Intro to JMP.pdf - LISA - Virginia Tech

Laboratory for InterdisciplinaryStatistical AnalysisLISA helps VT researchers benefit from the use of StatisticsCollaboration: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.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

• JMP license information• All Virginia Tech students may download JMP freeof chargeby going gto the Software DistributionOffice's Network Software page and logging onusing your PID and password▪ http://www2.ita.vt.edu/software/student/products/sas/jt / / t d t/ d t / /jmp/index.html• JMP 9 is available now for both Windows and Mac• Unzip the JMP 9 file, click on the ‘setup’ icon, andfollow the instructions for installation• Before you begin using JMP, note thefollowing information:• You can use many JMP features, such as datamanipulation, graphs, and scripting features,without t any statistical ttiti knowledge• A basic understanding of basic statistical concepts,such as mean and variation, is recommended• Analytical features require statistical knowledgeappropriate for the feature5• JMP platforms ltf use these windows:• Launch windows where you set up and run youranalysis• Report windows showing the output of your analysis• Report windows normally contain the followingitems:• A graph of some type (such as a scatterplot or ahistogram)• Specific reports that you can show or hide using thedisclosure button• Platform options that are located within red trianglemenus• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources8

• Tab + Alt to switch among different windows• Ctrl + Q to quit• You can enter, view, edit, and manage datausing data tables• In a data table, each variable is a column, andeach 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 menu9• This shows an empty data table with no rowsand one numeric column, labeled Column 1• Manually:• Move the cursor onto a cell, click in the cell and enter avalue• Construct a formula to calculate column values• Open the formula editor by right‐clicking the columnname to which you want to apply the formula andselecting Formula…• Or Double‐click the column name to which you want toapply the formula, Column Properties > Formula >Edit Formula• Select an empty formula element in the formula editingarea by clicking it11

• You can import many file formats into JMP bydefault. For example:• Comma‐separated (.csv)• .dat files that consist of text• Microsoft Excel 1997–2003 (.xls)• Plain text t (.txt)t)• 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)• 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 table14• There are three datatable panels• Table panel• Columns panel• Rows panel• The data tablepanels are arrangedto the left of thedata grid• These panelscontain informationabout the table andits contents• The modeling dli type of a variable ibl can be one of thefollowing types, shown with its corresponding icon:• Continuous• Ordinal• Nominal• When you import data into JMP, it predicts whichhmodeling types to use• Character data is considered nominal• Numeric data is considered continuous• To change the modeling type, click on the modelingtype icon next to the variable and make your selection

• All of the examples in the JMP documentation ti suiteuse 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 thefollowing directory:• On Windows: C:\Program Files\SAS\JMP\9\Support Files\Sample Data• On Macintosh: \Library\ApplicationSupport\JMP\9\\Sample Data• A saved session can help get you back to aprevious state without having to manually reflfiles and re‐run analyses• Select File > Saveopen• By df default, JMP asks whether h you would like lk tosave the state of your session each time youexit the program• Saving session upon exiting:17• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources• To add one or multiple l new empty rows, you cantake one of the following actions:• Select Rows > Add Rows• Double‐click an empty row number area below the lastrow to add that many empty rows• Double‐click the gray lower triangular area in the upperleft 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, andselect Add Rows…▪ Enter the number of rows to add19

• To delete rows from the data grid, you can doone of the following:• Highlight the rows that you want to delete, thenselect Rows > Delete Rows• Right‐click on the row numbers and select DeleteRows• To add one or multiple l new empty columns, youcan take one of the following actions:• Select Cols > New Column• Double‐click the empty space to the right of the lastdata table column• Select Cols > Add Multiple Cols… (or double‐click thegray upper triangular area in the upper left corner ofthe 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• To delete columns from the data grid, you cando one of the following:• Highlight the columns that you want to delete, thenselect Cols > Delete Columns• Right‐click on the column numbers and selectDelete Columns• Select Sl or deselect rows:• Select Rows > Row Selection > Go to Row… toselect a certain row number• Select Rows > Row Selection > Select All RowsSelect Rows > Clear Row States• Hold down Shift and click the gray lower triangulararea in the upper left corner of the data grid toselect all rows. Click again to deselect• To clear all highlights in the data table, press theESC key on your keyboard

