Laboratory for Interdisciplinary Statistical Analysis
Collaboration:Visit our website to request personalized statistical advice and assistance with:
Designing Experiments • Analyzing Data • Interpreting ResultsGrant Proposals • Software (R, SAS, JMP, Minitab...)
LISA statistical collaborators aim to explain concepts in ways useful for your research.
Great advice right now: Meet with LISA before collecting your data.
All services are FREE for VT researchers. We assist with research—not class projects or homework.
LISA helps VT researchers benefit from the use of Statistics
www.lisa.stat.vt.edu
LISA also offers:
Educational Short Courses: Designed to help graduate students apply statistics in their researchWalk-In Consulting: M-F 1-3PM in 401 Hutcheson Hall and Wed. 1-3PM in the GLC for questions <30 mins
Introduction to Using JMP®Wandi Huang
Laboratory for Interdisciplinary Statistical AnalysisDepartment of Statistics, Virginia Techhttp://www.lisa.stat.vt.edu/
October, 2011
Outline
Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
3
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
About JMP®
JMP Download and Installation JMP license information
All Virginia Tech students 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▪ http://www2.ita.vt.edu/software/student/products/
sas/jmp/index.html 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
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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 variation, is recommended
Analytical features require statistical knowledge appropriate for the feature
Prerequisites
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
Outline
IntroductionGetting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
8
JMP Home Window (Windows Only)
9
Tab + Alt to switch among different windows Ctrl + Q to quit
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
JMP Data Table
JMP Data Table
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 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
Entering 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)
Importing Data
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
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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
Data Table Panels
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
JMP Modeling Types
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\9\Support Files <language>\Sample Data
On Macintosh: \Library\Application Support\JMP\9\<language>\Sample Data
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:
Saving JMP Sessions
Outline
Introduction Getting StartedManaging Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
19
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
Adding 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
Deleting Rows
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
Adding 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
Deleting Columns
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
Selecting/Deselecting Rows
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
Selecting/Deselecting Columns
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
Selecting Cells with Specific Values
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
Show/Hide 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
Include/Exclude Data
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
Data Filter
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
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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
32
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
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
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
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
Histograms
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
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
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
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.
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
Scatterplots
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
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
Scatterplot Matrix
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
Scatterplot 3D
Information Displayed on the Scatterplot 3D Report
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
Scatterplot 3D
Example of Grouped Normal Contour Ellipsoids
Scatterplot 3D
If we select Nonpar Density Contour instead of Normal Contour Ellipsoids, we can create nonparametric density contours
Scatterplot 3D
The variability charts are used when we have multiple categorical x variables and one y variable
Select Graph > Variability/Gauge Chart
Exercise: Help > Sample data > Car
Physical Data Select Graph >
Variability/Gauge Chart Select Weight as Y, Response,
Country and Type as X, Grouping Click OK
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)
Variability Charts
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
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)…
Bubble Plots
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
Graph Builder
Exercise: Open SAT data Create a histogram for SAT Math
Graph Builder
Exercise: Open SAT data Create a histogram for
SAT Math by Region
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
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
Graph Builder
Exercise: Car Physical Data
Graph Builder
Outline
Introduction Getting Started Managing Data Visualizing DataCreating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
59
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
Numerical Summaries of Data
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
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
Summarizing Columns
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
Tabulating Data
Examples of summary tables:
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
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
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
Tabulating Data
Exercise: Create descriptive statistics for SAT Math by Region
Tabulating Data
Outline
Introduction Getting Started Managing Data Visualizing Data Creating Summary StatisticsPerforming Basic Statistical
