What s new in MINITAB English R14

Transcription

1 What s new in MINITAB English R14

2 Overview Graphics Interface enhancements Statistical Enhancements

3 Graphics Enhanced appearance Easy to create Easy to edit Easy to layout Paneling Automatic updating Rotating 3D plots Information display tools Data options Preferences New graphs: Individual value plots Area graphs ECDF plots Built-in 4-in-1 residual plots

4 Enhanced Appearance Better looking graphs by default Default titles Release 13 Default use of color Better use of space

5 Enhanced Appearance Legends Nicer legends, and legends for overlays Release 13

6 Easy to Create Icons in menus and toolbars to help users recognize graphs

7 Easy to Create Galleries show main subtypes

8 Easy to Create More streamlined and better organized dialog boxes Release 13 Release 14

9 Example Wait times at a Medical Facility The wait time (in minutes) for patients to see a cardiac surgeon are collected. The time that they sign-in at reception is also recorded. Tools: Time series plot Graph editing toolbar Graph editing

10 Example 1. Open Wait Times.mpj. 2. Create a Time series plot of the data: a.choose Graph > Time Series Plot. b.choose Simple, then click OK. c. In Series, enter Wait times. Click OK. 3. Double-click the graph s x-axis to edit it: 4. Time: Stamp with the column Time. 5. Choose Tools > Toolbars > Graph Editing to access the graph editing toolbar.

11 Example 6. Use the Select area of the Graph Editing toolbar to select the connect line. 7. Click the Edit button adjacent to the Select area to edit the line. (Selecting an item and using the Edit button is the same as double-clicking the item). 8. Change the color of the connect line. 9. Add a footnote titled Data Analysis via the Add section of the Graph Editing toolbar. (Alternatively, choose Editor > Add > Footnote, or right-click the graph and choose Add.

12 4:34 pm 3:54 pm 2:54 pm 12:10 pm 12:50 pm 1:30 pm 2:10 pm 11:00 am Example Results Time Wait Times 10 0 Time Series Plot of Wait Times 7:50 am 9:05 am 9:46 am Data Analysis

13 Easy to Edit Double-click an item to edit it.

14 Easy to Edit Double-click an item to edit it.

15 Easy to Edit Add elements without re-creating the graph

16 Example Product Evaluation Testers evaluate two droppers for their ability to dispense eye drops. They are interested in any differences between the two product types (Original and Sensitive), the two droppers (Current and New), and the three angles at which the dropper is held (0, 45, and 90 ). Testers also measure the weight of the dispensed product and calculate the percentage of the product that is dispensed.

17 Example Tools Individual value plot Graphics Point selection, adding to the data display, Undo/Redo, Make Similar Graph, and Duplicate Graph Layout tool

18 Example 1. Open Product Evaluation.mpj. 2. Create an individual value plot: a. Choose Graph > Individual Value Plot. b. Choose One Y with Groups. c. In Graph Variable, enter Weight. d. In Categorical Variables, enter Product Type Dropper Type Angle. e. Click OK. 3. Click on the symbols. Note: To choose all points, click once. To choose an individual point, click twice.

19 Example 4. Edit the symbols, choose Groups and specify Dropper Type. 5. Right-click the graph and choose Add > Data Display. Choose the mean symbol. 6. Choose Editor > Make Similar Graph. 7. Specify Percent for Weight. 8. Choose Editor > Graph Layout. 9. In Rows, enter 2. In Columns, enter Place both graphs into the layout. Click Finish.

