansOverall Distributionof Combined LotsVariation WithinEach LotVariation Between LotsTotal VariationThe 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. 1121 2

14、3 4 51 2 3 4 5LOTS班2121 2 3 4 51 2 3 4 5Each 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 s

15、et:生產線22 22222X12X2212121 , , ;X;X ; XXXX+=+=總 總 總 6原則 變異數(shù)可相加, 標準差則不能相加輸入變數(shù)變異數(shù)相加計算輸出中的總變異數(shù) 所以那麼引起的變異數(shù)輸入變數(shù)引起的變異數(shù)輸入變數(shù)過程輸出的變異數(shù)假設123456LotsWithin is smallsLot is largeprocess has small within-lot variation and large lot-to-lot variation (which is very common), data values from the same lot will be highl

16、y correlated, while data from different lots will be independent: 品質統(tǒng)計圖表直方圖(Histograms)方框圖(Boxplots)柏拉圖(Pareto Diagrams)散佈圖(Scatterplots)趨勢圖(Trend Charts)品質統(tǒng)計圖表 -直方圖(Histograms)Histograms provide a visual description of the distribution of a set of data. A histogram should be used in conjunction wit

17、h summary statistics such as and s.A histogram can be used to: Display the distribution of the data(現(xiàn)示數(shù)據(jù)的分佈). Provide a graphical indication of the center, spread, and shape of the data distribution (較定性地顯示數(shù)據(jù)的均值,散佈及形狀). Clarify any numerical summary statistics (which sometimes obscure information).

18、(顯示較模糊的統(tǒng)計結果). Look for outliers - data points that do not fit the distribution of the rest of the data. (顯示異常點) : : . . . : . . : : :.: : . : . : . .:.:.:.:.:.: : . +加侖/分鐘 49.00 49.50 50.00 50.50 51.00點圖分佈 設想有一個泵流量爲50加侖/分鐘的計量泵。按照節(jié)拍對泵的實際流量進行了100次獨立測量。畫出各個點，每點代表一個給定值的輸出“事件。當點聚集起來時，泵的實際性能狀況可以看作泵流量的“分佈。

19、 51.350.850.349.849.348.8403020100直方圖分佈 還是這些資料，現(xiàn)在設想將其分組後歸入“區(qū)間。泵流量點落入指定區(qū)間的次數(shù)決定區(qū)間條的高度。 頻率加侖/分鐘品質統(tǒng)計圖表 -直方圖(Histograms)150.7149.7154.5149.6155.3149.0160.5149.0155.3149.3149.2153.5145.5161.0151.5154.3150.9152.4150.5152.3144.5151.6151.1151.0147.5150.6147.4150.8148.3146.8148.7147.6153.0.0153.4146.5151.4143

20、.5149.4150.4153.1150.7149.1150.6149.6152.5145.2150.5146.4151.3151.7145.6147.1152.6147.0148.5155.0148.4151.3148.8146.7152.7155.3146.6144.8150.9149.5151.4147.3154.9151.2148.6142.5151.6151.0152.9146.9145.3150.8150.3153.6154.6150.6148.6155.1145.4148.5157.0148.9145.0147.7151.1149.7154.4149.1151.5153.3149

21、.5152.8150.8品質統(tǒng)計圖表 -直方圖(Histograms) Multi-Modal Shape(雙峰): Skewed Shape(偏一邊): Data can be right-skewed or left-skewed. This data is right-skewed the right tail is longer than the left tail. Outliers:特異點練習品質統(tǒng)計圖表 -方框圖(Boxplots)Boxplots are a graphical tool valuable for comparing the distributions of t

22、wo or more groups (e.g., different lots, shifts, operators, etc.). Each distribution on this chart consists of the following: A “box representing the middle 50% of the data values. The length of the “box is called the “Interquartile Range (IQR). Inside the “box is a line representing the median (50t

23、h percentile) of the data. Two “tails which extend out to the minimum and maximum data values (assuming there are no outliers in the data). If the distance between the a data point and the nearer quartile is greater than 1.5xIQR, the data point is labeled as an outlier, and the “tail on that side of

