Minitab系统的基本操作课件

1、Minitab系统的基本操作Minitab系统的基本操作Worksheet Conventions and Menu StructuresMinitab InteroperabilityGraphic CapabilitiesParetoHistogramBox PlotScatter PlotStatistical CapabilitiesCapability AnalysisHypothesis TestContingency TablesANOVADesign of Experiments (DOE) Minitab Training AgendaWorksheet Convention

2、s and Menu Worksheet Format and StructureSession WindowWorksheet Data WindowMenu BarTool Bar Worksheet Format and StructurText Column C1-T(Designated by -T)Numeric Column C3(No Additional Designation) Data Window Column ConventionsDate Column C2-D(Designated by -D)Text Column C1-TNumeric ColumnColum

3、n Names(Type, Date, Count & AmountEntered Data for Data Rows 1 through 4Data Entry ArrowData Rows Other Data Window ConventionsColumn NamesEntered Data for D Menu Bar - Menu ConventionsHot Key Available (Ctrl-S)Submenu Available ( at the end of selection) Menu Bar - Menu ConventionsHo Menu Bar - Fil

4、e MenuKey FunctionsWorksheet File ManagementSavePrintData Import Menu Bar - File MenuKey Funct Menu Bar - Edit MenuKey FunctionsWorksheet File EditsSelectDeleteCopyPasteDynamic Links Menu Bar - Edit MenuKey Funct Menu Bar - Manip MenuKey FunctionsData ManipulationSubset/SplitSortRankRow Data Manipul

5、ationColumn Data Manipulation Menu Bar - Manip MenuKey Func Menu Bar - Calc MenuKey FunctionsCalculation CapabilitiesColumn CalculationsColumn/Row StatisticsData StandardizationData ExtractionData Generation Menu Bar - Calc MenuKey Funct Menu Bar - Stat MenuKey FunctionsAdvanced Statistical Tools an

6、d GraphsHypothesis TestsRegressionDesign of ExperimentsControl ChartsReliability Testing Menu Bar - Stat MenuKey Funct Menu Bar - Graph MenuKey FunctionsData Plotting CapabilitiesScatter PlotTrend PlotBox PlotContour/3 D plottingDot PlotsProbability PlotsStem & Leaf Plots Menu Bar - Graph MenuKey Fu

7、nc Menu Bar - Data Window Editor MenuKey FunctionsAdvanced Edit and Display OptionsData BrushingColumn SettingsColumn Insertion/MovesCell InsertionWorksheet SettingsNote: The Editor Selection is Context Sensitive. Menu selections will vary for:Data WindowGraphSession WindowDepending on which is sele

8、cted. Menu Bar - Data Window Editor Menu Bar - Session Window Editor MenuKey FunctionsAdvanced Edit and Display OptionsFont Connectivity Settings Menu Bar - Session Window Edi Menu Bar - Graph Window Editor MenuKey FunctionsAdvanced Edit and Display OptionsBrushing Graph ManipulationColorsOrientatio

9、nFont Menu Bar - Graph Window Edito Menu Bar - Window MenuKey FunctionsAdvanced Window Display OptionsWindow Management/Display Toolbar Manipulation/Display Menu Bar - Window MenuKey Fun Menu Bar - Help MenuKey FunctionsHelp and TutorialsSubject SearchesStatguide Multiple TutorialsMinitab on the Web

10、 Menu Bar - Help MenuKey FunctMINITAB INTEROPERABILITYMINITAB INTEROPERABILITY Minitab InteroperabilityExcelMinitabPowerPoint Minitab InteroperabilityExcel Starting with Excel.Load file “Sample 1” in Excel. Starting with Excel.Load fi Starting with Excel.The data is now loaded into Excel. Starting w

11、ith Excel.The dat Starting with Excel.Highlight and Copy the Data. Starting with Excel.Highlig Move to Minitab.Open Minitab and select the column you want to paste the data into. Move to Minitab.Open Minita Move to Minitab.Select Paste from the menu and the data will be inserted into the Minitab Wor

12、ksheet. Move to Minitab.Select Past Use Minitab to do the Analysis.Lets say that we would like to test correlation between the Predicted Workload and the actual workload.Select Stat Regression. Fitted Line Plot. Use Minitab to do the Analysi Use Minitab to do the Analysis.Minitab is now asking for u

13、s to identify the columns with the appropriate date.Click in the box for “Response (Y): Note that our options now appear in this box.Select “Actual Workload” and hit the select button.This will enter the “Actual Workload” data in the Response (Y) data field. Use Minitab to do the Analysi Use Minitab

