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1. YimingPeng Laboratory for Interdisciplinary Statistical Analysis Department of Statistics, Virginia Tech http://www.lisa.stat.vt.edu/ June, 2012

   Introduction to Using JMP®

2. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
3. About JMP® JMP was developed by SAS Institute Inc., Cary, NC Using JMP statistical software, you can Interact with your graphs and data to discover patterns and relationships in your data See how the data and the model work together to produce the statistics Perform statistical summary and analysis
4. JMP Download and Installation JMP license information All Virginia Tech researchers may download JMP free of chargeby going to the Software Distribution Office's Network Software page and logging on using your PID and password https://www.ita.vt.edu/Apps/WebObjects/NetSoftware JMP 9 is available now for both Windows and Mac Unzip the JMP 9 file, click on the ‘setup’ icon, and follow the instructions for installation
5. Prerequisites Before you begin using JMP, note the following information: You can use many JMP features, such as data manipulation, graphs, and scripting features, without any statistical knowledge A basic understanding of basic statistical concepts, such as mean and variation, is recommended Analytical features require statistical knowledge appropriate for the feature
6. JMP Terminology JMP platforms use these windows: Launch windowswhere you set up and run your analysis Report windows showing the output of your analysis Report windows normally contain the following items: A graph of some type (such as a scatterplot or a histogram) Specific reports that you can show or hide using the disclosure button Platform options that are located within red triangle menus
7. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
8. JMP Home Window (Windows Only) Tab + Alt to switch among different windows Ctrl + Q to quit
9. JMP Data Table You can enter, view, edit, and manage data using data tables In a data table, each variable is a column, and each observation is a row To create a new data table: Select File > New > Data Table Ctrl + N Click on the first icon below the File menu
10. JMP Data Table This shows an empty data table with no rows and one numeric column, labeled Column 1
11. Entering Data Manually: Move the cursor onto a cell, click in the cell and enter a value Construct a formula to calculate column values Open the formula editor by right-clicking the column name to which you want to apply the formula and selecting Formula… Or Double-click the column name to which you want to apply the formula, Column Properties > Formula > Edit Formula Select an empty formula element in the formula editing area by clicking it
12. Importing Data You can import many file formats into JMP by default. For example: Comma-separated (.csv) .dat files that consist of text Microsoft Excel 1997–2003 (.xls) Plain text (.txt) SAS versions 6–9 on Windows (.sd2, .sd5, .sd7, .sas7bdat) SPSS files (.sav) Other files, such as Microsoft Excel 2007 files, require specific Open Database Connectivity (ODBC)
13. Import from Excel Files File > Open or Ctrl + O or Or, select all data in the excel spreadsheet, copy, switch to JMP, create a new data table, Edit > Paste with Column Names Exercise: Open the SAT.xls excel file in JMP In the Open Data File window, change ‘All JMP Files’ to ‘All Files’ Copy and paste data in SAT.xls to a JMP data table
14. Data Table Panels There are three data table panels Table panel Columns panel Rows panel The data table panels are arranged to the left of the data grid These panels contain information about the table and its contents
15. JMP Modeling Types The modeling type of a variable can be one of the following types, shown with its corresponding icon: Continuous Ordinal Nominal When you import data into JMP, it predicts which modeling types to use Character data is considered nominal Numeric data is considered continuous To change the modeling type, click on the modeling type icon next to the variable and make your selection