• Select or deselect columns:• Select Cols> Go … to select a certain columnnumber or name• Hold down Shift and click the gray upper triangulararea in the upper left corner of the data grid toselect all columns. Click again to deselect• To clear all highlights hli ht in the data table, press theESC key on your keyboard• Selecting cells that match the currentlyhighlighted cell• Highlight the cells that contain the value(s) that youwant to locate• Select Rows > Row Selection > Select MatchingCells• Sl Selecting cells that t contain ti specific values• Select Rows > Row Selection > Select Where• You suppress (hide) rows and columns so theyare included in analyses but do not appear inplots and graphs. To do so, you• Select Hide/Unhide from the Rows menu or Colsmenu• A mask icon appears beside the hidden rownumber or the column name, indicating that therow or column is hidden• To unhide rows or columns, you selectHide/Unhide again• You can exclude data from calculations l inanalyses. For most platforms, excluded data arenot hidden in plots. To do so, you• Select Exclude/Unexclude from the Rows menu orCols menu• A circle with a strikethrough appears beside eitherthe row number or the ecolumn name, indicating dcat gthatthe row or column is excluded and not analyzed• To un exclude rows or columns, you selectExclude/Unexclude again

• The Data Filter gives you avariety of ways to identifysubsets of data• Using Data Filter commandsand options, you interactivelyselect complex subsets ofdata, hide these subsets inplots, or exclude them fromanalyses• Select Rows > Data Filter• Exercise: Select data for Virginia• Open SAT data in JMP• Select Rows > Data Filter• Select State and click Add• Lt’ Let’s check Select tf for Virginiai i• Can also check Show or Include• De‐select? Click Clear• Choose another variable?Click Start Over30• To select/show/include continuous variablessuch 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• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources3132

• Histograms visually display the distribution ofyour data• For categorical (nominal or ordinal) variables, thehistogram shows a bar for each level of the ordinalor nominal variable ibl• For continuous variables, the histogram shows abar for grouped values of the continuous variable• Select Analyze > Distribution• Exercise: Create a histogram for SAT Math• Open SAT data in JMP• Select Analyze > Distributionib i• In the Select Columns box, select SAT Math > Y,Columns, then click on OK• 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 setthe parameters in the XAxis Specification window• Resizing i histogram bars (continuous variables ibl only): l)▪ Click on the ▼ red triangle menu > Histogram Options > Set BinWidth▪ Hover over the axis until you see a hand, double‐click on the axis and setthe increment in the XAxis Specification window• Interacting with thehistogram• Clicking on a histogrambar highlights the barand selects thecorresponding rows inthe data table• The appropriateportions of all othergraphical displays alsohighlight the selection

• Select Analyze > Fit Y by X• Exercise:Plot SAT Verbal vs. SAT Math• Select Analyze >Fit Y by X• Click SAT Verbal in SelectColumns box > Y, Response• Click SAT Math in SelectColumns box > X, Factor button• Click OK• Interacting with thescatterplots• Suppose we are interested inthe points with both SAT Mathand SAT Verbal greater than600▪ Point at this point and click on it▪ The point gets highlighted▪ The corresponding row (row 274)is also highlighted hli h in the datatable• Interacting with thescatterplots• Suppose we are interested inall the points with both SATMath and SAT Math > 580▪ Shift‐click on all the points thatsatisfied this condition• Or, drag a box over all thesepoints▪ To deselect, Ctrl‐click• Interacting with thescatterplots• Color the selected points redand change the symbol to anempty circle▪ Right click on the scatterplot▪ Row Colors▪ Row Markers▪ etc.

• Interacting with thescatterplots• Suppose those highlightedpoints are considered as‘outliers’ and need to beremoved from the plot (or theanalysis)▪ Right click on the scatterplot▪ Row Hide▪ Row Exclude▪ ▼ Red triangle menu > Script >Redo Analysis to update the plot• Using the Scatterplot Matrix platform,you can assess the relationships betweenmultiple variables simultaneously• A scatterplot matrix is an orderedcollection 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 clickY, Columns• Select Species and click Group• Click OK• To make the groupingsstand out, you can:• From the ▼ red trianglemenu, select DensityEllipses• From the ▼ red trianglemenu, select ShadedEllipses• The Scatterplot t3D platform ltf shows the values ofnumeric columns in the associated data table in arotatable, 3D view• Select Graph > Scatterplot 3D• Exercise:• Help > Sample data > Iris• Select Graph > Scatterplot 3D• Select Sepal length, Sepal width,Petal length, and Petal widthand click Y, Columns• Click OK

• InformationDisplayed onthe Scatterplot3D Report• 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 ddbased on species• Help > Sample data > Iris• Select Graph > Scatterplot 3D• Select Sepal length, Sepal width, Petal length, and Petal width and clickY, 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• Example ofGroupedNormal ContourEllipsoids• If we selectNonpar DensityContour insteadof NormalContourEllipsoids, wecan createnonparametricdensitycontours