20 Results Individual Value Plot of Percent vs Product Type, Dropper Type, Angle Percent 0 degrees 45 degrees 90 degrees C urrent 0 degrees 45 degrees 90 degrees New 0 degrees 45 degrees 90 degrees C urrent 0 degrees 45 degrees 90 degrees New Angle Dropper Ty pe O riginal S ensitiv e Product Type Individual Value Plot of Weight vs Product Type, Dropper Type, Angle Dropper Type Current New Weight 0 degrees Angle C urrent 45 degrees 90 degrees 0 degrees 45 degrees New 90 degrees 0 degrees 45 degrees C urrent 90 degrees 0 degrees 45 degrees New 90 degrees Dropper Ty pe O riginal S ensitiv e Product Type Dropper Type Current New

21 New Graphs Individual Value Plot Use to assess and compare sample distributions

22 Easy to Edit Once you have customized the graph, you can easily make similar ones.

23 Easy to Edit Intelligent Selection Intelligent selecting Click once to select all points Click twice to select the group Click three times to select an individual point

24 Easy to Edit Other editing improvements Multiple undo and redo Text in point sizes Macro writers can copy the command language of a graph and capture all the graph editing

25 Easy to Layout New, visual layout tool Easy to change layout grid List of graphs in project You can drag graphs to different cells Preview of selected graph

26 Example Sales Revenue Analysts collect 62 months of sales revenue for 2 department store locations. They are interested in comparing the locations. Tools Histogram / multiple graphs Time series plot Panels Updating graphs

27 Example Create a histogram for each location. 1. Open Sales Revenue.mpj. 2. Choose Graph > Histogram. 3. Choose Simple. 4. In Graph variables, enter Store 1 Store Click Multiple Graphs. 6. Under Show Graph Variables, choose Overlaid on the same graph. 7. Click OK.

28 Example Plot the data over time: 8. Choose Graph > Time Series Plot. 9. Choose Multiple, then click OK. 10. In Series, enter Store 1 Store Click OK. Panel the data to improve the display: 12. Right-click the graph and choose Panel. 13. Choose Split data across panels, then enter Click OK.

29 Example Edit the x-axis label: 14. Double-click the x-axis. 15. Time-stamp the Date column. 16. Click OK. Make changes to the graph: 17. Change a value in the data set. 18. Return to either graph. 19. Right-click the graph and choose Update Graph Now. 20. Click OK.

30 Results Histogram of Store 1, Store 2 20 Variable Store 1 Store 2 15 Frequency 10 5 Time Series Plot of Store 1, Store Variable Store 1 Store Data Data 1/ / /1998 6/ /1999 6/ /2000 6/ /2001 Date 6/ /2002

31 Paneling An important new feature that lets you decide exactly how to display data Show different graph variables in panels on the same display Show BY variables on panels, separate graphs, or any combination Split a crowded display into panels

32 Paneling Show different graph variables in panels on the same display

33 Paneling Show BY variables on panels, separate graphs, or any combination

34 Paneling Alternatively

35 Paneling Allows you to split a crowded display into panels

36 Paneling

37 Automatic Updating Graphs are now linked to the data, and can be updated manually or automatically. Right-click the icon in the upper left corner, or change settings in the Editor menu.

38 Automatic Updating New icons indicate the update status Green = graph is up to date Yellow = data used to create the graph has changed Red = on hold (some condition prevents auto-updating) White = ineligible for updating (layout, or contains statistical results)

39 Rotating 3D Plots You can now truly rotate 3D plots in real time. A preview window pops up to show exactly how the graph will spin.

40 Information Display Tools Get more information from graphs than ever before Tooltips Crosshairs Plant flags in contour plots

41 Data Options New Data Options button in graph dialogs allows you to: Subset graph data no need to manipulate the worksheet Include or exclude missing groups and values Use frequency columns to graph summarized data

42 Preferences Release 14 offers much more extensive preferences, which allow users to customize default settings. Choose Tools > Options.