Analysis Saving and Exporting Results Resources
68
Types of Data Analysis
One variable (univariate) Distribution
Two variables (bivariate) Fit Y by X
More than two variable Fit Model
More advanced features Modeling
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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 Pairs72
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 Variances75
ANOVA
One-Way ANOVA with more than two groups Example: Car Physical Data (cont.) -
Residuals▪ ▼ Red triangle menu next to Oneway Analysis
of Weight by Country > Save > Save Residuals▪ Rename Weight centered by Country as residual▪ Select Analyze > Distribution > residual > Y,
Columns > OK▪ Select Continuous Fit > Normal > Goodness of
Fit▪ ▼ Red triangle menu next to Oneway Analysis
of Weight by Country > Means/ANOVA▪ Prob > F is 0.0001, this is strong evidence for
concluding that at least one mean is statistically different from one of the other means
76
ANOVA
One-Way ANOVA with more than two groups Example: Car Physical Data (cont.) –
Contrasts ▪ ▼ Red triangle menu next to Oneway Analysis
of Weight by Country > Compare Means > Each Pair Student’s t
▪ The diamonds for 1 and 2 overlap – they probably are not different; 2 and 3 do not overlap – probably different
▪ The circles cannot be interpreted unless you interact with them – select a comparison circle to highlight it
▪ ▼ Red triangle menu next to Comparisons for each pair using Student’s t > Different Matrix
▪ ▼ Red triangle menu next to Comparisons for each pair using Student’s t > Detailed Comparisons Report
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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 > 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
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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 Line82
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.) – Model selection
▪ Select Analyze > Fit Model▪ Select Oxy > Y▪ Select Age and Shift-click MaxPulse > Add▪ Select Oxy, Remove from Model Effects▪ Select Standard Least Squares > Stepwise▪ Run▪ Direction: Forward > Go▪ Run Model▪ Direction: Backward > Enter All > Go▪ Run Model
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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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ANCOVA
A model relating a categorical predictor and a continuous covariate to a single continuous response is known as an analysis of covariance (ANCOVA) model
ANOVA with categorical and continuous predictors First of all, need to identify if there is interaction
between predictors Example 1: DrugLBI – no interactions
Data: ▪ Help > Sample Data > DrugLBI
Notes: ▪ From Snedecor and Cockran, Statistical Methods, 1967▪ Use Fit Model with 'LBS' as response (Y), 'Drug' and 'LBI' as
effects (Xs)87
ANCOVA
Example 1: DrugLBI – no interactions▪ Select Analyze > Fit Model▪ Select LBS > Y▪ Select Drug, LBI > Macros > Full Factorial or
Factorial to Degree▪ Click Run▪ P-value for Drug*LBI = 0.5606, greater than 0.05,
indicating that Drug*LBI is not significant, thus can be removed from the model
▪ Examine the interaction in the Regression Plot:A linear regression line is drawn with a different color for each level of Drug. It may be difficult to interpret this graph for statistical significance of the interaction 88
ANCOVA
Example 1: DrugLBI – no interactions Re-do the analysis without including the
interaction term▪ Select Analyze > Fit Model▪ Select LBS > Y▪ Select Drug, LBI > Add▪ Click Run▪ Effect Tests report that Drug is not significant (p-
value = 0.1384), and LBI is significant (p-value < 0.0001);it appears that there is no difference among Drug types in the response LBS
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ANCOVA
Example 2: Sawblade – model with interaction Data:
▪ Import Sawblade.xls file to JMP Notes:
▪ Fit a model to study the effect of blade material and blade speed on heat generation
90
ANCOVA
Example 2: Sawblade – model with interaction▪ Select Analyze > Fit Model▪ Select Heat > Y▪ Select Material, Speed > Macros > Full Factorial or
Factorial to Degree▪ Click Run▪ p-value for the interaction term Material*Speed <
0.0001, which is significant▪ When there is a significant interaction, we cannot make
a conclusion about Material or Speed along; the effect of Material depends on the Speed of the blade
▪ To interpret the interaction, look at the Regression Plot:A linear regression line is drawn with a different color for each level of Material
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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
93
Saving Data Tables
You can save data tables in multiple formats: JMP data table (.jmp) SAS Transport File (.xpt) Excel File (.xls) Text File (.txt, .dat) etc.
Select File > Save As
94
Saving Reports
JMP saves reports in the following formats : JMP report (.jrp) Hypertext markup language (.htm, .html) Joint photographics expert group(.jpg) Microsoft Word (.doc) Portable Document Format (.pdf) Portable Network Graphics (.pgn) Text File (.txt) etc.
Select File > Save As
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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 96
Pasting Reports into Another Program
Exercise: Bring up any analysis in JMP
Press Alt and choose selection tool
Click on plot Copy (Ctrl + C) from JMP,
Paste (or Paste Special) into the desired program
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Outline
Introduction Getting Started Managing Data Visualizing Data Creating Summary Statistics Performing Basic Statistical Analysis Saving and Exporting Results Resources
98
Resources
Help menu Indexes Tutorials Books – JMP documentations
▪ Discovering JMP▪ Using JMP▪ Basic Analysis and Graphing▪ DOE Guide
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▪ JMP 9 Quick Guide
100
Resources
On-line resources http://www.lisa.stat.vt.edu/
Welcome to LISA! http://www.lisa.stat.vt.edu/?q=short_course
sLISA short courses
101
References
JMP Sample Data Car Physical Data DrugLBI Fitness Iris SAT Saw Blade
JMP Documentation Using JMP Basic Analysis and Graphing
JMP® Software: ANOVA and Regression Course Notes
102
Thank You
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