Minitab操作使用介紹

1、Minitab操作使用介紹Content 操作介面 & 主要功能介紹 基本繪圖 製程能力指標 管制圖 假設檢定操作介面操作介面 & & 主要功能介紹主要功能介紹 起始畫面起始畫面操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-File操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Edit操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功

2、能表介紹-Data - Transpose操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Transpose操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Transpose操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Transpose操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Stack操作介面操作介面 & 主要功能介紹主要功能介紹

3、 continue 功能表介紹功能表介紹-Data - Stack操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Stack操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Data - Stack操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Calc操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功能表介紹-Stat操作介面操作介面 & 主要功能介紹主要功能介紹 continue 功能表介紹功

4、能表介紹-Graph基本繪圖基本繪圖 直方圖 柏拉圖 散佈圖 魚骨圖基本繪圖基本繪圖-直方圖直方圖銲錫厚度的好壞影響PCBA板的品質, 為了解資料特性, 故收集資料, 繪製直方圖, 判斷資料是否分布異常.基本繪圖基本繪圖-直方圖直方圖 continue基本繪圖基本繪圖-直方圖直方圖 continue Step2 : 選擇功能選擇功能(Graph Histogram)基本繪圖基本繪圖-直方圖直方圖 continue Step3 : 選擇圖形格式選擇圖形格式 基本繪圖基本繪圖-直方圖直方圖 continue Step4 : 於於”Graph variable”空白欄中點選左方欲繪製的資料空白欄中點

5、選左方欲繪製的資料行行基本繪圖基本繪圖-直方圖直方圖 continue Step5 : 按下按下ok，顯現圖形，顯現圖形基本繪圖基本繪圖-直方圖直方圖 continue 實例練習You work for a shampoo manufacturer and need to ensure that the caps on your bottles are being fastened properly. If fastened too loosely, they may fall off during shipping. If fastened too tightly, they may be

6、hard for your customers to open (especially in the shower).You collect a random sample of bottles and test the amount of torque required to remove the caps. Create a histogram to evaluate the data and determine how close the samples are to the target value of 18.打開CAP.MTW 基本繪圖基本繪圖-直方圖直方圖 continue 實例

7、結果TorqueFrequency3632282420161214121086420Histogram of Torque基本繪圖基本繪圖柏拉圖柏拉圖 continue為了解本週MNT Process最常出現的不良現象, 故收集本週Process不良現象出現的資料如下 基本繪圖基本繪圖柏拉圖柏拉圖 continue Step1：將資料分”不良原因”及”不良數目”輸入資料視窗中.基本繪圖基本繪圖柏拉圖柏拉圖 continue Step2 : 選擇功能 ( StatQuality Tools Pareto Chart)基本繪圖基本繪圖柏拉圖柏拉圖 continue Step3：選擇Chart de

8、fects table。將不良原因點選至”Label in”欄，不良數目點選至”Frequencies in”欄。基本繪圖基本繪圖柏拉圖柏拉圖 continue Step4：按下ok，顯現結果基本繪圖基本繪圖柏拉圖柏拉圖 continue實例練習PCBA終檢站主要不良現象為金手指不良, 為確認金手指最常出現的不良現象, 故收集9月份的資料, 繪製柏拉圖, 找出主要Issue, 並加以改善. 收集資料如下,基本繪圖基本繪圖柏拉圖柏拉圖 continue 實例結果CountPercentCount19.719.013.210.910.6Cum %26.546.265.278.4565489.410

9、0.042154061282523342275Percent26.5g奎溃勃购端W IfSu20000150001000050000100806040200Pareto Chart of 基本繪圖基本繪圖散佈圖散佈圖PCBA金手指主要不良現象之一為沾錫, 利用Brain Storm判斷附著力大小為造成沾錫的影響原因, 故收集各家廠商附著力與對應的沾錫比率資料, 繪製散佈圖以確認影響關係. 收集資料如下 基本繪圖基本繪圖散佈圖散佈圖 continue Step1：將欲分析的變數資料key in 至資料視窗中基本繪圖基本繪圖散佈圖散佈圖 continue Step 2 ：選擇功能選擇功能 ( Gr