14、 to do the Analysis.Now click in the Predictor (X): box. Then click on “Predicted Workload” and hit the select button This will fill in the “Predictor (X):” data field.Both data fields should now be filled.Select OK. Use Minitab to do the Analysi Use Minitab to do the Analysis.Minitab now does the a

15、nalysis and presents the results.Note that in this case there is a graph and an analysis summary in the Session WindowLets say we want to use both in our PowerPoint presentation. Use Minitab to do the Analysi Transferring the Analysis.Lets take care of the graph first.Go to Edit. Copy Graph. Transfe

16、rring the Analysis.L Transferring the Analysis.Open PowerPoint and select a blank slide.Go to Edit. Paste Special. Transferring the Analysis.O Transferring the Analysis.Select “Picture (Enhanced Metafile) This will give you the best graphics with the least amount of trouble. Transferring the Analysi

17、s.S Transferring the Analysis.Our Minitab graph is now pasted into the powerpoint presentation. We can now size and position it accordingly. Transferring the Analysis.O Transferring the Analysis.Now we can copy the analysis from the Session window.Highlight the text you want to copy.Select Edit. Cop

18、y. Transferring the Analysis.N Transferring the Analysis.Now go back to your powerpoint presentation.Select Edit. Paste. Transferring the Analysis.N Transferring the Analysis.Well we got our data, but it is a bit large.Reduce the font to 12 and we should be ok. Transferring the Analysis.W Presenting

19、 the results.Now all we need to do is tune the presentation.Here we position the graph and summary and put in the appropriate takeaway. Then we are ready to present. Presenting the results.NowGraphic CapabilitiesGraphic Capabilities Pareto Chart.Lets generate a Pareto Chart from a set of data.Go to

20、File Open Project. Load the file Pareto.mpj.Now lets generate the Pareto Chart. Pareto Chart.Lets generat Pareto Chart.Go to:Stat Quality ToolsPareto Chart. Pareto Chart.Go to: Pareto Chart.Fill out the screen as follows:Our data is already summarized so we will use the Chart Defects table. Labels i

21、n “Category”Frequencies in “Quantity”.Add title and hit OK. Pareto Chart.Fill out the Pareto Chart.Minitab now completes our pareto for us ready to be copied and pasted into your PowerPoint presentation. Pareto Chart.Minitab now c Histogram.Lets generate a Histogram from a set of data.Go to File Ope

22、n Project. Load the file 2_Correlation.mpj.Now lets generate the Histogram of the GPA results. Histogram.Lets generate a Histogram.Go to:Graph Histogram Histogram.Go to: Histogram.Fill out the screen as follows:Select GPA for our X value Graph VariableHit OK. Histogram.Fill out the scr Histogram.Min

23、itab now completes our histogram for us ready to be copied and pasted into your PowerPoint presentation.This data does not look like it is very normal.Lets use Minitab to test this distribution for normality. Histogram.Minitab now comp Histogram.Go to:Stat Basic StatisticsDisplay Descriptive Statist

24、ics. Histogram.Go to: Histogram.Fill out the screen as follows:Select GPA for our Variable.Select Graphs. Histogram.Fill out the scr Histogram.Select Graphical Summary.Select OK.Select OK again on the next screen. Histogram.Select Graphical Histogram.Note that now we not only have our Histogram but

25、a number of other descriptive statistics as well.This is a great summary slide.As for the normality question, note that our P value of .038 rejects the null hypothesis (P.05). So, we conclude with 95% confidence that the data is not normal. Histogram.Note that now we Histogram.Lets look at another “

26、Histogram” tool we can use to evaluate and present data.Go to File Open Project. Load the file overfill.mpj. Histogram.Lets look at an Histogram.Go to:Graph Marginal Plot Histogram.Go to: Histogram.Fill out the screen as follows:Select filler 1 for the Y Variable.Select head for the X VariableSelect

27、 OK. Histogram.Fill out the scr Histogram.Note that now we not only have our Histogram but a dot plot of each head data as well.Note that head number 6 seems to be the source of the high readings.This type of Histogram is called a “Marginal Plot”. Histogram.Note that now we Boxplot.Lets look at the

28、same data using a Boxplot. Boxplot.Lets look at the Boxplot.Go to:Stat Basic StatisticsDisplay Descriptive Statistics. Boxplot.Go to: Boxplot.Fill out the screen as follows:Select “filler 1” for our Variable.Select Graphs. Boxplot.Fill out the scree Boxplot.Select Boxplot of data.Select OK.Select OK

