INTRODUCTIONTOMINITABVERSION(英文版)

1、INTRODUCTION TO MINITAB VERSION 13Worksheet Conventions and Menu StructuresMinitab InteroperabilityGraphic CapabilitiesParetoHistogramBox PlotScatter PlotStatistical CapabilitiesCapability AnalysisHypothesis TestContingency TablesANOVADesign of Experiments (DOE) Minitab Training Agenda Worksheet For

2、mat and StructureSession WindowWorksheet Data WindowMenu BarTool BarText Column C1-T(Designated by -T)Numeric Column C3(No Additional Designation) Data Window Column ConventionsDate Column C2-D(Designated by -D)Column Names(Type, Date, Count & AmountEntered Data for Data Rows 1 through 4Data Ent

3、ry ArrowData Rows Other Data Window Conventions Menu Bar - Menu ConventionsHot Key Available (Ctrl-S)Submenu Available ( at the end of selection) Menu Bar - File MenuKey FunctionsWorksheet File ManagementSavePrintData Import Menu Bar - Edit MenuKey FunctionsWorksheet File EditsSelectDeleteCopyPasteD

4、ynamic Links Menu Bar - Manip MenuKey FunctionsData ManipulationSubset/SplitSortRankRow Data ManipulationColumn Data Manipulation Menu Bar - Calc MenuKey FunctionsCalculation CapabilitiesColumn CalculationsColumn/Row StatisticsData StandardizationData ExtractionData Generation Menu Bar - Stat MenuKe

5、y FunctionsAdvanced Statistical Tools and GraphsHypothesis TestsRegressionDesign of ExperimentsControl ChartsReliability Testing Menu Bar - Graph MenuKey FunctionsData Plotting CapabilitiesScatter PlotTrend PlotBox PlotContour/3 D plottingDot PlotsProbability PlotsStem & Leaf Plots Menu Bar - Da

6、ta 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 selected. Menu Bar -

7、 Session Window Editor MenuKey FunctionsAdvanced Edit and Display OptionsFont Connectivity Settings Menu Bar - Graph Window Editor MenuKey FunctionsAdvanced Edit and Display OptionsBrushing Graph ManipulationColorsOrientationFont Menu Bar - Window MenuKey FunctionsAdvanced Window Display OptionsWind

8、ow Management/Display Toolbar Manipulation/Display Menu Bar - Help MenuKey FunctionsHelp and TutorialsSubject SearchesStatguide Multiple TutorialsMinitab on the WebMINITAB INTEROPERABILITY Minitab InteroperabilityExcelMinitabPowerPoint Starting with Excel.Load file “Sample 1” in Excel. Starting with

9、 Excel.The data is now loaded into Excel. Starting with Excel.Highlight and Copy the Data. Move to Minitab.Open Minitab and select the column you want to paste the data into. Move to Minitab.Select Paste from the menu and the data will be inserted into the Minitab Worksheet. Use Minitab to do the An

10、alysis.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 Analysis.Minitab is now asking for us to identify the columns with the appropriate date.Click in the box for “Response (Y): No

11、te 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 Analysis.Now click in the Predictor (X): box. Then click on “Predicted Workload” and hit the select button Th

12、is will fill in the “Predictor (X):” data field.Both data fields should now be filled.Select OK. 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

13、our PowerPoint presentation. Transferring the Analysis.Lets take care of the graph first.Go to Edit. Copy Graph. Transferring the Analysis.Open PowerPoint and select a blank slide.Go to Edit. Paste Special. Transferring the Analysis.Select “Picture (Enhanced Metafile) This will give you the best gra

14、phics with the least amount of trouble. Transferring the Analysis.Our Minitab graph is now pasted into the powerpoint presentation. We can now size and position it accordingly. Transferring the Analysis.Now we can copy the analysis from the Session window.Highlight the text you want to copy.Select E

15、dit. Copy. Transferring the Analysis.Now go back to your powerpoint presentation.Select Edit. Paste. Transferring the Analysis.Well we got our data, but it is a bit large.Reduce the font to 12 and we should be ok. Presenting the results.Now all we need to do is tune the presentation.Here we position

16、 the graph and summary and put in the appropriate takeaway. Then we are ready to present.Graphic 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.Go to:Stat Quality ToolsPareto

17、 Chart. 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.Minitab now completes our pareto for us ready to be copied and pasted into your PowerPoint prese

18、ntation. 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.Go to:Graph Histogram Histogram.Fill out the screen as follows:Select GPA for our X value Graph VariableHit OK. Histo

19、gram.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.Go to:Stat Basic StatisticsDisplay Descriptive Statistics. Histogram.Fill

20、 out the screen as follows:Select GPA for our Variable.Select Graphs. Histogram.Select Graphical Summary.Select OK.Select OK again on the next screen. 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

21、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.Lets look at another “Histogram” tool we can use to evaluate and present data.Go to File Open Project. Load the file overfill.mpj. Histogra

22、m.Go to:Graph Marginal Plot Histogram.Fill out the screen as follows:Select filler 1 for the Y Variable.Select head for the X VariableSelect OK. 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

23、readings.This type of Histogram is called a “Marginal Plot”. Boxplot.Lets look at the same data using a Boxplot. Boxplot.Go to:Stat Basic StatisticsDisplay Descriptive Statistics. Boxplot.Fill out the screen as follows:Select “filler 1” for our Variable.Select Graphs. Boxplot.Select Boxplot of data.