10、aph Scatterplot)基本繪圖基本繪圖散佈圖散佈圖 continue Step3:選擇圖形格式選擇圖形格式 基本繪圖基本繪圖散佈圖散佈圖 continue Step4 : 於於”Graph variable”空白欄中點選左方欲繪製的資空白欄中點選左方欲繪製的資料行料行基本繪圖基本繪圖散佈圖散佈圖 continue Step5 : 按下按下ok，顯現圖形，顯現圖形基本繪圖基本繪圖散佈圖散佈圖 continueStep1：將欲分析的變數資料key in 至資料視窗中基本繪圖基本繪圖散佈圖散佈圖 continue Step 2 ：選擇功能選擇功能 ( Graph Regression Fi

11、tted Line Plot)基本繪圖基本繪圖散佈圖散佈圖 continue Step 3：分別將結果與影響因點選至：分別將結果與影響因點選至”Response”, “Predictor”.基本繪圖基本繪圖散佈圖散佈圖 continue Step5 : 按下按下ok，顯現圖形，顯現圖形帝 Og奎 计1000900800700600403020100S11.0601R-Sq35.6%R-Sq(adj)24.9%Fitted Line Plotg奎计 = 62.02 - 0.06824 帝O基本繪圖基本繪圖散佈圖散佈圖 continue 實例練習You are interested in how

12、well your companys camera batteries are meeting customers needs. Market research shows that customers become annoyed if they have to wait longer than 5.25 seconds between flashes.You collect a sample of batteries that have been in use for varying amounts of time and measure the voltage remaining in

13、each battery immediately after a flash (VoltsAfter), as well as the length of time required for the battery to be able to flash again (flash recovery time, FlashRecov). Create a scatterplot to examine the results. Include a reference line at the critical flash recovery time of 5.25 seconds.讀取BATTERI

14、ES.MTW 基本繪圖基本繪圖散佈圖散佈圖 continue 實例結果VoltsAfterFlashRecov1.51.41.31.21.11.00.97.57.06.56.05.55.04.54.03.5Scatterplot of FlashRecov vs VoltsAfter基本繪圖基本繪圖魚骨圖魚骨圖MNT檢驗時常發生手感不良現象, 故針對此現象進行腦力激盪, 列出所有造成手感不良的原因, 並繪製成魚骨圖.基本繪圖基本繪圖魚骨圖魚骨圖 continue Step1 :將腦力激盪後結論的不良原因，以5M 1E分類，並分行”輸入至Minitab資料視窗中. 基本繪圖基本繪圖魚骨圖魚骨圖

15、continue Step2 :選擇功能 ( Stat Quality Tools Cause-and-Effect)基本繪圖基本繪圖魚骨圖魚骨圖 continue Step3: 於Causes一欄點選相對應Label(5M1E)之資料行，並於”Effect”欄中填入探討的主題項目.基本繪圖基本繪圖魚骨圖魚骨圖 continue Step4:基本繪圖基本繪圖魚骨圖魚骨圖 continue 實例練習Using a Pareto chart, you discovered that your parts were rejected most often due to surface flaws.

16、This afternoon, you are meeting with members of various departments to brainstorm potential causes for these flaws. Beforehand, you decide to print a cause-and-effect (fishbone) diagram to help organize your notes during the meeting. The example below illustrates how to generate a complete cause-and

17、-effect diagram. 打開SURFACEFLAWS.MTW. 基本繪圖基本繪圖魚骨圖魚骨圖 continue 實例結果SURFACEFLAWSMicrometersTrainingEnvironmentMeasurementsMethodsMaterialMachinesPersonnelOperatorsTrainingSupervisorsShiftsSpeedLathesBitsSocketsSuppliersLubricantsAlloysBrakeEngagerAngleInspectorsMicroscopesMicrometersCondensationMoistur

18、e%ErraticToo slowConditionAccuracyCause-and-Effect Diagram製程能力指標 以錫膏厚度為例以錫膏厚度為例製程能力指標 continue Step 1 : 輸入資料製程能力指標 continue Step2 : 選擇功能(Stat Quality Tool Capability analysis Normal)製程能力指標 continue Step3 : 將資料點選至“Single column”, 填入subgroup size. 並將上下規格分別填入Lower spec & Upper spec製程能力指標 continue S