16. Access Sample Data Tables All of the examples in the JMP documentation suite use sample data. To access JMP’s sample data tables, Select Help > Sample Data.From here, you can: Open the sample data directory Open an alphabetized list of all sample data tables Search for a sample data table within a category Alternatively, the sample data tables are installed in the following directory: On Windows: C:\Program Files\SAS\JMP\9\Support Files <language>\Sample Data On Macintosh: \Library\Application Support\JMP\9\<language>\Sample Data
17. Saving JMP Sessions A saved session can help get you back to a previous state without having to manually re-open files and re-run analyses Select File > Save By default, JMP asks whether you would like to save the state of your session each time you exit the program Saving session upon exiting:
18. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
19. Adding Rows To add one or multiple new empty rows, you can take one of the following actions: Select Rows > Add Rows Double-click an empty row number area below the last row to add that many empty rows Double-click the gray lower triangular area in the upper left corner of the data grid. In the Add Rows… window, Enter the number of rows to add Specify where you would like to add them Right-click in an empty row below the last row, and select Add Rows… Enter the number of rows to add
20. Deleting Rows To delete rows from the data grid, you can do one of the following: Highlight the rows that you want to delete, then select Rows > Delete Rows Right-click on the row numbers and select Delete Rows
21. Adding Columns To add one or multiple new empty columns, you can take one of the following actions: Select Cols > New Column Double-click the empty space to the right of the last data table column Select Cols > Add Multiple Cols… (or double-click the gray upper triangular area in the upper left corner of the data grid). In the Add Multiple Cols… window, Enter the number of columns to add Specify if they are to be grouped Select a data type Enter their location Select the initial data values
22. Deleting Columns To delete columns from the data grid, you can do one of the following: Highlight the columns that you want to delete, then select Cols > Delete Columns Right-click on the column numbers and select Delete Columns
23. Selecting/Deselecting Rows Select or deselect rows: Select Rows > Row Selection > Go to Row… to select a certain row number Select Rows > Row Selection > Select All Rows Select Rows > Clear Row States Hold down Shift and click the gray lower triangular area in the upper left corner of the data grid to select all rows. Click again to deselect To clear all highlights in the data table, press the ESC key on your keyboard
24. Selecting/Deselecting Columns Select or deselect columns: Select Cols> Go … to select a certain column number or name Hold down Shift and click the gray upper triangular area in the upper left corner of the data grid to select all columns. Click again to deselect To clear all highlights in the data table, press the ESC key on your keyboard
25. Selecting Cells with Specific Values Selecting cells that match the currently highlighted cell Highlight the cells that contain the value(s) that you want to locate Select Rows > Row Selection > Select Matching Cells Selecting cells that contain specific values Select Rows > Row Selection > Select Where
26. Show/Hide Data You suppress (hide) rows and columns so they are included in analyses but do not appear in plots and graphs. To do so, you Select Hide/Unhide from the Rows menu or Cols menu A mask icon appears beside the hidden row number or the column name, indicating that the row or column is hidden To unhide rows or columns, you select Hide/Unhide again
27. Include/Exclude Data You can exclude data from calculations in analyses. For most platforms, excluded data are not hidden in plots. To do so, you Select Exclude/Unexcludefrom the Rows menu or Cols menu A circle with a strikethrough appears beside either the row number or the column name, indicating that the row or column is excluded and not analyzed To un exclude rows or columns, you select Exclude/Unexcludeagain
28. Data Filter The Data Filter gives you a variety of ways to identify subsets of data Using Data Filter commands and options, you interactively select complex subsets of data, hide these subsets in plots, or exclude them from analyses Select Rows > Data Filter