• The variability charts areused when we havemultiple categorical xvariables ibl and one y variable ibl• Select Graph >Variability/Gauge Chart• Exercise:• Help > Sample data > Car PhysicalData• Select Graph > Variability/GaugeChart• Select Weight as Y, Response,Country and Type as X, Grouping• Click OK• From the ▼ redtriangle menu, youcan• Connect Cell Means(blue lines are added)• Uncheck Show RangeBars (easier to seepoints)• Show Group Means(purple lines are added)• A bubble bbl plot is a scatter plot that represents itspoints as circles, or bubbles. You can use bubbleplots to:• dynamically animate bubbles using a time variable, tosee patterns and movement across time• use size and color to clearly distinguish betweendifferent e variablesabes• Bubble plots can produce dramatic visualizationsand readily show patterns and trends• Select Graph > Bubble Plot• 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 provides a platform where youcan interactively create and modify graphs• Graph types include points, lines, bars,histograms, etc.• It allows you to explore relationships betweenseveral variables on the same graph• Select Graph > Graph Builder• Exercise:• Open SAT data• Create a histogram for SAT Math• Exercise:• Open SAT data• Create a histogram forSAT Math by Region• 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

• 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• Exercise: Car Physical Data• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources• To general numerical summaries of data, youcan:• Create a table that contains columns of summarystatistics• Tabulate data so it is displayed in a tabular format59

• The Tables > Summary command calculatesl various summary statistics, including the meanand median, standard deviation, minimum andmaximum value, etc.• Select Tables > Summary• Select the columns you want to summarize in theSelect Columns box• A new data table is created to store all thesummary statistics requested but it is not savedwhen you close it unless you select File > Save Asto give it a name and location• Exercise: Create summary statistics for SAT Verbal• Open SAT data• Select Tables > Summary• Click SAT Verbal near upper left• Click Statistics buttonand choose Mean• Can choose any statistic• Can choose more thanone statistic ttiti – clickStatistics again• Click OK• Use the Tables > Tabulatecommand for constructingtables of descriptivestatistics• The tables are built fromgrouping columns, analysiscolumns, and statisticskeywords• Through its interactiveinterface for defining andmodifying tables, theTabulate commandprovides a powerful andflexible way to presentsummary data in tabularform• Examples of summary tables:• To create a summary table using the Tabulateblcommand is an iterative process:• Click and drag the items (column name from thecolumn list or statistics from the keywords list) fromthe appropriate p list• Drop the items on the dimension (row table orcolumn table) where you want to place the items’lbl labels• After creating a table, add to it by repeating theabove process

• When you drag and drop a variable, ibl JMPpopulates the table automatically for it if its role isobvious, such as keywords or character columns• Otherwise, a popup menu lets you choose the rolefor the variable• Add Grouping Columns –if you want to use thevariables to categorize the data. For multiple groupingcolumns, Tabulate creates a hierarchical nesting of thevariable• Add Analysis Columns –if you want to compute thestatistics of these columns• Exercise: Create descriptive statistics ttiti for SATMath by Region• Open SAT data• Select Tables > Tabulate• Click Region and drag and drop it into the Drop zone forcolumns• Select Add Grouping Columns• Click Mean and drag and drop it into the first blank cellon the third row• Click Std Dev and drag and drop it just below Mean• Exercise: Create descriptive e statistics for SATMath by Region• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources68

• One variable ibl (univariate)i • Distribution• Two variables (bivariate)• Fit Y by X• More than two variable• Fit Model• More advanced features• Modeling• One‐Sample t‐TestT t• Data: Help > Sample Data > Fitness• Linneruds Fitness data:fitting oxygen uptake to exercise and othervariables. The original is in Rawlings (1988), butcertain values of MaxPulse and RunPulse werechanged for illustration. Names and Sexcolumns were contrived for illustration6970• One‐Sample t‐TestT t• 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 nullhypothesis• Paired t‐Test – used when you have two relatedmeasurements• Create a new column for ‘difference’▪ Select Cols > New Column▪ Type Difference in the Column Name box▪ Select Cols > Formula▪ Sl 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 Pairs7172

• Two‐Sample t‐Test – used when you compare themeans 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 b>|t| is 0.1835, there is not enough evidence to reject the nullhypothesis• One‐Way ANOVA with ih two groups – usedwhen you compare the means of twopopulations• Same as Two‐Sample t‐Test7374• One‐Way ANOVA with ih more than two groups –used when you compare the means of more thantwo populationsp• 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 byCountry > Normal Quantile Plot▪ ▼ Red triangle menu next to Oneway Analysis of Weight byCountry > UnEqual Variances• One‐Way ANOVA with ih more than two groups• Example: Car Physical Data (cont.) ‐ Residuals▪ ▼ Red triangle menu next to Oneway Analysis of Weight byCountry > Save > Save Residuals▪ Rename Weight centered by Country as residual▪ Sl Select Analyze > Distribution ib ti > residual > Y, Columns > OK▪ Select Continuous Fit > Normal > Goodness of Fit▪ ▼ Red triangle menu next to Oneway Analysis of Weight byCountry > Means/ANOVA▪ Prob > F is 0.0001, this is strong evidence for concluding that atleast one mean is statistically different from one of the othermeans7576

