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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®Yiming Peng

Laboratory for Interdisciplinary Statistical AnalysisDepartment of Statistics, Virginia Techhttp://www.lisa.stat.vt.edu/

June, 2012

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

JMP Download and Installation JMP license information

All Virginia Tech researchers may download JMP free of charge by going to the Software Distribution Office's Network Software page and logging on using your PID and password▪ https://www.ita.vt.edu/Apps/WebObjects/NetSoftware

JMP 9 is available now for both Windows and Mac Unzip the JMP 9 file, click on the ‘setup’ icon, and

follow the instructions for installation

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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 variation, is recommended

Analytical features require statistical knowledge appropriate for the feature

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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JMP Home Window (Windows Only)

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Tab + Alt to switch among different windows Ctrl + Q to quit

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

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

Importing Data You can import many file formats into JMP by

default. For example: Comma-separated (.csv) .dat files that consist of text Microsoft Excel 1997–2003 (.xls) Plain text (.txt) SAS versions 6–9 on Windows (.sd2, .sd5, .sd7, .sas7bdat) SPSS files (.sav)

Other files, such as Microsoft Excel 2007 files, require specific Open Database Connectivity (ODBC)

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.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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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

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

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

Saving JMP Sessions

A saved session can help get you back to a previous state without having to manually re-open files and re-run analyses

Select File > Save By default, JMP asks whether you would

like to save the state of your session each time you exit the program Saving session upon exiting:

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

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

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

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

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

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

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

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

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

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

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 Over30

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

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

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

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

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

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

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 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

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

Variability Charts The variability charts are

used when we have multiple categorical x variables and one y variable

Select Graph > Variability/Gauge Chart

Exercise: Help > Sample data > Car

Physical Data Select Graph >

Variability/Gauge Chart Select Weight as Y, Response,

Country and Type as X, Grouping Click OK

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)

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

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)…

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

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 BuilderExercise: Car Physical Data

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

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

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

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:

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

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 DataExercise: Create descriptive

statistics for SAT Math by Region

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 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 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 > 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 → Normal81

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)

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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

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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

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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

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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 As95

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

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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▪ JMP 9 Quick Guide

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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 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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