29. Data Filter Exercise: Select data for Virginia Open SAT data in JMP Select Rows > Data Filter Select State and click Add Let’s check Select for Virginia Can also check Show or Include De-select? Click Clear Choose another variable? Click Start Over
30. Data Filter To select/show/include continuous variables such as time or weight, Use sliders to control selection Drag the end sliders to select the range you want Need specific end points? Click on those values
31. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
32. Histograms Histograms visually display the distribution of your data For categorical (nominal or ordinal) variables, the histogram shows a bar for each level of the ordinal or nominal variable For continuous variables, the histogram shows a bar for grouped values of the continuous variable Select Analyze > Distribution
33. Histograms Exercise: Create a histogram for SAT Math Open SAT data in JMP Select Analyze > Distribution In the Select Columns box, select SAT Math > Y, Columns, then click on OK
34. Histograms Interacting with the histogram Change the orientation: Click on the ▼ red triangle menu> Histogram Options > Vertical Display the count of within each bar: Click on the ▼ red triangle menu> Histogram Options > Show Counts Rescaling the axis (continuous variables only): Click and drag on an axis to rescale it Hover over the axis until you see a hand, double-click on the axis and set the parameters in the XAxis Specification window Resizing histogram bars (continuous variables only): Click on the ▼ red triangle menu> Histogram Options > Set Bin Width Hover over the axis until you see a hand, double-click on the axis and set the increment in the XAxis Specification window
35. Histograms Interacting with the histogram Clicking on a histogram bar highlights the bar and selects the corresponding rows in the data table The appropriate portions of all other graphical displays also highlight the selection
36. Scatterplots Select Analyze > Fit Y by X Exercise: Plot SAT Verbal vs. SAT Math Select Analyze >Fit Y by X Click SAT Verbal in Select Columns box > Y, Response Click SAT Math in Select Columns box > X, Factor button Click OK
37. Scatterplots Interacting with the scatterplots Suppose we are interested in the points with both SAT Math and SAT Verbal greater than 600 Point at this point and click on it The point gets highlighted The corresponding row (row 274) is also highlighted in the data table
38. Scatterplots Interacting with the scatterplots Suppose we are interested in all the points withboth SAT Math and SAT Math > 580 Shift-click on all the points that satisfied this condition Or, drag a box over all these points To deselect, Ctrl-click
39. Scatterplots Interacting with the scatterplots Color the selected points red and change the symbol to an empty circle Right click on the scatterplot Row Colors Row Markers etc.
40. Scatterplots Interacting with the scatterplots Suppose those highlighted points are considered as ‘outliers’ and need to be removed from the plot (or the analysis) Right click on the scatterplot Row Hide Row Exclude ▼ Red triangle menu> Script > Redo Analysis to update the plot
41. Scatterplot Matrix Using the Scatterplot Matrix platform, you can assess the relationships between multiple variables simultaneously A scatterplot matrix is an ordered collection of bivariate graphs Select Graph > Scatterplot Matrix Select Analyze > Multivariate Methods > Multivariate (continuous data only) Exercise: Help > Sample data > Iris Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns Select Speciesand click Group Click OK
42. Scatterplot Matrix To make the groupings stand out, you can: From the ▼red triangle menu, select Density Ellipses From the ▼ red triangle menu, select Shaded Ellipses
43. Scatterplot 3D The Scatterplot 3D platform shows the values of numeric columns in the associated data table in a rotatable, 3D view Select Graph > Scatterplot 3D Exercise: Help > Sample data > Iris Select Graph > Scatterplot 3D Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns Click OK
44. Scatterplot 3D Information Displayed on the Scatterplot 3D Report