19、tep4 :點選”Estimates”功能, 選擇變異數的估計值製程能力指標 continue Step5 :按下ok, 顯現結果製程能力指標 continue實例練習Suppose you work at an automobile manufacturer in a department that assembles engines. One of the parts, a camshaft, must be 600 mm + 2 mm long to meet engineering specifications. There has been a chronic problem wit

20、h camshaft lengths being out of specification - a problem that has caused poor-fitting assemblies down the production line and high scrap and rework rates.Upon examination of the inventory records, you discovered that there were two suppliers for the camshafts. An Xbar and R chart showed you that Su

21、pplier 2s camshaft production was out of control, so you decided to stop accepting production runs from them until they get their production under control.After dropping Supplier 2, the number of poor quality assemblies has dropped significantly, but the problems have not completely disappeared. You

22、 decide to run a capability study to see whether Supplier 1 alone is capable of meeting your engineering specifications.打開CAMSHAFT.MTW 製程能力指標 continue 實例結果601.50600.75600.00599.25598.50597.75LSLUSLProcess DataSample?N100StDev(Within)0.584695StDev(Overall)0.620865LSL598Target*USL602Sample Mean599.548

23、Potential (Within) CapabilityCCpk1.14Overall CapabilityPp1.07PPL0.83PPU1.32PpkCp0.83Cpm*1.14CPL0.88CPU1.40Cpk0.88Observed PerformancePPM?USL0.00PPM?Total10000.00Exp. Within PerformancePPM?USL13.73PPM?Total4067.77Exp. Overall PerformancePPM?USL39.19PPM?Total6367.35WithinOverallProcess Capability of S

24、upp1管管 制制 圖圖 Xbar-R管制圖 Xbar-S管制圖 P管制圖管管 制制 圖圖Xbar-R管制圖管制圖 Monitor中白平衡值為衡量品質的重要指標之一, 為即時監控白平衡值是否正常, 故收集以下資料, 繪製管制圖.管管 制制 圖圖Xbar-R管制圖管制圖 continue Step1 : 輸入資料管管 制制 圖圖Xbar-R管制圖管制圖 continue Step2 : 選擇功能(Stat Control charts Variables Charts for Subgroups Xbar-R)管管 制制 圖圖Xbar-R管制圖管制圖 continue Step3 : 將資料點選

25、至“All observation for a chart are in one column, 填入subgroup size.管管 制制 圖圖Xbar-R管制圖管制圖 continue Step4 : 點選“Xbar-R Option”, 選擇變異數估計值管管 制制 圖圖Xbar-R管制圖管制圖 continue Step5 : 點選ok, 顯現結果管管 制制 圖圖Xbar-R管制圖管制圖 continue實例練習You work at an automobile engine assembly plant. One of the parts, a camshaft, must be 60

26、0 mm +2 mm long to meet engineering specifications. There has been a chronic problem with camshaft length being out of specification, which causes poor-fitting assemblies, resulting in high scrap and rework rates. Your supervisor wants to run X and R charts to monitor this characteristic, so for a m

27、onth, you collect a total of 100 observations (20 samples of 5 camshafts each) from all the camshafts used at the plant, and 100 observations from each of your suppliers. First you will look at camshafts produced by Supplier 2.打開CAMSHAFT.MTW 管管 制制 圖圖Xbar-R管制圖管制圖 continue 實例結果SampleSample Mean1917151

28、31197531602600598_X=600.23UCL=602.376LCL=598.084SampleSample Range19171513119753186420_R=3.72UCL=7.866LCL=011Xbar-R Chart of Supp2管管 制制 圖圖Xbar-S管制圖管制圖 PCBA錫膏印刷工站中, 錫膏厚度為判斷印刷品質的重要參數, 為即時監控印刷品質, 故利用管制圖進行監控管管 制制 圖圖Xbar-S管制圖管制圖 continue Step1 : 輸入資料管管 制制 圖圖Xbar-S管制圖管制圖 continue Step2 : 選擇功能(Stat Control