43 New Graphs: 4-in-1 Residual Plots Now a built-in graph choice in DOE, ANOVA, and Regression Do not need to store residuals and fits Can be set as a default preference

44 Interface Enhancements Customizable menus and toolbars Buttons for commands Expanded preferences User-editable profiles Control chart dialogs & menus Enhanced Help Expanded StatGuide

45 Interface Enhancements Save output and data as HTML Import Microsoft Excel workbook sheets individually Import currency formats Improvements to Copy and Sort Tooltips

46 Customizable Menus and Toolbars

47 Customizable Menus and Toolbars Use Tools > Customize to: Add or remove commands from menus Create new menus Remove a menu Choose which toolbars are active Create custom toolbars Add programs to the Tools menu Change or add keyboard shortcuts Reset menus and toolbars to their defaults

48 Control Chart Dialogs and Menus Menus are better organized by category

49 Control Chart Dialogs and Menus Streamlined dialog boxes (even though they have more functionality) Release 13 Release 14

50 Control Chart Dialogs and Menus Streamlined dialog boxes (even though they have more functionality) New ability to store values in the worksheet

51 Enhanced Help New HTML Help offers distinct advantages View the table of contents, index, or search results alongside the content for quick referencing. Links change color when followed so you know where you've been, making navigation simpler. Topics are organized into books which can be printed conveniently. Detailed information about the methods and formulas used in all MINITAB statistical procedures is available by clicking the See Also' link. Thoroughly updated and expanded tutorials and an all new Meet MINITAB make learning MINITAB easier.

52 Expanded StatGuide New StatGuide content Multivariate analysis Time series Nonparametrics MANOVA

53 Save Output and Data as HTML Save Report As Save Session Window As Save Current Worksheet As

54 Import Microsoft Excel Workbooks Sheets are now imported individually Data are stored in separate Excel worksheets Release 13 merged the sheets Release 14 produces the expected behavior

55 Import Currency Formats MINITAB 14 recognizes international currency formats when imported and places the results in a numeric column. Dollar $ Pound Euro Yen Won

56 Tooltips Not just for graphics

57 Statistics General statistics Quality statistics Partial least squares regression Design of experiments Reliability

58 General Statistics Basic statistics Display Descriptive Statistics Generate statistics from summary data Analysis of variance R 2 and R 2 (adj) Tables Improved cross-tabulation Cleaner interface New measures of association Fisher s exact test for 2x2 tables

59 General Statistics Simulation and distributions New distributions added: Loglogistic Largest extreme value Smallest extreme value Multivariate normal

60 Open Statistical Enhancements.mpj Worksheets in this project: Loglogistic Summary Tests Tables Fisher s Exact Test Control Charts Johnson Transformation Individual Distribution Cause and Effect Autogage Partial Least Squares Analyze Variability Reliable Vseat Circuit

61 Display Descriptive Statistics Open Summary Tests.mtw. Calculate the average Whiteness score for each Stain. Specify which statistics you want to display. Save your selection as a preference under Tools > Options > Individual Commands > Display Descriptive Statistics.

62 Basic Statistics From Summary Data Z-tests t-tests Test of two variances It is believed that the true average temperature during the summer is 80º. Conduct a t-test to verify this hypothesis with summarized data: Sample Size = 92 Mean = 82 Standard deviation = 7

63 Analysis of Variance R 2 and R 2 (adj) are now displayed in the Session window.

64 Cross-Tabulation Improved interface 1.Open Tables.mtw. 2.Create a table that summarizes the number of times a black belt and non-belt were categorized on a 1-5 rating scale.

65 Cross-Tabulation Improved interface Easier to specify the layout of your table Stripped out of crosstabulation dialog box to simplify More options in Chi-Square

66 Cross-Tabulation New measures of association Goodman-Kruskal gamma, Somer's D, and Kendall's tau-b Pearson's and Spearman's

67 Fisher s Exact Test for 2x2 Tables A test of independence based on an exact distribution rather than on an approximate chi-square distribution Useful when the expected cell counts are low and the chi-square approximation is not very good A very commonly requested feature

68 Fisher s Exact Test for 2x2 Tables Open Fisher s Exact Test.mtw.

69 Fisher s Exact Test for 2x2 Tables The output suggests that the overall rating category and belt type are dependent on one another.

70 New Distributions Added to Calc > Probability Distributions and Random Data Loglogistic Commonly used in biostatistics to model survival probabilities. Largest extreme value Used to model high extremes. For example, the probability of exceeding certain temperature, rainfall amounts (floods), and gust velocities. Smallest extreme value Used to model minimum values. Multivariate normal Multivariate normal A generalization of the normal distribution for variables that may be correlated. Parameters include not only means and variances, but also the correlations between the variables (random data only).