29、 again on the next screen. Boxplot.Select Boxplot of Boxplot.We now have our Boxplot of the data. Boxplot.We now have our Bo Boxplot.There is another way we can use Boxplots to view the data.Go to:Graph Boxplot. Boxplot.There is another w Boxplot.Fill out the screen as follows:Select “filler 1” for

30、our Y Variable.Select “head” for our X Variable.Select OK. Boxplot.Fill out the scree Boxplot.Note that now we now have a box plot broken out by each of the various heads.Note that head number 6 again seems to be the source of the high readings. Boxplot.Note that now we n Scatter plot.Lets look at d

31、ata using a Scatterplot.Go to File Open Project. Load the file 2_Correlation.mpj.Now lets generate the Scatterplot of the GPA results against our Math and Verbal scores. Scatter plot.Lets look at Scatter plot.Go to:Graph Plot. Scatter plot.Go to: Scatter Plot.Fill out the screen as follows:Select GP

32、A for our Y Variable.Select Math and Verbal for our X Variables.Select OK when done. Scatter Plot.Fill out the Scatter plot.We now have two Scatter plots of the data stacked on top of each otherWe can display this better by tiling the graphs. Scatter plot.We now have t Scatter plot.To do this:Go to

33、WindowTile. Scatter plot.To do this: Scatter plot.Now we can see both Scatter plots of the data Scatter plot.Now we can se Scatter plot.There is another way we can generate these scatter plots.Go to:Graph Matrix Plot. Scatter plot.There is anot Scatter Plot.Fill out the screen as follows:Click in th

34、e “Graph variables” blockHighlight all three available data setsClick on the “Select” button.Select OK when done. Scatter Plot.Fill out the Scatter plot.We now have a series of Scatter plots, each one corresponding to a combination of the data sets availableNote that there appears to be a strong cor

35、relation between Verbal and both Math and GPA data. Scatter plot.We now have aMinitab Statistical ToolsMinitab Statistical ToolsPROCESS CAPABILITY ANALYSISPROCESS CAPABILITY ANALYSISLets do a process capability study.Open Minitab and load the file Capability.mpj.Lets do a process capability SETTING

36、UP THE TEST.Go to Stat Quality Tools. Capability Analysis (Weibull).SETTING UP THE TEST.Go to StaSelect “Torque” for our single data column.Enter a lower spec of 10 and an upper spec of 30. Then select “OK”.SETTING UP THE TEST.Select “Torque” for our singleNote that the data does not fit the normal

37、curve very well.Note that the Long Term capability (Ppk) is 0.43. This equates to a Z value of 3*0.43=1.29 standard deviations or sigma values.This equates to an expected defect rate PPM of 147,055.INTERPRETING THE DATA.Note that the data does not fiHYPOTHESIS TESTINGHYPOTHESIS TESTINGLoad the file

38、normality.mpj.Setting up the test in MinitabLoad the file normality.mpj.Checking the Data for Normality.Its important that we check for normality of data samples.Lets see how this works.Go to STAT. Basic Statistics. Normality Test.Checking the Data for NormalitSet up the TestWe will test the “Before

39、” column of data.Check Anderson-DarlingClick OKSet up the TestWe will test thAnalyzing the ResultsSince the P value is greater than .05 we can assume the “Before” data is normalNow repeat the test for the “After” Data (this is left to the student as a learning exercise.)Analyzing the ResultsSince th

40、eChecking for equal variance.We now want to see if we have equal variances in our samples.To perform this test, our data must be “stacked”.To accomplish this go to Manip Stack Stack Columns.Checking for equal variance.WSelect both of the available columns (Before and After) to stack.Type in the loca

41、tion where you want the stacked data. In this example we will use C4.Type in the location where you want the subscripts stored In this example we will use C3.Select OK.Checking for equal variance.Select both of the available cNow that we have our data stacked, we are ready to test for equal variance

42、s.Go to Stat ANOVA. Test for equal Variances.Checking for equal variance.Now that we have our data stacSetting up the test.Our response will be the actual receipt performance for the two weeks we are comparing. In this case we had put the stacked data in column C4.Our factors is the label column we

43、created when we stacked the data (C3).We set our Confidence Level for the test (95%).Then select “OK”.Setting up the test.Our respoHere, we see the 95% confidence intervals for the two populations. Since they overlap, we know that we will fail to reject the null hypothesis.The F test results are sho

44、wn here. We can see from the P-Value of .263 that again we would fail to reject the null hypothesis. Note that the F test assumes normalityNote that we get a graphical summary of both sets of data as well as the relevant statistics. Analyzing the data.Levenes test also compares the variance of the t