45. Scatterplot 3D Normal Contour Ellipsoids Exercise: Grouped normal contour ellipsoids The ellipsoids cover 75% of the data points and are 50% transparent The contours are color-coded based on species Help > Sample data > Iris Select Graph > Scatterplot 3D Select Sepal length, Sepal width, Petal length, and Petal width and click Y, Columns Click OK ▼ Red triangle menu > Normal Contour Ellipsoids Select Grouped by Column Select Species Type 0.75 next to Coverage Type 0.5 next to Transparency Click OK
46. Scatterplot 3D Example of Grouped Normal Contour Ellipsoids
47. Scatterplot 3D If we select Nonpar Density Contour instead of Normal Contour Ellipsoids, we can create nonparametric density contours
48. Variability Charts The variability charts are used when we have multiple categorical x variables and one y variable Select Graph > Variability/Gauge Chart Exercise: Help > Sample data > Car Physical Data Select Graph > Variability/Gauge Chart Select Weight as Y, Response,Country and Type as X, Grouping Click OK
49. Variability Charts From the ▼ red triangle menu, you can Connect Cell Means (blue lines are added) Uncheck Show Range Bars (easier to see points) Show Group Means (purple lines are added)
50. Bubble Plots A bubble plot is a scatter plot that represents its points as circles, or bubbles. You can use bubble plots to: dynamically animate bubbles using a time variable, to see patterns and movement across time use size and color to clearly distinguish between different variables Bubble plots can produce dramatic visualizations and readily show patterns and trends Select Graph > Bubble Plot
51. Bubble Plots Exercise: Open SAT data in JMP Graph > Bubble Plot Select SAT Verbal for Y Select SAT Math for X Select Region, State for ID Select Year for Time Select SAT % Taking (2004) for Sizes Select ACT % Taking (2004) for Coloring Click OK Click on one bubble > ▼ red triangle menu > Trail Lines ▼ Red triangle menu >Save for Adobe Flash platform (.SWF)…
52. Graph Builder Graph Builder provides a platform where you can interactively create and modify graphs Graph types include points, lines, bars, histograms, etc. It allows you to explore relationships between several variables on the same graph Select Graph > Graph Builder
53. Graph Builder Exercise: Open SAT data Create a histogram for SAT Math
54. Graph Builder Exercise: Open SAT data Create a histogram for SAT Math by Region
55. Graph Builder Exercise: Open SAT data Create a histogram for SAT Verbal by Region Drag SAT Verbal and drop it on top of SAT Math Where to drop matters
56. Graph Builder Exercise: Interaction plot Open Car Physical Data Select Graph > Graph Builder Click, drag and drop Weightto Y Click, drag and drop Typeto X Click, drag and drop Country to Overlay Right click on the plot > Add > Line
57. Graph Builder Exercise: Car Physical Data
58. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
59. Numerical Summaries of Data To general numerical summaries of data, you can: Create a table that contains columns of summary statistics Tabulate data so it is displayed in a tabular format
60. Summarizing Columns The Tables > Summary command calculates various summary statistics, including the mean and median, standard deviation, minimum and maximum value, etc. Select Tables > Summary Select the columns you want to summarize in the Select Columns box A new data table is created to store all the summary statistics requested but it is not saved when you close it unless you select File > Save As to give it a name and location
61. Summarizing Columns Exercise:Create summary statistics for SAT Verbal Open SAT data Select Tables > Summary Click SAT Verbal near upper left Click Statistics buttonand choose Mean Can choose any statistic Can choose more thanone statistic – click Statistics again Click OK
62. Tabulating Data Use the Tables > Tabulate command for constructing tables of descriptive statistics The tables are built from grouping columns, analysis columns, and statistics keywords Through its interactive interface for defining and modifying tables, the Tabulate command provides a powerful and flexible way to present summary data in tabular form Examples of summary tables:
63. Tabulating Data To create a summary table using the Tabulate command is an iterative process: Click and drag the items (column name from the column list or statistics from the keywords list) from the appropriate list Drop the items on the dimension (row table or column table) where you want to place the items’ labels After creating a table, add to it by repeating the above process
