introduction to using jmp®
DESCRIPTION
Yiming Peng Laboratory for Interdisciplinary Statistical Analysis Department of Statistics, Virginia Tech http://www.lisa.stat.vt.edu/ Nov, 2013. Introduction to Using JMP®. Outline. Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics - PowerPoint PPT PresentationTRANSCRIPT
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Introduction to Using JMP®Yiming Peng
Laboratory for Interdisciplinary Statistical AnalysisDepartment of Statistics, Virginia Techhttp://www.lisa.stat.vt.edu/
Nov, 2013
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Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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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 No need to write computer code
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JMP Download and Installation 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/
products/sas/jmp/index.html5
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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 variance, is recommended
Analytical features require statistical knowledge appropriate for the feature
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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
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Outline IntroductionGetting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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JMP Home Window (Windows Only)
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Tab + Alt to switch among different windows Ctrl + Q to quit
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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
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JMP Data Table This shows an empty data table with
no rows and one numeric column, labeled Column 1
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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
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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) 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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Import from Excel FilesFile > 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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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
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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
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Access Sample Data Tables
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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\10\Samples On Macintosh: \Library\Application Support\JMP\10\
Samples
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Outline Introduction Getting StartedManaging Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Data FilterExercise: 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 Over29
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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
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Outline Introduction Getting Started Managing DataVisualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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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
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HistogramsExercise: 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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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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Scatterplot 3D
Information Displayed on the Scatterplot 3D Report
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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
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Scatterplot 3D
Example of Grouped Normal Contour Ellipsoids
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Scatterplot 3D
If we select Nonpar Density Contour instead of Normal Contour Ellipsoids, we can create nonparametric density contours
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Variability Charts 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: 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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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)
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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
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Bubble Plots 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
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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Graph Builder
Exercise: Open SAT data Create a histogram for SAT Math
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Graph Builder
Exercise: Open SAT data Create a histogram for
SAT Math by Region
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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
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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
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Graph BuilderExercise: Car Physical Data
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Outline Introduction Getting Started Managing Data Visualizing DataCreating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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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
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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
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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
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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:
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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
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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
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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
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Tabulating DataExercise: Create descriptive
statistics for SAT Math by Region
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Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary StatisticsPerforming Basic Statistical
Analysis Saving and Exporting Results Resources
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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
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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
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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
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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 Pairs71
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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
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ANOVA 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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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
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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
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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
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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)
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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
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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
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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 > Normal Quantile Plot▪ Hold down Ctrl and click ▼ Red triangle menu
next to Age > Continuous Fit → Normal
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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 Line81
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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
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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
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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
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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
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Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical AnalysisSaving and Exporting Results Resources
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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
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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 As88
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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 89
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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
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Outline Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
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Resources Help menu
Indexes Tutorials Books – JMP documentations
▪ Discovering JMP▪ Using JMP▪ Basic Analysis and Graphing▪ DOE Guide
Sample Data
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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
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Resources On-line resources
http://www.lisa.stat.vt.edu/Welcome to LISA!
http://www.lisa.stat.vt.edu/?q=short_coursesLISA short courses
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References JMP Sample Data
Car Physical Data Fitness Iris SAT
JMP Documentation Using JMP Basic Analysis and Graphing
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Thank You
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