What's New in Version 8

New graphics and improved interface

• Half-normal selection of important effects on all factorial designs*: Simple and robust method for selecting important effects—formerly available only for two-level designs.  For example, the screen shot to the right is from an experiment on 5 woods glued with 5 adhesives, using 2 applicators with 4 clamps at 2 pressures.  The vital effects become apparent at a glance!
  *(Detailed in “Graphical Selection of Effects in General Factorials”—winner of the Shewell Award for best presentation at the 2007 Fall Technical Conference, co-sponsored by the American Society for Quality and the American Statistical Association.)

  

• Smoother color gradations on 2D contours: More impressive for presentations to management, clients, or colleagues.
  
  
   
• Rounded contour values: More presentable defaults requiring less ‘fiddling’ for reporting purposes.
• Plant flags on 3D surfaces: Previously, you could only put flags on 2D contour plots.  To the right we see a flag planted by numerical optimization to maximize the filtration rate in an industrial process.
  
  
   
• New and fully configurable mesh option that reflects smooth, lighted colors off your 3D surface: Dazzle your customers and colleagues while providing highly-informative graphics showing how responses will react to process changes. (Mesh can be turned off if you like.)
• 3D graphs that you can spin with your mouse: When you see your cursor turn into a hand, simply grab and rotate!  Double-click the graph to go back to the starting angle.
• Push-button averaging on the factors tool: Provides far easier main effects plotting and makes interactions more meaningful.  Previously, the only option to average factors came via a hidden drop-list. The screen shot series below shows the result of simply pressing the “Avg” for 5 woods glued with 5 adhesives using 2 applicators at 2 pressures.  This causes the least significant difference (LSD) bars to shrink, revealing an important difference between two particular clamps.
  
  
   
• More-interactive cube plots: Click on design points to see factor levels and response predictions on graph legends, as shown below.
  
  

• Enter input variables vertically: When entering many levels, this may be more convenient than the horizontal layout (see below).
  
  
   
• Reference lines on plots: Horizontal, vertical, and free style-lines enhance plots. As shown below, it becomes clear that four clamps tested for a wood-adhesive application fall into two distinct groups—acceptable versus not acceptable, based on a cutoff of 50.
  
  
   
• Predicted vs. Actual graph availability in Model Graphs, not just in Diagnostics: This is useful when a response has been transformed because in Model Graphs mode, you can view the more relevant original scale.
  
  
   
• Confidence, prediction, and tolerance intervals (CI, PI & TI) plotted with configurable colors in one-factor response plots: Convey prediction uncertainties via bands around the best fit.  The screen shot below shows actual run results represented as solid red circles.  The solid line is the predicted value based on the model.  The bands represent the CI (narrowest), PI, and TI (widest).
  
  
   
• Color-coded response surface graphs show where standard error increases: This makes it easier to understand why extrapolating beyond the actual experimental region for a prediction will get you into trouble.  The graphic shows a flag set beyond the factorial points in a fractional factorial—ultimately making the prediction meaningless (high standard error is indicated by the dark shading).
  
  

More choices when custom-designing your experiment

• D-, IV-, and A-optimal design selection: New and expanded optimization criteria for use when crafting algorithmically designed experiments of a specified model order.
  
  

• Tolerance-interval-based design sizing: Enhances your fraction of design space (FDS) plots to assess whether your planned experiment is large enough, given the underlying variability (noise), to establish tolerances within the acceptable range.
  
  

Additional statistics and more concise reporting of vital results

• Improved curvature testing for factorials with center points: All design points are now fitted to the polynomial model used for predictions.  This provides a more realistic impact of significant non-linear response behavior.  Diagnostics can be done for the model adjusted for curvature or, via a view option, unadjusted.  Models without a term for curvature (unadjusted) are used for model graph and point predictions.
• Coefficients summary: After modeling your response(s), see a concise table of coefficients that’s color-coded by relative significance. Below, the second response is modeled only by main effects, two being significant at the p<0.1 level.
  
  
   
• Tolerance interval (TI) estimates on point prediction: This is important for verification studies to ensure your process stays within manufacturing specifications.  For example, the TI shown below provides assurance that thickness will remain within a required range of 4400 to 4600.
  
  

Increased visibility and versatility of tools and features

• Many new, high-visibility tools: Options previously available via hidden View menu options are now easily seen and capitalized upon. The Design Tool shown ‘floating’ on the screen shot below is one example.
• Design layout column widths now adjust automatically by double-clicking column-header boundaries: Multiple columns adjust simultaneously!
• Attach row comments by right-clicking on the row headers.  View them by right-clicking on the Select box and selecting “Comments” as a column header: A handy way to record important observations, as shown below.
  