64. Tabulating Data When you drag and drop a variable, JMP populates the table automatically for it if its role is obvious, such as keywords or character columns Otherwise, a popup menu lets you choose the role for the variable Add Grouping Columns –if you want to use the variables to categorize the data. For multiple grouping columns, Tabulate creates a hierarchical nesting of the variable Add Analysis Columns – if you want to compute the statistics of these columns
65. Tabulating Data Exercise: Create descriptive statistics for SAT Math by Region Open SAT data Select Tables > Tabulate Click Region and drag and drop it into the Drop zone for columns Select Add Grouping Columns Click Mean and drag and drop it into the first blank cell on the third row Click Std Dev and drag and drop it just below Mean
66. Tabulating Data Exercise: Create descriptive statistics for SAT Math by Region
67. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
68. Types of Data Analysis One variable (univariate) Distribution Two variables (bivariate) Fit Y by X More than two variable Fit Model More advanced features Modeling
69. Comparing Means One-Sample t-Test Data: Help > Sample Data > Fitness Linneruds Fitness data: fitting oxygen uptake to exercise and other variables. The original is in Rawlings (1988), but certain values of MaxPulse and RunPulse were changed for illustration. Names and Sex columns were contrived for illustration
70. Comparing Means One-Sample t-Test Example: Fitness Select Analyze > Distribution Select RunPulse > Y, Columns Click OK ▼ Red triangle menu next to RunPulse > Normal Quantile Plot ▼ Red triangle menu next to RunPulse > Continuous Fit >Normal ▼ Red triangle menu next to Fitted Normal > Goodness of Fit ▼ Red triangle menu next to RunPulse > Test Mean Enter 170 for Specify Hypothesized Mean to test if RunPulse equals 170 Click OK Prob >|t| is 0.8485, there is not enough evidence to reject the null hypothesis
71. Comparing Means Paired t-Test – used when you have two related measurements Create a new column for ‘difference’ Select Cols > New Column Type Difference in the Column Name box Select Cols > Formula Select col 1 Select the subtraction sign Select col 2 Click OK Click OK Then perform the same procedures as for One-Sample t-Test Or, select Analyze > Matched Pairs
72. Comparing Means Two-Sample t-Test – used when you compare the means of two populations Example: Fitness Select Analyze > Fit Y by X Choose Sex > X, Factor Choose RunPulse > Y, Response Click OK ▼ Red triangle menu next to Oneway Analysis of RunPulse by Sex>Normal Quantile Plot ▼ Red triangle menu next to Oneway Analysis of RunPulse by Sex >UnEqual Variances ▼ Red triangle menu next to Oneway Analysis of RunPulse by Sex >Means/Anova/Pooled t (for unequal variance select t-test) Prob >|t| is 0.1835, there is not enough evidence to reject the null hypothesis
73. ANOVA One-Way ANOVA with two groups – used when you compare the means of two populations Same as Two-Sample t-Test
74. ANOVA One-Way ANOVA with more than two groups – used when you compare the means of more than two populations Example: Help > Sample Data > Car Physical Data Select Analyze > Fit Y by X Select Country > X, Factor Select Weight > Y, Response Click OK ▼ Red triangle menu next to Oneway Analysis of Weight by Country > Normal Quantile Plot ▼ Red triangle menu next to Oneway Analysis of Weight by Country >UnEqual Variances
75. ANOVA One-Way ANOVA with more than two groups Example: Car Physical Data (cont.) - Residuals ▼ Red triangle menu next to Oneway Analysis of Weight by Country > Save > Save Residuals Rename Weight centered by Country as residual Select Analyze > Distribution > residual > Y, Columns > OK Select Continuous Fit > Normal > Goodness of Fit ▼ Red triangle menu next to Oneway Analysis of Weight by Country> Means/ANOVA Prob > F is 0.0001, this is strong evidence for concluding that at least one mean is statistically different from one of the other means
76. ANOVA One-Way ANOVA with more than two groups Example: Car Physical Data (cont.) – Contrasts ▼ Red triangle menu next to Oneway Analysis of Weight by Country > Compare Means > Each Pair Student’s t The diamonds for 1 and 2 overlap – they probably are not different; 2 and 3 do not overlap – probably different The circles cannot be interpreted unless you interact with them – select a comparison circle to highlight it ▼ Red triangle menu next to Comparisons for each pair using Student’s t > Different Matrix ▼ Red triangle menu next to Comparisons for each pair using Student’s t > Detailed Comparisons Report