45、wo samples and is robust to nonnormal data. Again, the P-Value of .229 indicates that we would fail to reject the null hypothesis.Here we have box plot representations of both populations.Here, we see the 95% confidencLets test the data with a 2 Sample t Test- -Under Stat Basic Statistics. We see se

46、veral of the hypothesis tests which we discussed in class. In this example we will be using a 2 Sample t Test.Go to Stat. Basic Statistics. 2 Sample t.Lets test the data with a 2 SaSince we already have our data stacked, we will load C4 for our samples and C3 for our subscripts.Setting up the test.S

47、ince we have already tested for equal variances, we can check off this boxNow select Graphs.Since we already have our dataSetting up the test.We see that we have two options for our graphical output. For this small a sample, Boxplots will not be of much value so we select “Dotplots of data” and hit

48、“OK”. Hit OK again on the next screen.Setting up the test.We see thIn the session window we have each populations statistics calculated for us.Note that here we have a P value of .922. We therefore find that the data does not support the conclusion that there is a significant difference between the

49、means of the two populations. Interpreting the results.In the session window we have The dotplot shows how close the datapoints in the two populations fall to each other. The close values of the two population means (indicated by the red bar) also shows little chance that this hypothesis could be re

50、jected by a larger sample Interpreting the results.The dotplot shows how close thPaired ComparisonsIn paired comparisons we are trying to “pair” observations or treatments. An example would be to test automatic blood pressure cuffs and a nurse measuring the blood pressure on the same patient using a

51、 manual instrument. It can also be used in measurement system studies to determine if operators are getting the same mean value across the same set of samples.Lets look at an example: 2_Hypothesis_Testing_Shoe_wear.mpjPaired ComparisonsIn paired co2_Hypothesis_Testing_Shoe_wear.mpjIn this example we

52、 are trying to determine if shoe material “A” wear rate is different from shoe material “B”.Our data has been collected using ten boys, whom were asked to wear one shoe made from each material.Ho: Material “A” wear rate = Material “B” wear rateHa: Material “A” wear rate Material “B” wear rate 2_Hypo

53、thesis_Testing_Shoe_wearPaired ComparisonGo to Stat. Basic Statistics Paired t. Paired ComparisonGo to Stat. Paired ComparisonSelect the samplesGo to Graphs. Paired ComparisonSelect the saPaired ComparisonSelect the Boxplot for our graphical output.Then select OK. Paired ComparisonSelect the BoPaire

54、d ComparisonWe see how the 95% confidence interval of the mean relates to the value we are testing. In this case, the value falls outside the 95% confidence interval of the data mean. This gives us confirmation that the shoe materials are significantly different. Paired ComparisonWe see how thCONTIN

55、GENCY TABLES(CHI SQUARE)CONTINGENCY TABLES(CHI SQUAREEntering the data.Enter the data in a table format. For this example, load the file Contingency Table.mpj.Entering the data.Enter the dLets set up a contingency table.Contingency tables are found under Stat. Tables Chi Square Test. Lets set up a c

56、ontingency tabSelect the columns which contain the table. Then select “OK”Setting up the test.Select the columns which contaNote that you will have the critical population and test statistics displayed in the session window. Minitab builds the table for you. Note that our original data is presented

57、and directly below, Minitab calculates the expected values. Here, Minitab calculates the Chi Square statistic for each data point and totals the result. The calculated Chi Square statistic for this problem is 30.846. Performing the Analysis.Note that you will have the crANalysis Of VArianceANOVAANal

58、ysis Of VArianceLets set up the analysisLoad the file Anova example.mpjStack the data in C4 and place the subscripts in C5Lets set up the analysisLoad Set up the analysis.Select StatANOVAOne waySet up the analysis.Select StSelect C4 Responses C5 FactorsThen select Graphs.Set up the analysis.SelectSe

59、t up the analysis.Choose boxplots of data.Then OKSet up the analysis.Choose boxplots of data.SetNote that the P value is less than .05that means that we reject the null hypothesisAnalyzing the results.Note that the P value is less Lets Look At Main Effects.Choose StatANOVAMain Effects Plot.Lets Look

60、 At Main Effects.ChMain EffectsSelectC4 ResponseC5 FactorsOKMain EffectsSelectAnalyzing Main Effects.Formulation 1 Has Lowest Fuel ConsumptionAnalyzing Main Effects.FormulDESIGN OF EXPERIMENTS (DOE) FUNDAMENTALSDESIGN OF EXPERIMENTS (DOE) First Create an Experimental Design.Go to StatDOE Factorial.C