Minitab软件操作方法英文版课件1.ppt

Worksheet Conventions and Menu StructuresMinitab InteroperabilityGraphic CapabilitiesParetoHistogramBox PlotScatter PlotStatistical CapabilitiesCapability AnalysisHypothesis TestContingency TablesANOVADesign of Experiments (DOE),Minitab Training Agenda,Worksheet Conventions and Menu,Worksheet Format and Structure,Session Window,Worksheet Data Window,Menu Bar,Tool Bar,Worksheet Format and Structur,Text Column C1-T(Designated by -T),Numeric Column C3(No Additional Designation),Data Window Column Conventions,Date Column C2-D(Designated by -D),Text Column C1-TNumeric Column,Column Names(Type, Date, Count & Amount,Entered Data for Data Rows 1 through 4,Data Entry Arrow,Data Rows,Other Data Window Conventions,Column NamesEntered Data for D,Menu Bar - Menu Conventions,Hot Key Available (Ctrl-S),Submenu Available ( at the end of selection),Menu Bar - Menu ConventionsHo,Menu Bar - File Menu,Key FunctionsWorksheet File ManagementSavePrintData Import,Menu Bar - File MenuKey Funct,Menu Bar - Edit Menu,Key FunctionsWorksheet File EditsSelectDeleteCopyPasteDynamic Links,Menu Bar - Edit MenuKey Funct,Menu Bar - Manip Menu,Key FunctionsData ManipulationSubset/SplitSortRankRow Data ManipulationColumn Data Manipulation,Menu Bar - Manip MenuKey Func,Menu Bar - Calc Menu,Key FunctionsCalculation CapabilitiesColumn CalculationsColumn/Row StatisticsData StandardizationData ExtractionData Generation,Menu Bar - Calc MenuKey Funct,Menu Bar - Stat Menu,Key FunctionsAdvanced Statistical Tools and GraphsHypothesis TestsRegressionDesign of ExperimentsControl ChartsReliability Testing,Menu Bar - Stat MenuKey Funct,Menu Bar - Graph Menu,Key FunctionsData Plotting CapabilitiesScatter PlotTrend PlotBox PlotContour/3 D plottingDot PlotsProbability PlotsStem & Leaf Plots,Menu Bar - Graph MenuKey Func,Menu Bar - Data Window Editor Menu,Key FunctionsAdvanced Edit and Display OptionsData BrushingColumn SettingsColumn Insertion/MovesCell InsertionWorksheet Settings,Note: The Editor Selection is Context Sensitive. Menu selections will vary for:Data WindowGraphSession WindowDepending on which is selected.,Menu Bar - Data Window Editor,Menu Bar - Session Window Editor Menu,Key FunctionsAdvanced Edit and Display OptionsFont Connectivity Settings,Menu Bar - Session Window Edi,Menu Bar - Graph Window Editor Menu,Key FunctionsAdvanced Edit and Display OptionsBrushing Graph ManipulationColorsOrientationFont,Menu Bar - Graph Window Edito,Menu Bar - Window Menu,Key FunctionsAdvanced Window Display OptionsWindow Management/Display Toolbar Manipulation/Display,Menu Bar - Window MenuKey Fun,Menu Bar - Help Menu,Key FunctionsHelp and TutorialsSubject SearchesStatguide Multiple TutorialsMinitab on the Web,Menu Bar - Help MenuKey Funct,17,MINITAB INTEROPERABILITY,17MINITAB INTEROPERABILITY,Minitab Interoperability,Excel,Minitab,PowerPoint,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 with 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 Worksheet.,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 us 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 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 analysis 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.,Transferring 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 Analysis.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. Copy.,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 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.Now,36,Graphic Capabilities,36Graphic Capabilities,Pareto Chart.,Lets generate a Pareto Chart from a set of data.Go to 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 in “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 Open 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.,Minitab 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 Statistics.,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 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 “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 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 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 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 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 data 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 GPA 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 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 the “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 correlation between Verbal and both Math and GPA data.,Scatter plot.We now have a,70,Minitab Statistical Tools,70Minitab Statistical Tools,71,PROCESS CAPABILITY ANALYSIS,71PROCESS CAPABILITY ANALYSIS,Lets do a process capability study.,Open Minitab and load the file Capability.mpj.,Lets do a process capability,SETTING UP THE TEST.,Go to Stat Quality Tools. Capability Analysis (Weibull).,SETTING UP THE TEST.Go to Sta,Select “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 single,Note that the data does not fit the normal 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 fi,76,HYPOTHESIS TESTING,76HYPOTHESIS TESTING,Load the file normality.mpj.,Setting up the test in Minitab,Load 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 Normalit,Set up the Test,We will test the “Before” column of data.Check Anderson-DarlingClick OK,Set up the TestWe will test th,Analyzing the Results,Since 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 the,Checking 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.W,Select both of the available columns (Before and After) to stack.Type in the location 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 c,Now that we have our data stacked, we are ready to test for equal variances.Go to Stat ANOVA. Test for equal Variances.,Checking for equal variance.,Now that we have our data stac,Setting 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 created when we stacked the data (C3).,We set our Confidence Level for the test (95%).,Then select “OK”.,Setting up the test.Our respo,Here, 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 shown 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 normality,Note 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 two 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% confidenc,Lets test the data with a 2 Sample t Test,- -,Under Stat Basic Statistics. We see several 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 Sa,Since we already have our data stacked, we will load C4 for our samples and C3 for our subscripts.,Setting up the test.,Since we have already tested for equal variances, we can check off this boxNow select Graphs.,Since we already have our data,Setting 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 “OK”. Hit OK again on the next screen.,Setting up the test.We see th,In 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 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 rejected by a larger sample,Interpreting the results.,The dotplot shows how close th,Paired Comparisons,In paired comparisons we are trying to “pair” ob