71 Quality Statistics Control charts T 2 Generalized variance Combination T 2 /generalized variance Multivariate EWMA Quality tools Johnson transformation Distribution identification Nonnormal capability Capability for multiple variables Cause-and-effect Attribute gage study analytic method

72 Control Charts When responses are highly correlated, create a multivariate control chart to assess whether multiple variables are simultaneously in control. Open Control charts.mtw.

73 T 2 Chart Multivariate equivalent of the X-bar chart Session window output shows which variables caused alarms.

74 Generalized Variance Chart Multivariate equivalent of the S chart

75 T 2 /Generalized Variance Chart Multivariate equivalent of X-bar/S chart

76 Multivariate EWMA Chart Multivariate equivalent of the EWMA chart Each point includes weighting from all previous data, allowing you to detect small process shifts quicker than with other multivariate methods.

77 Quality Tools Enhancements to: Handle nonnormal data Johnson transformation Individual distribution identification Facilitate capability analysis Nonnormal capability Multiple variables Improve cause-and-effects charts Provide standard attribute gage R&R

78 Quality Tools = new

79 Johnson Transformation Transforms data sets from nonnormal to normal Helpful when collected data are nonnormal, but one desires to apply a methodology that requires the underlying distribution to be normal May transform data on which Box-Cox is ineffective Optimally selects from three distribution families Allows you to store the transformed data for further analysis

80 Johnson Transformation Open Johnson Transformation.mtw.

81 Johnson Transformation The original data fail the normality test, but the transformed data pass.

82 Individual Distribution Identification Fits 14 distributions to the data to identify the best fit Finds appropriate distributions to specify in capability analysis for nonnormal data Useful for customers who have nonnormal data and want to fit the best model to the data, rather than trying to make it look normally distributed via a transformation

83 Individual Distribution Identification Open Individual Distribution.mtw.

84 Individual Distribution Identification Session window output A p-value greater than α suggests that the data follow the indicated distribution. A likelihood ratio test p-value (LRT P) greater than α suggests that the 3-parameter version does not fit significantly better than the 2-parameter version.

85 Individual Distribution Identification Graph output

86 Nonnormal Capability Analysis Produces a process capability report when data do not follow a normal distribution 13 nonnormal distributions Johnson transformation (transforms specification limits)

87 Nonnormal Capability Analysis

88 Capability for Multiple Variables Performs capability analysis for multiple variables or for a single variable with grouping Both normal and nonnormal All variables or groups being analyzed simultaneously must follow the same distribution

89 Capability for Multiple Variables Graph output Results are paneled on a single graph. Probability plots allow you to confirm the distribution fit.

90 Cause-and-Effect Diagram Open Cause and Effect.mtw. Labels are from the main branch. You can now enter data directly into the dialog box.

91 Cause-and-Effect Up to ten branches Sub-branches

92 Attribute Gage Study (Analytic) Calculates bias and repeatability of a measurement system when the response is a binary attribute variable Uses the Automotive Industry Action Group (AIAG) method, which has very specific requirements (20 trials per part ) Optional, more flexible, regression method

93 Attribute Gage Study (Analytic) Open AutoGage.mtw.

94 Attribute Gage Study (Analytic) Reject the hypothesis because the gage is biased. The gage performance curve shows the probability of acceptance for each reference value.

95 Partial Least Squares Regression A method for constructing predictive models when many factors are collinear Used by process and chemical industries Also helpful in manufacturing processes that measure a large number of outputs Useful when you have more predictors than observations (short and wide data sets)

96 Partial Least Squares Regression Open Partial Least Squares.mtw.

97 Partial Least Squares Regression Use cross-validation to select the components that maximize the model's predictive ability.

98 Partial Least Squares Regression Session window output The model is significant. MINITAB selected the two-component model as optimal.