24、 the boxplot is shortened to the outermost data value within 1.5xIQR from the quartile.品質統(tǒng)計圖表 -方框圖(Boxplots)MedianMaximumData Value75thPercentile25thPercentileOutermostdata valueswithin 1.5xIQRof the 75th and25th Percentiles.OutlierNO OUTLIERSIQROUTLIERSMinimumData ValueOutlier1.5xIQR品質統(tǒng)計圖表 -方框圖(Box

25、plots)EXAMPLE : Creating a BoxplotThe figure below is a boxplot of the 100 plating thickness measurements. The histogram for the same data set is displayed for comparison.品質統(tǒng)計圖表 -方框圖(Boxplots)Lot 1Lot 2Lot 3Lot 4Lot 5Lot 6Lot 7149.18144.78146.77167.85144.51134.96152.41151.31147.18150.66164.17144.411

26、34.7146.76150.8145.66145.11168.23146.68.02148.19149.06147.09145.09162.88145.4134.63143.75151.73145.86145.98163.1143.3134.87153.71148.15144.64146.77166.91146.87.34145.13152.55143.67149.9165.78148.61134.6148.54Plating thickness measurements collected from 7 lots of product.品質統(tǒng)計圖表 -方框圖(Boxplots) Multi-

27、Modal Shape: Skewed Shape: Outliers: 練習品質統(tǒng)計圖表 -柏拉圖(Pareto Diagrams)While histograms are used to display the distribution of a set of continuous (measured) data, Pareto diagrams are used to display the distribution of discrete (counted) data, such as different types of defects.Pareto diagrams can als

28、o be used with continuous (measured) data, particularly in displaying variance components analysis results, as we will see later in this course.Pareto diagrams are a useful tool for determining which problems or types of problems are most severe or occur most frequently, hence should be given high p

29、riority for process improvement efforts. Pareto diagrams separate the significant vital few problems from the trivial many to help determine which problems to address first (and which to address later).重點中找重點!Pareto圖分析Pareto 圖根據(jù) frequency 欄的內容判斷各個缺陷影響的大小，並按從大到小的次序陳列。最後一組總是標有 “其他 ，並以默認方式包括一切缺陷的分類計算，這

30、幾類缺陷非常少， 它們占總缺陷的 5% 以下。該圖右側 Y 軸表示占總缺陷的百分比，左側 Y 軸表示缺陷數(shù)。紅線 (在螢幕上可以看到) 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) 。在圖的下方列出一切的值 百分比缺陷的Pareto圖 計數(shù) 缺陷 計數(shù) 274 59 43 19 10 18百分比 64.8 13.9 10.2 4.5 2.4 4.3累積百分比 64.8 78.7 88.9 93.4 93.4 100.0螺釘丟失 夾子喪失襯墊走漏 外殼有缺陷 零件不完好 其他 400300200100 0100806040200百分比%品質統(tǒng)計圖表 -柏拉圖(Pareto Di

31、agrams)Pareto圖分析: 創(chuàng)建一個加權的 Pareto圖 通過指定金額/缺陷或用其他的加權方法，可以給次數(shù)加權。列在C1中的缺陷發(fā)生次數(shù)的價格列在 C3 (value) 中， 價格乘以次數(shù)等於這類缺陷的費用 (c4) 。繪製費用cost曲線圖，而不是繪製次數(shù)count圖， 這樣可以更好地說明每個缺陷對業(yè)務的影響。 缺陷的Pareto圖 缺陷計數(shù) 2320.71 1653.00 1230.00 800.00 349.87 155.52 百分比 35.7 25.4 18.9 12.3 5.4 2.4累積百分比 35.7 61.0 79.9 92.2 97.6 100.0螺釘喪失螺釘丟失襯

32、墊走漏外殼有缺陷零件不完好其他600050004000300020001000 0100806040200計數(shù)百分比%品質統(tǒng)計圖表 -柏拉圖(Pareto Diagrams)層別Pareto圖: 解釋分組資料 上圖運用了一個 By Variable從屬變數(shù)，一切的圖都在一頁上。 下圖運用同樣的命令，沒有從屬變數(shù)。 當選擇每頁一張圖時，一切的圖的計數(shù)(左軸)刻度一樣。 右側的百分比只反映該圖占總體的百分比。這些圖闡明， 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)。此外，記錄缺陷是刮傷和剝落的比例，對白班和夜班的 來說似乎也差不多。然而，晚班和周末班出現(xiàn)的

