2. Introduction to Using JMP®
Yiming Peng
Laboratory for Interdisciplinary Statistical Analysis
Department of Statistics, Virginia Tech
June, 2012

3. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

4. 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

5. JMP Download and Installation
JMP license information
All Virginia Tech researchers may download JMP free of charge by going to the Software Distribution Office's Network Software page and logging on using your PID and password
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

6. 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

7. JMP Terminology JMP platforms use these windows:
Launch windows where 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

8. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

9. JMP Home Window (Windows Only)
Tab + Alt to switch among different windows
Ctrl + Q to quit

10. 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

11. JMP Data Table
This shows an empty data table with no rows and one numeric column, labeled Column 1

12. Entering Data Manually: Construct a formula to calculate column values
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

13. 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)

14. 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

15. Data Table Panels There are three data table panels
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

16. 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

17. 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

18. 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:

19. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

20. 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…

21. 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

22. 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

23. 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

24. 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

25. 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

26. 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

27. 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

28. 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/Unexclude from 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/Unexclude again

29. 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

30. 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

31. 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

32. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

33. 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

34. 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

35. 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 X Axis 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 X Axis Specification window

36. 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

37. 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

38. 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

39. Scatterplots Interacting with the scatterplots
Suppose we are interested in all the points with both 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

40. 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.

41. 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

42. 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 Species and click Group
Click OK

43. 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

44. 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 Sepal length, Sepal width,
Petal length, and Petal width
and click Y, Columns
Click OK

45. Scatterplot 3D
Information Displayed on the Scatterplot 3D Report

46. 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

47. Scatterplot 3D
Example of Grouped Normal Contour Ellipsoids

48. Scatterplot 3D
If we select Nonpar Density Contour instead of Normal Contour Ellipsoids, we can create nonparametric density contours

49. 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 Weight as Y, Response, Country and Type as X, Grouping
Click OK

50. 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)

51. 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

52. 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)…

53. 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

54. Graph Builder
Exercise:
Open SAT data
Create a histogram for SAT Math

55. Graph Builder Exercise: Open SAT data Create a histogram for
SAT Math by Region

56. 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

57. Graph Builder Exercise: Interaction plot Open Car Physical Data
Select Graph > Graph Builder
Click, drag and drop Weight to Y
Click, drag and drop Type to X
Click, drag and drop Country to Overlay
Right click on the plot > Add > Line

58. Graph Builder
Exercise: Car Physical Data

59. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

60. 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

61. 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

62. Summarizing Columns Exercise: Create summary statistics for SAT Verbal
Open SAT data
Select Tables > Summary
Click SAT Verbal near upper left
Click Statistics button and choose Mean
Can choose any statistic
Can choose more than one statistic – click
Statistics again
Click OK

63. 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:

64. 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

65. 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

66. 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

67. Tabulating Data
Exercise: Create descriptive statistics for SAT Math by Region

68. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

69. 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

70. 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

71. 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
Prob >|t| is , there is not enough evidence to reject the null hypothesis

72. 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
Then perform the same procedures as for One-Sample t-Test
Or, select Analyze > Matched Pairs

73. 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 , there is not enough evidence to reject the null hypothesis

74. ANOVA
One-Way ANOVA with two groups – used when you compare the means of two populations
Same as Two-Sample t-Test

75. 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

76. 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 , this is strong evidence for concluding that at least one mean is statistically different from one of the other means

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 > 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

78. 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)

79. 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

80. 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
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

81. 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

82. 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
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

83. 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

84. 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

85. 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

86. 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 Age and Ctrl-click Runtime and RunPulse > Macro > Polynomial to Degree (2 is used here)

87. 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)

88. 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 = , 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

89. 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 = ), and LBI is significant (p-value < );
it appears that there is no difference among Drug types in the response LBS

90. ANCOVA Example 2: Sawblade – model with interaction Data: Notes:
Import Sawblade.xls file to JMP
Notes:
Fit a model to study the effect of blade material and blade speed on heat generation

91. 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 < , 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

92. 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

93. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

94. 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

95. 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

96. 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

97. 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

98. Outline Introduction Getting Started Managing Data Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources

99. Resources Help menu Indexes Tutorials Books – JMP documentations
Discovering JMP
Using JMP
Basic Analysis and Graphing
DOE Guide
Sample Data

100. Resources On-line resources http://www.jmp.com/about/events/webcasts/
for webcasts and recorded demos
check out Learning Library
JMP 9 Quick Guide

101. Resources On-line resources http://www.lisa.stat.vt.edu/
Welcome to LISA!
LISA short courses

102. References JMP Sample Data JMP Documentation
Car Physical Data
DrugLBI
Fitness
Iris
SAT
Saw Blade
JMP Documentation
Using JMP
Basic Analysis and Graphing
JMP® Software: ANOVA and Regression Course Notes

103. Thank You