Transcript www.lisa.stat.vt.edu

Yiming Peng
Laboratory for Interdisciplinary Statistical Analysis
Department of Statistics, Virginia Tech
http://www.lisa.stat.vt.edu/
Nov, 2013
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
3
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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
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No need to write computer code
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JMP license information
 All Virginia Tech researchers may Purchase
products directly from Software Distribution
Office at the Torgersen end of Torgersen Bridge.
 Price: 7$ + tax
 JMP 10 is available for both Windows and Mac
 http://www2.ita.vt.edu/software/student/product
s/sas/jmp/index.html
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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 variance, is recommended
 Analytical features require statistical knowledge
appropriate for the feature
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JMP platforms use these windows:
 Launch windows where you set up and run your
analysis
 Report windows showing the output of your analysis
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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
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
8
 Tab + Alt to switch among different windows
 Ctrl + Q to quit
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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
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This shows an empty data table with no rows
and one numeric column, labeled Column 1
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Manually:
 Move the cursor onto a cell, click in the cell and enter a
value
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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
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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)
 Microsoft Excel 2007,2010 files (.xlsx)
 Plain text (.txt)
 SAS versions 6–9 on Windows (.sd2, .sd5, .sd7, .sas7bdat)
 SPSS files (.sav)
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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.xlxs excel file in JMP
 In the Open Data File window, change ‘All JMP Files’ to
‘All Files’
 Copy and paste data in SAT.xlxs to a JMP data table
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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
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The modeling type of a variable can be one of the
following types, shown with its corresponding icon:
 Continuous
 Ordinal
 Nominal
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When you import data into JMP, it predicts which
modeling types to use
 Character data is considered nominal
 Numeric data is considered continuous
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To change the modeling type, click on the modeling
type icon next to the variable and make your selection
 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:
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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:
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On Windows: C:\Program Files\SAS\JMP\10\Samples
On Macintosh: \Library\Application Support\JMP\10\Samples
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
18
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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
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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
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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
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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
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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
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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
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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
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Selecting cells that contain specific values
 Select Rows > Row Selection > Select Where
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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
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To unhide rows or columns, you select
Hide/Unhide again
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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
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To un exclude rows or columns, you select
Exclude/Unexclude again
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
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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
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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
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
31
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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
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Select Analyze > Distribution
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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
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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
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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
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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
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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
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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
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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.
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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
Using the Scatterplot Matrix platform,
you can assess the relationships between
multiple variables simultaneously
 A scatterplot matrix is an ordered
collection of bivariate graphs
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 Select Graph > Scatterplot Matrix
 Select Analyze > Multivariate Methods >
Multivariate (continuous data only)
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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
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To make the groupings
stand out, you can:
 From the ▼ red triangle
menu, select Density
Ellipses
 From the ▼ red triangle
menu, select Shaded
Ellipses
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:
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 Help > Sample data > Iris
 Select Graph > Scatterplot 3D
 Select Sepal length, Sepal width,
Petal length, and Petal width
and click Y, Columns
 Click OK
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Information
Displayed on
the Scatterplot
3D Report
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Normal Contour Ellipsoids
Exercise: Grouped normal contour ellipsoids
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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
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Example of
Grouped
Normal Contour
Ellipsoids
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If we select
Nonpar Density
Contour instead
of Normal
Contour
Ellipsoids, we
can create
nonparametric
density
contours
The variability charts are
used when we have
multiple categorical x
variables and one y variable
 Select Analyze > Quality
and Process > Variability/
Attribute Gauge Chart
 Exercise:
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Help > Sample data > Car Physical
Data
Select Variability/ Attribute
Gauge Chart
Select Weight as Y, Response,
Country and Type as X, Grouping
Click OK
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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)
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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
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Bubble plots can produce dramatic visualizations
and readily show patterns and trends
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Select Graph > Bubble Plot
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Exercise:
 Open SAT data in JMP
 Graph > Bubble Plot
▪
▪
▪
▪
▪
▪
▪
▪
▪
Select SAT Verbal for Y
Select SAT Math for X
Select Region 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)…
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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
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Exercise:
 Open SAT data
 Create a histogram for SAT Math
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Exercise:
 Open SAT data
 Create a histogram for
SAT Math by Region
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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
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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
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Exercise: Car Physical Data
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
58
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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
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
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Exercise: Create summary statistics for SAT Verbal
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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
Use the Tables > Tabulate  Examples of summary tables:
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
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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
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
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 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
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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
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Exercise: Create descriptive statistics for SAT
Math by Region
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Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
67
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One variable (univariate)
 Distribution
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Two variables (bivariate)
 Fit Y by X
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More than two variable
 Fit Model
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More advanced features
 Modeling
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One-Sample t-Test
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Data: Help > Sample Data > Fitness
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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
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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
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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
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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
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One-Way ANOVA with two groups – used
when you compare the means of two
populations

Same as Two-Sample t-Test
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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
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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
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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
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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)
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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
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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
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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 >
Normal Quantile Plot
▪ Hold down Ctrl and click ▼ Red triangle menu next to Age >
Continuous Fit → Normal
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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
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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
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
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
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
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
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
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
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







Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
86

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
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
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
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
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
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 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
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







Introduction
Getting Started
Managing Data
Visualizing Data
Creating Summary Statistics
Performing Basic Statistical Analysis
Saving and Exporting Results
Resources
91

Help menu
 Indexes
 Tutorials
 Books – JMP documentations
▪
▪
▪
▪
Discovering JMP
Using JMP
Basic Analysis and Graphing
DOE Guide
 Sample Data
92

On-line resources
 http://www.jmp.com/about/events/webcasts/
for webcasts and recorded demos
 http://www.jmp.com/academic/
check out Learning Library
93

On-line resources
 http://www.lisa.stat.vt.edu/
Welcome to LISA!
 http://www.lisa.stat.vt.edu/?q=short_courses
LISA short courses
94

JMP Sample Data





Car Physical Data
Fitness
Iris
SAT
JMP Documentation
 Using JMP
 Basic Analysis and Graphing
95
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