33、缺陷樣式是不同的。 裂紋Pareto圖 白班 晚班 夜班 周末班 刮傷剝落其他污點 151050151050151050151050裂紋Pareto圖403020100100806040200缺陷計數(shù) 15 13 6 6百分比 37.5 32.5 15.0 15.0 累積百分比 35.5 70.0 85.0 100.0刮傷撥落其他污點計數(shù) 計數(shù)計數(shù)計數(shù)計數(shù)百分比%品質統(tǒng)計圖表 -柏拉圖(Pareto Diagrams)練習品質統(tǒng)計圖表 -散佈圖(Scatterplots)Until now, all the graphical tools weve discussed have been fo

34、r examining the distribution of a single process characteristic. The scatterplot is a graphical tool for examining the relationship between two process characteristics. A scatterplot is an X-Y plot of one variable versus another.Each unit of product usually has many characteristics, process input va

35、riables, etc. One objective might be to see whether two variables or characteristics are related to each other (i.e., to see what happens to one of the variables when the other variable changes). This relationship between two variables is called correlation. Scatterplots can help us answer this type

36、 of question.品質統(tǒng)計圖表 -散佈圖(Scatterplots)Acid AgeEtch RateAcid AgeEtch RateAcid AgeEtch Rate4.0134.5134.0154.5181.5302.5233.0183.5191.0313.5195.575.044.0122.0253.5212.0241.0292.0261.0283.0205.593.0195.064.5145.095.592.5272.5251.5301.531品質統(tǒng)計圖表 -散佈圖(Scatterplots)In addition to telling us whether or not t

37、wo variables are related, scatterplots can tell us how they are related, and the strength of the relationship:Strong Positive Correlation強正相關No Correlation無關Weak Negative Correlation弱負相關Weak Positive Correlation弱正相關Strong Negative Correlation強負相關品質統(tǒng)計圖表 -散佈圖(Scatterplots)In addition, scatterplots are

38、 an excellent tool for determining the type of relationship between the two variables, as well as looking for outliers:Linear Relationship線性相關Outliers 特異Non-Linear Relationship非線性相關品質統(tǒng)計圖表 -散佈圖(Scatterplots)Correlation and CausationWe must always take care not to confuse correlation with causation. T

39、he fact that two characteristics are correlated does not prove that one causes the other. Both may be related to some other factor which is the true root cause.Number of TelevisionsNumber ofTrafficAccidents19701990But is there a cause-effect relationship between the two? Did the increase in TVs caus

40、e the number of accidents to go up? (Not likely.) Did the increase in traffic accidents cause people to buy more TVs? (Not likely, either.)練習品質統(tǒng)計圖表 -趨勢圖(Trend Charts)Trend ChartsStability: A process is stable if its mean and standard deviation are constant and predictable over time.A disadvantage of

41、 histograms and normal probability plots is that they cannot be used to determine whether the process is stable over time. A plot of the data in time order will allow us to do that.These time-ordered plots, called Trend charts and Control charts are essential when examining the stability of a distri

42、bution over time. A trend chart or a control chart can detect instability if it exists.Control charts, which are a special kind of trend chart, are discussed in detail separately in a later course module.可看出穩(wěn)定性及預測性品質統(tǒng)計圖表 -趨勢圖(Trend Charts)The table below contains average plating thickness measuremen

43、ts taken from 21 lots of product. Below that is a trend chart of the data.Lot #Plating ThicknessLot #Plating ThicknessLot #Plating Thickness1151.98143.815149.22147.49152.716147.53155.810147.417151.94151.711152.718141.95149.212143.819152.76153.813.120147.47159.914142.521157.3練習品質統(tǒng)計圖表 - NoisyThe resul

44、ts of a statistical analysis can be seriously affected by the failure of the data to meet certain required assumptions. One of the most common assumptions is that the data values are independent and that they come from a Normal distribution. This assumption can be violated in several ways: Outliers