99 Partial Least Squares Regression 15 built-in graphs covering model evaluation, residual analysis, and component evaluation

100 Design of Experiments (DOE) Enhancements: Analysis of variability for factorial designs Minor enhancements to design creation = new

101 Analysis of Variability Analyzes the standard deviation of repeat or replicate responses to identify factor settings that produce less variable results. Open Analyze Variability.mtw. Two-step process: 1. Analyze variability 2. Pre-process responses (prepares the data for analysis)

102 Analysis of Variability Pre-Process Responses creates columns for the standard deviation, number of replicates, and optionally, the average.

103 Analysis of Variability One approach to analyzing variability suggests using least squares regression to determine which factors are significantly related to the response. Once a reduced model is identified, use maximum likelihood estimation to determine the final model coefficients. Note: Once you fit a model to the standard deviations, the response is available to use in the response optimization.

104 Analysis of Variability Session window output

105 Analysis of Variability Effects plots

106 Reliability/Survival Analysis Reliability test plans Threshold parameter distributions Analysis of repairable systems Analysis of multiple failure modes

107 Reliability/Survival Analysis Reliability enhancements: Reliability test plans Demonstration Estimation Accelerated life Threshold parameter distributions Analysis of repairable systems Parametric Nonparametric Analysis of multiple failure modes Parametric Nonparametric

108 Reliability/Survival Analysis = new

109 Reliability Test Plans Three types of test plans: Demonstration test plans Determine the sample size or testing time needed to demonstrate that the reliability exceeds a given standard. Estimation test plans Estimate how many units to test and how long to test them in order to estimate percentiles or reliability with a specified degree of precision. Accelerated life Accelerated life test plans Extrapolate normal-condition failure times for highly reliable products from elevated stress-level testing. Determine the degree of precision with which to estimate information and the number of products to test.

110 Demonstration Test Plans Create a plan to show that at least 95% of our products will still be functional at 1,000 hours (with only 2 failures allowed during testing). From historical data, we know that failures are normally distributed with a standard deviation of 800 hours. Sample sizes we re considering

111 Demonstration Test Plans Output To demonstrate the reliability of the system, 100 parts can be tested for 1,082.8 hours. To shorten the amount of time needed for the test, consider testing 300 parts for hours.

112 Threshold Parameter Distributions Allows you to choose a distribution with a threshold parameter when you want to estimate the earliest time-to-failure Adds four new distributions to: Distribution ID Plot Distribution Overview Plot Parametric Distribution Analysis

113 Threshold Parameter Distributions Open Reliable.mtw.

114 Threshold Parameter Distributions New distribution plots:

115 Analysis of Repairable Systems Perform an analysis on a system of parts that have been repaired Determine when the cost or number of repairs suggests an overhaul of the system Includes parametric and nonparametric versions

116 Analysis of Repairable Systems Open Vseat.mtw.

117 Analysis of Repairable Systems Session window output An estimate of shape greater than 1 that indicates an increasing failure rate Tests whether enough evidence exists to say that the data come from populations with different shapes Tests for a trend in the data

118 Analysis of Repairable Systems Graph output Shows failures over time Used to determine whether your system is improving, deteriorating, or staying constant

119 Analysis of Multiple Failure Modes Assess the impact of failure modes on a given product Use parametric or nonparametric analysis for each failure mode Evaluate the overall product reliability and test the impact of removing particular failure modes

120 Analysis of Multiple Failure Modes Open Circuit.mtw. Assume a Weibull distribution by default but the failure mode Meter will be modeled with the logistic distribution.

121 Analysis of Multiple Failure Modes Session window output Parameter estimates Goodness-of-fit Characteristics of distribution (MTTF ) Table of percentiles Survival probabilities (optional through the Estimate button) Analysis for each failure mode Analysis of overall reliability

122 Analysis of Multiple Failure Modes Graph output

123 MINITAB Corporate Edition A premium version of Release 14 COM enabled Customized solutions MINITAB as an engine Create your own dialog boxes and MINITAB add-ins