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

25、xplot.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. 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

26、of the GPA results against our Math and Verbal scores. Scatter plot.Go to:Graph Plot. 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.We now have two Scatter plots of the data stacked on top of eac

27、h otherWe can display this better by tiling the graphs. Scatter plot.To do this:Go to WindowTile. Scatter plot.Now we can see both Scatter plots of the data Scatter plot.There is another way we can generate these scatter plots.Go to:Graph Matrix Plot. Scatter Plot.Fill out the screen as follows:Clic

28、k in the “Graph variables” blockHighlight all three available data setsClick on the “Select” button.Select OK when done. 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 Ve

29、rbal and both Math and GPA data.Minitab Statistical ToolsPROCESS CAPABILITY ANALYSISLets do a process capability study.Open Minitab and load the file Capability.mpj.SETTING UP THE TEST.Go to Stat Quality Tools. Capability Analysis (Weibull).Select “Torque” for our single data column.Enter a lower sp

30、ec of 10 and an upper spec of 30. Then select “OK”.SETTING UP THE TEST.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

31、 of 147,055.INTERPRETING THE DATA.HYPOTHESIS TESTINGLoad the file normality.mpj.Setting up the test in MinitabChecking 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.Set up the TestWe will test the

32、 “Before” column of data.Check Anderson-DarlingClick OKAnalyzing 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.)Checking for equal variance.We now want to see if w

33、e have equal variances in our samples.To perform this test, our data must be “stacked”.To accomplish this go to Manip Stack Stack Columns.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

34、location where you want the subscripts stored In this example we will use C3.Select OK.Checking for equal variance.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.Setting up the test.Our response will

35、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”.Here, we see the 95% confidence interva

36、ls 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 normalityNote that we get a graphical su

37、mmary 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

38、 both populations.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.Since we already have our data stacked, we will l

39、oad 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.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

40、so we select “Dotplots of data” and hit “OK”. Hit OK again on the next screen.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 b

41、etween the means of the two populations. Interpreting the results.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

42、sample Interpreting the results.Paired Comparisons In 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 manual instrument. It can also be used in measu

43、rement 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.mpj2_Hypothesis_Testing_Shoe_wear.mpj In this example we are trying to determine if shoe material “A” wear rate is different from sh

44、oe 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 Paired ComparisonGo to Stat. Basic Statistics Paired t. Paired ComparisonSelect t

45、he samplesGo to Graphs. Paired ComparisonSelect the Boxplot for our graphical output.Then select OK. Paired 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

46、 us confirmation that the shoe materials are significantly different. CONTINGENCY TABLES(CHI SQUARE)Entering the data.Enter the data in a table format. For this example, load the file Contingency Table.mpj.Lets set up a contingency table.Contingency tables are found under Stat. Tables Chi Square Tes

47、t. Select the columns which contain the table. Then select “OK”Setting up the test.Note 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 and directly below, Minitab calculates th

48、e 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.ANalysis Of VArianceANOVALets set up the analysisLoad the file Anova example.mpjStack the data in C4

49、 and place the subscripts in C5Set up the analysis.Select StatANOVAOne waySelect C4 Responses C5 FactorsThen select Graphs.Set up the analysis.Choose boxplots of data.Then OKSet up the analysis.Note that the P value is less than .05that means that we reject the null hypothesisAnalyzing the results.L

50、ets Look At Main Effects.Choose StatANOVAMain Effects Plot.Main EffectsSelectC4 ResponseC5 FactorsOKAnalyzing Main Effects.Liters/Hr 3Liters/Hr 2Liters/Hr 12221201918FormulationLiters Per HMain Effects Plot - Data Means for Liters Per HFormulation 1 Has Lowest Fuel ConsumptionDESIGN OF EXPERIMENTS (

51、DOE) FUNDAMENTALSFirst Create an Experimental Design.Go to StatDOE Factorial.Create Factorial Design.First Create an Experimental Design.Select 2 Level Factorial design with 3 factorsThen go to Display Available Designs.Bowling Example (continued)We can now see the available experimental designs. We

52、 will be using the Full (Factorial) for 3 factors and we can see that it will require 8 runsNow, select OK and go back to the main screen.Once at the main screen select Designs.Bowling Example (continued)Select your design. We will be using the Full (Factorial) and again we can see that it will requ

53、ire 8 runsNow, select OK and go back to the main screen.Once at the main screen select Factors.Bowling Example (continued)Fill in the names for your factors. Then fill in the actual conditions for low (-) or high (+)Now, select OK and go back to the main screen.Once at the main screen select Options

54、.Bowling Example (continued)Remove the option to Randomize Runs. Now, select OK and go back to the main screen.Once at the main screen select OK.Bowling Example (continued)Minitab has now designed our experiment for us. Now, type your Data from each of your experimental treatments into C8.We are now

55、 ready to analyze the resultsBowling Example (continued)Go toStat.DOEFactorial.Analyze Factorial Design.Bowling Example (continued)Highlight your Data column and use Select to place it in the Responses box. Then, select the Terms Option.Bowling Example (continued)Note that Selected Terms has all of

56、the available choices already selected. We need do nothing further. Select OK.Then, at the main screen select GraphsBowling Example (continued)Select your Effects Plots and reset your Alpha to .05.Select OK to return to the main screen and then select OK again.Bowling Example (continued)Note that only one effect has a significance greater than 95%.All th