45、(points that do not fit the rest of the distribution) in the data, Non-Normal-shaped distributions (multi-modal or skewed distributions),Data that exhibit these characteristics can be thought of as noisy data. The procedures in this section provide techniques for effective detection and analysis of

46、noisy data.雜訊品質統(tǒng)計圖表 - NoisyBoxplotsTrend ChartHistogramScatterplotNormal Prob. Plot品質統(tǒng)計圖表 - NoisyRecommended strategy for handling outliers:1. Identify the outliers using the methods described in the following pages. If possible, find the causes of the outliers. Remove the outliers with identified c

47、auses from the data set(找緣由).2. If all the outliers can be explained, then analyze the data as usual.3. However, if there are any outliers that do not have explanations, analyze the data twice: including the outliers, excluding the outliers.See if and how the analysis results differ.製程才干分析當製程開始產生變異時

48、,其統(tǒng)計分佈圖的形狀也開始變化。通常變化不外下面三種根本狀況的組合:整體製程數(shù)據(jù)漂移散佈變寬中心值漂移假設將每日之統(tǒng)計分佈串起來一同看,則又可看到更多變異現(xiàn)象,普通可分為兩種如下: 1.突發(fā)變異:製程中有特殊或突發(fā)緣由而產生變異,呵斥不穩(wěn)定。例:每日生產參數(shù)設定漂移。2.共同變異:製程中只需共同緣由的變異此種現(xiàn)象是穩(wěn)定的不良。例:模具尺寸超差。瞭解以上根本觀念後便開始參與控制的觀念。作控制時參與規(guī)格上下線, 超出規(guī)格則視為不良如下圖:製程才干好,中心值在目標上且分佈均在規(guī)格內製程才干尚可,中心值在目標上,分佈均在規(guī)格內但略微太分散製程才干尚可,中心值有漂移,但分佈尚在規(guī)格內製程才干不好,中心值

49、雖在目標,但分佈超出規(guī)格外製程才干不好,中心值不在目標,分佈雖集中但超出規(guī)格外製程才干最差,中心值不在目標,分佈不集中且超出規(guī)格外計算Ca,Cp,Cpk公式規(guī)格中心mLSL+ 3 - 3 製程寬度6 規(guī)格寬度TUSLSuSLCa: Capability of Accuracy準確度:實際中心Ca-=Xm(T/2)-XmXCa只對雙邊規(guī)格適用.分級標準如下:等級 Ca 值A Ca 12.25%B 12.25% Ca 25%C25%50%計算Ca,Cp,Cpk公式規(guī)格中心mLSL+ 3 - 3 製程寬度6 規(guī)格寬度TUSLSuSLCp: Capability of Precision精確度:實際中

50、心-XmX當僅有下限時:Cp = ( -SL)/(3)對雙邊規(guī)格: Cp = T/(6)當僅有上限時: Cp = (Su- )/(3)XX 等級Cp值ACp1.33B 1.00 Cp1.33C0.67Cp1.00DCp0.67分級標準如下: 計算Ca,Cp,Cpk公式Cpk: 指制程才干參數(shù), 是Cp和Ca的綜合.對雙邊規(guī)格: Cpk=(1-Ca)*Cp= Min(Su- )/(3), ( -SL)/(3) 對單邊規(guī)格, 可以認為T為, 則 Ca= ( -)/ (T/2)= 0 Cpk= (1-Ca)*Cp= Cp等級Cpk值評價ACpk1.33理想B1.00Cpk1.33正常CCpk1.0缺

51、乏 分級標準如下:XXX練習SPC統(tǒng)計製程控制SPC介紹SPC是用於研討變動的一種根本工具,它運用統(tǒng)計信號監(jiān)測並改善過程績效。該工具可用於任何領域：製造業(yè)、商業(yè)，銷售業(yè)等等SPC是統(tǒng)計程式控制 Statistical Process Control的縮寫。大多數(shù)公司是將 SPC用於最終産品 (Y)上, 而不是用於過程特徵 (X)。第一步是運用統(tǒng)計方法控制公司的輸出。然而，只需我們將重點放在控制輸入 (X),而不是控制輸出 (Y)時, 我們才干認識到我們在提高質量、生産率及降低本錢上的努力收效有多大。什麼是統(tǒng)計製程控制SPC一切過程都有固有變動由於普通緣由和非固有變動由於特殊緣由， 我們運用SP