77. ANOVA One-Way ANOVA with more than two groups Example: Car Physical Data (cont.) – Contrasts ▼ Red triangle menu next to Oneway Analysis of Weight by Country > Compare Means > All Pairs, Tukey HSD Use this test to control the experimentwise error rate at the significance level α (e.g. α=0.05)
78. ANOVA N-Way ANOVA – used when there are more than one categorical factor Example: Car Physical Data Select Analyze > Fit Model Select Weight > Y Select Country, Type > Macros > Full Factorial Click Run ▼ Red triangle menu next to the response > Factor Profiling >Interaction Plots ▼ Red triangle menu next to the two-way interaction > LSMeans Plot p-values for the interactions is smaller than 0.05; not all the lines in interaction plots are parallel – conclude there is a significant interaction between the factors
79. ANOVA N-Way ANOVA Example: Car Physical Data – Contrasts ▼ Red triangle menu next to Country*Type > LSMeans Contrast Select the plus sign for USA, Compact; the minus sign for USA, Sporty > Done Prob > F is 0.03 – A US made sporty car is heavier than a US made compact car ▼ Red triangle menu next to Country*Type > LSMeans Contrast Select the plus sign for Japan, Sporty; the minus sign for USA, Sporty > Done Prob > F is 0.01 – A US made sporty car is heavier than a Japan made sporty car
80. Regression Simple Linear Regression – used to assess the significance of the predictor in explaining the variability in the response Example: Help > Sample Data > Fitness Select Analyze > Distribution Select Age, Shift-click MaxPlus > Y, Columns > OK Hold down Ctrl and click▼ Red triangle menu next to Age > Display Options> More Moments Hold down Ctrl and click▼ Red triangle menu next to Age > Normal Quantile Plot Hold down Ctrl and click▼ Red triangle menu next to Age > Continuous Fit → Normal
81. Regression Simple Linear Regression Example: Fitness (cont.) Select Analyze > Fit Y by X Select Oxy > Y, Response Select Age and hold down Shift and click MaxPulse > X, Factor Click OK Select Oxy, Remove from X, Factor Click OK Hold down Ctrl and click▼ Red triangle menu next to Bivariate Fit of Oxy By Age > Density Ellipse > 0.95 Hold down Ctrl and click ▼ Red triangle menu next to Bivariate Fit of Oxy By Age > Fit Mean Hold down Ctrl and click ▼ Red triangle menu next to Bivariate Fit of Oxy By Age > Fit Line
82. Regression Multiple Linear Regression – used to model the relationship between many continuous predictors and a single continuous response Example: Help > Sample Data > Fitness Select Analyze > Fit Model Select Oxy > Y Select Age and Shift-click MaxPulse > Add Select Oxy, Remove from Model Effects Run ▼ Red triangle menu next to Response Oxy > Save Columns > Residuals Rename Residual Oxy as residual Select Analyze > Distribution > residual > Y, Columns > OK Select Continuous Fit > Normal > Goodness of Fit
83. Regression Multiple Linear Regression Example: Fitness (cont.) – Model selection ▼ Red triangle menu next to Response Oxy > Model Dialog Select RstPulse from the Model Effects list and select Remove Run Select Weight from the Model Effects list and select Remove Run
84. Regression Multiple Linear Regression Example: Fitness (cont.) – Model selection Select Analyze > Fit Model Select Oxy > Y Select Age and Shift-click MaxPulse > Add Select Oxy, Remove from Model Effects Select Standard Least Squares > Stepwise Run Direction: Forward > Go Run Model Direction: Backward > Enter All > Go Run Model
85. Regression Multiple Linear Regression Example: Fitness (cont.) – Add interaction and higher order terms Select Analyze > Fit Model Select Oxy > Y Select Age and Ctrl-click Runtime and RunPulse > Macro > Factorial to degree (2 is used here) Run Select Analyze > Fit Model Select Oxy > Y Select Age and Ctrl-click Runtime and RunPulse > Macro > Polynomial to Degree (2 is used here) Run