29、 charts Variables Charts for Subgroups Xbar-S)管管 制制 圖圖Xbar-S管制圖管制圖 continue Step3 : 將資料點選至“All observation for a chart are in one column, 填入subgroup size.管管 制制 圖圖Xbar-S管制圖管制圖 continue Step4 :點選“Xbar-S Option”,選擇變異數估計值管管 制制 圖圖Xbar-S管制圖管制圖 continue Step5 : 點選ok, 顯現結果管管 制制 圖圖Xbar-S管制圖管制圖 continue 實例練習Y

30、ou work at an automobile engine assembly plant. One of the parts, a camshaft, must be 600 mm +2 mm long to meet engineering specifications. There has been a chronic problem with camshaft length being out of specification, which causes poor-fitting assemblies, resulting in high scrap and rework rates

31、. Your supervisor wants to run X and R charts to monitor this characteristic, so for a month, you collect a total of 100 observations (20 samples of 5 camshafts each) from all the camshafts used at the plant, and 100 observations from each of your suppliers. First you will look at camshafts produced

32、 by Supplier 2.打開CAMSHAFT.MTW 管管 制制 圖圖Xbar-S管制圖管制圖 continue 實例結果SampleSample Mean191715131197531602600598_X=600.23UCL=602.424LCL=598.036SampleSample StDev1917151311975313210_S=1.537UCL=3.211LCL=011Xbar-S Chart of Supp2管管 制制 圖圖P管制圖管制圖 PCBA錫膏印刷時, 常會發生銲點不良現象, 故設定焊點目檢工站, 計算焊點不良造成PCBA不良的比率, 並以P管制圖進行監控. 收

33、集不良資料如下,管管 制制 圖圖P管制圖管制圖 Step1 : 輸入不良數管管 制制 圖圖P管制圖管制圖 continue Step2 : 選擇功能(Stat Control charts Attributes charts P chart)管管 制制 圖圖P管制圖管制圖 continue Step3 : 將資料點選至“Variables”, 輸入subgroup size管管 制制 圖圖P管制圖管制圖 continue Step4 : 點選ok, 顯示結果管管 制制 圖圖P管制圖管制圖 continue 實例練習龍華IQC16月資料管管 制制 圖圖P管制圖管制圖 continue 不良P管制

34、圖SampleProportion6543210.140.120.100.080.060.040.020.00_P=0.0540UCL=0.0872LCL=0.0209Tests performed with unequal sample sizes假設檢定1.平均數假設檢定平均數假設檢定 -Z檢定 : 當樣本數 30 -t檢定 : 當樣本數 30 -ANOVA 2.變異數假設檢定變異數假設檢定 -卡方檢定 -F檢定假設檢定 continue E.g. H0 : A=3, E.g. H0 : A=B E.g. H0 : A=B=C=F假設檢定 continue假設檢定 continue 產品的

35、Test key: R35 (ITO/SD contact Resistance) 受設計圖案不同，而在有 不同 的R35 電性表現。原本AGZA 產品為原本之R35 設計，但R35值之變動相當 大。為了改善R35值，在同尺寸的產品作了更改設計圖案，為確定改善後的產 品(AGZC)是否在R35電性值是否有改善, 故進行假設檢定。 比較變異數, 兩母體資料. two sample variance.假設檢定 continue Step1 : 輸入資料假設檢定 continue Step2 : 選擇功能(Stat Basic Statistics2 Variance)假設檢定 continue S

36、tep3: 點選”Sample in different columns”, 並將資料分別選入First與Second欄內假設檢定 continueStep4 : 按下ok, 顯現結果檢定結果 N Lower StDev UpperAGZA 30 4332.98 5613.51 7895.70AGZC 30 246.21 318.97 448.65F-Test (normal distribution)Test statistic = 309.72, p-value = 0.00095% Bonferroni Confidence Intervals for StDevsAGZCAGZA800