52、C來監(jiān)測並改善過程。 SPC的運用使我們能夠通過失控信號發(fā)現(xiàn)特殊緣由。這些失控信號無法說明過程失控的緣由，只能闡明過程處於失控狀態(tài)?？刂茍D表是在統(tǒng)計上從時間上跟蹤過程和産品參數(shù)的方法。控制圖表中包括反映過程隨機變動固有限值的上下控制限值。 這些限值不應與 顧客規(guī)定限值相比較 。什麼是統(tǒng)計製程控制續(xù)根本統(tǒng)計原理，控制圖表能夠用於識別過程變數(shù)中的非固有非隨機型式。當控制圖表出現(xiàn)非隨機型式信號時，我們就可以知道特殊緣由引起的變動改變了過程。我們採用措施修正控制圖表中非隨機型式，這是勝利運用 SPC的關鍵?？刂葡拗凳且誀懞饬康腨或X建立 3限值爲基礎。沒有正確訓練X或Y的SPC=牆紙警示信號用於發(fā)現(xiàn)缺

53、陷。一旦生産成爲1#優(yōu)先度，操作者將學會忽略或切警示信號!實施S.O.P以發(fā)現(xiàn)缺陷。這種措施不能短期或長期堅持。用經過充分訓練的操作者對X或Y進行統(tǒng)計程式控制SPC。操作者已受過訓練並瞭解SPC的規(guī)定，但管理層不準許他們停下來或進行研討。第3種類型修正措施=檢查：實施短期遏制政策的措施，這種措施有能夠發(fā)現(xiàn)由錯誤條件引起的缺陷。常用的遏制政策是審查或100%檢查。對遵守規(guī)定的操作者和職員進行充分訓練，用他們對X或Y進行統(tǒng)計程式控制SPC 。一旦圖表顯示出現(xiàn)問題，每個人瞭解SPC規(guī)定，並由於識別和消除特殊緣由而贊同停頓。第2種類型修正措施=標記：對那些錯誤條件已經出現(xiàn)的過程進行改善。該標記使設備停

54、工，以免缺陷繼續(xù)發(fā)展。第1種類型修正措施=防範措施：改善過程，消除錯誤條件發(fā)生的情況，缺陷永遠也不會發(fā)生。在防錯或設計變更方式上，這也可作爲長期的修正措施?？刂品椒ㄗ畈钭顑?yōu)過程改善及控制圖過程衡量系統(tǒng)輸入輸出1. 發(fā)現(xiàn)可指定的緣由4. 驗證結果3.實施修正措施2. 確定根本緣由控制圖的益處用於提高生産率的已證實的技術有效防範缺陷防止不用要的過程調整提供診斷資訊提供關於過程才干的資訊控制圖類型控制圖有許多類型，但是它們的根本原理是一樣的利用 SPC和過程目標方面的知識選擇正確的類型根據(jù)以下幾方面選擇控制圖類型：資料類型: 屬性還是變數(shù)?採樣容易：樣本同質性資料分佈: 正?；蚍钦?分組大小: 不

55、變的或變化的?其他考慮控制圖的組成KVOP的X均值圖20100615605595585樣本數(shù)X=599.1UCL=613.6LCL=584.6控制下限UCL = m +ks中線 = mLCL = m - k s其中m = 樣本均值s = 樣本標準偏向k = 控制限制距中線的差值 (通常爲 3)記住:控制限值與顧客規(guī)定限值無關控制上限中線 樣本均值常用控制圖類型(X-S)常用控制圖類型(X-R)短期N 30For control charts with N 30 lots, rather than the usual UCL (upper control limit) and LCL (lowe

56、r control limit), there are dual sets of control limits: Outer Control Limits(3s). Inner Control Limits (1s).短期N 30Any point outside either of the outer control limits indicates an unstable process. All points falling between both inner control limits indicates a stable process. If any points fall inside either “uncertainty zone (but none are outside the outer control limits), we cannot say whether or not the process is stable, because we do not ye