• One‐Way ANOVA with ih more than two groups• Example: Car Physical Data (cont.) – Contrasts▪ ▼ Red triangle menu next to Oneway Analysis of Weight byCountry > Compare Means > Each Pair Student’s t▪ The diamonds for 1 and 2 overlap –they probably are notdifferent; 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▪ ▼ Rd Red triangle menu next to Comparisons for each pair usingStudent’s t > Different Matrix▪ ▼ Red triangle menu next to Comparisons for each pair usingStudent’s t’ t > Detailed Comparisons Report• One‐Way ANOVA with ih more than two groups• Example: Car Physical Data (cont.) – Contrasts▪ ▼ Red triangle menu next to Oneway Analysis of Weightby Country > Compare Means > All Pairs, Tukey HSD▪ Use this test to control the experimentwise error rate at thesignificance level α (e.g. α=0.05) 05)7778• N‐Way ANOVA – used when there are more than onecategorical 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• 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 madecompact 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 Japanmade sporty car7980

• Simple Linear Regression – used to assess thesignificance of the predictor in explaining thevariability 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• 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 ofOxy By Age > Density Ellipse > 0.95▪ Hold down Ctrl and click ▼ Red triangle menu next to Bivariate Fit ofOxy By Age > Fit Mean▪ Hold down Ctrl and click ▼ Red triangle menu next to Bivariate Fit ofOxy By Age > Fit Line8182• Multiple Linear Regression – used to model therelationship between many continuous predictors anda 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• Multiple l 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▪ Run8384

• Multiple l 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 Model85• Multiple l Linear Regression• Example: Fitness (cont.) –Add interaction and higher orderterms▪ 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)▪ Run86• A model dlrelating a categorical predictor and a continuouscovariate to a single continuous response is known as ananalysis of covariance (ANCOVA) model• ANOVA with categorical and continuous predictors• First of all, need to identify if there is interaction betweenpredictors• 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)• Example 1: DrugLBI – no interactionsi▪ Select Analyze > Fit Model▪ Select LBS > Y▪ Select Drug, LBI > Macros > Full Factorial or Factorial toDegree▪ Click Run▪ P‐value for Drug*LBI = 0.5606, greater than 0.05, indicatingthat Drug*LBI is not significant, thus can be removed fromthe model▪ Examine the interaction in the Regression Plot:A linear regression line is drawn with a different color foreach level of Drug. It may be difficult to interpret this graphfor statistical significance of the interaction8788

• Example 1: DrugLBI – no interactions• Re‐do the analysis without including the interactionterm▪ 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 theresponse LBS• Example 2: Sawblade – model with interaction• Data:▪ Import Sawblade.xls l file to JMP• Notes:▪ Fit a model to study the effect of blade material and bladespeed on heat generation8990• Example 2: Sawblade – model dl with ih interactioni▪ Select Analyze > Fit Model▪ Select Heat > Y▪ Select Material, Speed > Macros > Full Factorial or Factorialto 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 aconclusion about Material or Speed along; the effect ofMaterial 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 foreach level of Material91• To re‐produce the previous analysis when youre‐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 Script92

• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources• 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 As9394• 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.• Sl Select File > Save As95• When you need to use JMP reports or data tables in anotherprogram, you can copy and paste parts of it into thedocument, such as Microsoft Word or PowerPoint file.• Click the selection tool• Click and drag (or hold down Shift and click) to select items in areport window or data table• Click the selected items and drag them from JMP to the otherprogram• Or, copy the selected items in JMP and paste them into the otherprogram• Note:• To copy all text (no graphs) from the active report window asunformatted text, select Edit > Copy As Text• To copy only the graph (no text), right‐click the graph and selectEdit > Copy Picture96

• Exercise:• Bring up any analysis in JMP• Press Alt and chooseselection tool• Click on plot• Copy (Ctrl + C) from JMP,Paste (or Paste Special) intothe desired program• Introduction• Getting Started• Managing Data• Visualizing Data• Creating Summary Statistics• Performing Basic Statistical Analysis• Saving and Exporting Results• Resources9798• Help menu• Indexes• Tutorials• Books – JMP documentations▪ Discovering JMP▪ Using JMP▪ Basic Analysis and Graphing▪ DOE Guide• Sample Data• 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 Guide99100

• On‐line resources• http://www.lisa.stat.vt.edu/Welcome to LISA!• http://www.lisa.stat.vt.edu/?q=short_coursesLISA short courses• 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 Notes101102103