37、06000400020000DataAGZCAGZA300002500020000150001000050000F-Test0.000Test Statistic309.72P-Value0.000Levenes TestTest Statistic24.99P-ValueTest for Equal Variances for AGZA, AGZC以以F test之結果判斷：之結果判斷： AGZA與與AGZC 產品產品在在R35 之電性表現是有差異的。之電性表現是有差異的。假設檢定 continue 實例練習A study was performed in order to evaluate

38、 the effectiveness of two devices for improving the efficiency of gas home-heating systems. Energy consumption in houses was measured after one of the two devices was installed. The two devices were an electric vent damper (Damper = 1) and a thermally activated vent damper (Damper = 2). The energy c

39、onsumption data (BTU.In) are stacked in one column with a grouping column (Damper) containing identifiers or subscripts to denote the population. You are interested in comparing the variances of the two populations so that you can construct a two-sample t-test and confidence interval to compare the

40、two dampers. .打開FURNACE.MTW 假設檢定 continue實例結果Damper N Lower StDev Upper 1 40 2.40655 3.01987 4.02726 2 50 2.25447 2.76702 3.56416F-Test (normal distribution)Test statistic = 1.19, p-value = 0.558Damper95% Bonferroni Confidence Intervals for StDevs214.03.53.02.52.0DamperBTU.In212015105F-Test0.996Test

41、 Statistic1.19P-Value0.558Levenes TestTest Statistic0.00P-ValueTest for Equal Variances for BTU.In假設檢定 continue為產能考量, 需調整GE Photo Post Bake溫度,實驗為由140度降至120度,因此,需檢定調之後, ADI值是否會受影響?將資料分為調整之前與調整之後,各收集10天左右的資料 比較平均數, 兩母體資料. two sample t test假設檢定 continue Step1 : 輸入資料假設檢定 continue Step2 : 檢定兩資料變異數是否有差異 檢

42、定結果 N Lower StDev UpperBefore 30 0.0426646 0.0552734 0.0777449After 52 0.0379896 0.0464553 0.0595049F-Test (normal distribution)Test statistic = 1.42, p-value = 0.27495% Bonferroni Confidence Intervals for StDevsAfterBefore0.080.070.060.050.04DataAfterBefore0.250.200.150.100.050.00F-Test0.442Test St

43、atistic1.42P-Value0.274Levenes TestTest Statistic0.60P-ValueTest for Equal Variances for Before, After以以F test之結果判斷：之結果判斷： 變異數無差異變異數無差異假設檢定 continue Step3 : 選擇功能(Stat Basic Statistics2 sample t)假設檢定 continue Step4: 點選”Sample in different columns”, 並將資料分別選入First與Second欄內. 根據變異數檢定結果決定是否勾選”Assume Equal

44、 Variance”假設檢定 continue Step5 : 按下ok, 顯現結果檢定結果 N Mean StDev SE MeanBefore 30 0.0791 0.0553 0.010After 52 0.0824 0.0465 0.0064Difference = mu (Before) - mu (After)Estimate for difference: -0.00330595% CI for difference: (-0.026042, 0.019432)T-Test of difference = 0 (vs not =): T-Value = -0.29 P-Value

45、 = 0.773 DF = 80Both use Pooled StDev = 0.0498以以t test之結果判斷：之結果判斷： 無差異無差異假設檢定 continue 實例練習A study was performed in order to evaluate the effectiveness of two devices for improving the efficiency of gas home-heating systems. Energy consumption in houses was measured after one of the two devices was

46、installed. The two devices were an electric vent damper (Damper=1) and a thermally activated vent damper (Damper=2). The energy consumption data (BTU.In) are stacked in one column with a grouping column (Damper) containing identifiers or subscripts to denote the population. Suppose that you performe

47、d a variance test and found no evidence for variances being unequal (see Example of 2 Variances). Now you want to compare the effectiveness of these two devices by determining whether or not there is any evidence that the difference between the devices is different from zero.打開FURNACE.MTW 假設檢定 continue 實例結果Two-sample T for BTU.InDamper N Mean StDev SE Mean1 40 9.91 3.02 0.482 50 10.14 2.77 0.39Difference = mu (1) - mu (2)Estimate for differe