86. ANCOVA A model relating a categorical predictor and a continuous covariate to a single continuous response is known as an analysis of covariance (ANCOVA) model ANOVA with categorical and continuous predictors First of all, need to identify if there is interaction between predictors Example 1: DrugLBI – no interactions Data: Help > Sample Data > DrugLBI Notes: From Snedecor and Cockran, Statistical Methods, 1967 Use Fit Model with 'LBS' as response (Y), 'Drug' and 'LBI' as effects (Xs)
87. ANCOVA Example 1: DrugLBI – no interactions Select Analyze > Fit Model Select LBS > Y Select Drug, LBI > Macros > Full Factorial or Factorial to Degree Click Run P-value for Drug*LBI = 0.5606, greater than 0.05, indicating that Drug*LBI is not significant, thus can be removed from the model Examine the interaction in the Regression Plot: A linear regression line is drawn with a different color for each level of Drug. It may be difficult to interpret this graph for statistical significance of the interaction
88. ANCOVA Example 1: DrugLBI – no interactions Re-do the analysis without including the interaction term Select Analyze > Fit Model Select LBS > Y Select Drug, LBI > Add Click Run Effect Tests report that Drug is not significant (p-value = 0.1384), and LBI is significant (p-value < 0.0001); it appears that there is no difference among Drug types in the response LBS
89. ANCOVA Example 2: Sawblade – model with interaction Data: Import Sawblade.xls file to JMP Notes: Fit a model to study the effect of blade material and blade speed on heat generation
90. ANCOVA Example 2: Sawblade – model with interaction Select Analyze > Fit Model Select Heat > Y Select Material, Speed > Macros > Full Factorial or Factorial to Degree Click Run p-value for the interaction term Material*Speed < 0.0001, which is significant When there is a significant interaction, we cannot make a conclusion about Material or Speed along; the effect of Material depends on the Speed of the blade To interpret the interaction, look at the Regression Plot: A linear regression line is drawn with a different color for each level of Material
91. Saving Analyses to Data Table To re-produce the previous analysis when you re-open the data table, you can: ▼ Red triangle menu >Script > Save Script to Data Table Re-produce the analysis from Data Table by▼ Red triangle menu > Run Script
92. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
93. Saving Data Tables You can save data tables in multiple formats: JMP data table (.jmp) SAS Transport File (.xpt) Excel File (.xls) Text File (.txt, .dat) etc. Select File > Save As
94. Saving Reports JMP saves reports in the following formats : JMP report (.jrp) Hypertext markup language (.htm, .html) Joint photographics expert group(.jpg) Microsoft Word (.doc) Portable Document Format (.pdf) Portable Network Graphics (.pgn) Text File (.txt) etc. Select File > Save As
95. Pasting Reports into Another Program When you need to use JMP reports or data tables in another program, you can copy and paste parts of it into the document, such as Microsoft Word or PowerPoint file. Click the selection tool Click and drag (or hold down Shift and click) to select items in a report window or data table Click the selected items and drag them from JMP to the other program Or, copy the selected items in JMP and paste them into the other program Note: To copy all text (no graphs) from the active report window as unformatted text, select Edit > Copy As Text To copy only the graph (no text), right-click the graph and select Edit > Copy Picture
96. Pasting Reports into Another Program Exercise: Bring up any analysis in JMP Press Alt and choose selection tool Click on plot Copy (Ctrl+C) from JMP, Paste (or Paste Special) into the desired program
97. Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
98. Resources Help menu Indexes Tutorials Books – JMP documentations Discovering JMP Using JMP Basic Analysis and Graphing DOE Guide SampleData
99. Resources On-line resources http://www.jmp.com/about/events/webcasts/ for webcasts and recorded demos http://www.jmp.com/academic/ check out Learning Library JMP 9 Quick Guide
100. Resources On-line resources http://www.lisa.stat.vt.edu/ Welcome to LISA! http://www.lisa.stat.vt.edu/?q=short_courses LISA short courses
101. References JMP Sample Data Car Physical Data DrugLBI Fitness Iris SAT Saw Blade JMP Documentation Using JMP Basic Analysis and Graphing JMP® Software: ANOVA and Regression Course Notes

102. Thank You