  

  Note: Runs having a comment will be identified with an asterisk in the “Select” column as shown in the screenshot above.

• Row comments are also visible when a point is highlighted on a plot.  See how the “starch sticky” comment from above is carried over to the plot below, alerting the experimenter to the unique observations about this treatment.
  
  
   
• Topic Help, Tutorials, and Sample Files now also reside in the main Help menu: Follow these alternate paths for getting timely program advice.
• In addition, Topic Help (F1) content has been greatly enhanced.  Context-specific information is right at your fingertips.  Simply press the F1 key to discover options, definitions, and suggestions.
• Screen Tips is now a main menu item (“Tips”): Great visibility and easy access to very useful just-in-time advice, shown below.
  
  

Enhanced Design Evaluation

• Several new matrix measures are now provided: Most notable is the G-efficiency.  (This criterion, expressed on a 0 to 100 percent scale with higher being better, leads to designs that generate more consistent variance of your predicted response.  However, like any other single measure, it may not accurately reflect the overall effectiveness of a particular matrix.  That’s why Design-Ease provides an array of matrix statistics and graphics for overall design evaluation.)
• New, powerful tools for multiple response optimization: Options include standard error models. All else equal, choose system settings in regions predicted to exhibit the highest precision.

Many things made nicer, easier faster throughout the program

• One-click updates: Check for free software updates with one click (shown below) and download them directly.
  
  

• Better defaults and tick marks: Nicely rounded values provide presentable graphs straight away.
• Zoom up graphs with your mouse wheel (a right-click resets to original size): Quickly zero in on regions of interest.
• Hold down your left mouse button to drag graphs into various positions (a right-click resets original placement): It’s a fast way to situate the region of interest where you want it in the coordinate space. Factors G and H in the trace plot at right are constrained to very tight ranges relative to other ingredients. They are hardly visible without first zooming and then dragging the intersection (the overall centroid of the formulation space) to the middle.
  
  
   
• Separate preference tabs for X-Y versus surface graphs: Design-Ease v8 delivers plotting and graphing simplicity.
• Reduced graph-updating flicker: Now it’s less distracting when you redraw responses at varying input-variable levels.
• Keyboard shortcut for preferences: Press Ctrl + F8 to get a box allowing you to adjust all of the program preferences with one click, a convenient way to reset all of the default settings.
• Color-by-point-type added to graph columns: Very useful addition to scatter-plots, such as this one below for a factorial design with center points.
  
  

Technical stuff only programmers will appreciate

• Upgraded MFC (Microsoft Foundation Class) common controls: This new application framework provides an improved look and feel.
• XML utility offers new script feature that lists all possible commands. You can parse files with extensions other than .xml. It also provides new import/export/reset-preference commands: TRANSLATION: More power to operate Design-Ease programmatically.

Other great features you will find in Design-Ease 8 software include:

A Variety of Design Creation Tools to Meet All Your Experimental Needs

• Upfront power calculation for factorial designs: This mainstreams in the design-builder a ‘heads-up’ on the percent probability of seeing the desired difference in each response—the signal—based on the underlying variability—the noise.
• “Min-Run Res V” designs are now available for 6 to 50 factors: Resolve two-factor interactions (2FI's) in the least runs possible while maintaining a balance in low versus high levels.
• “Min-Run Res IV” (two-level factorial) designs for 5 to 50 factors: Screen main effects with maximum efficiency in terms of experimental runs.
• Two-level full and fractional factorials for up to 512 runs and 21 factors, along with minimum-aberration blocking choices: Build large designs.
• New “Color By” option: Color-code points on graphs according to the level of another factor—a great way to incorporate another piece of information into a graph.
• General (multilevel) factorial designs (up to 32,766 runs) using factors with mixed levels.
• High-resolution irregular fractions, such as 4 factors in 12 runs.
• Placket-Burman designs for 11, 19, 23, 27 or 31 factors in up to 64 runs respectively.
• Taguchi orthogonal arrays.
• Ability to graph any two columns of data on the XY graph (this is a great way to view a blocked effect).
• Easy-to-use automatic or manual model reduction.
• Ability to easily analyze designs with botched or missing data.
• Design-builder updates resolution of two-level fractional factorials when the number of blocks is changed: Immediately see how segmenting a design might reduce its ability to resolve effects.
• Block names are now entered during the design build: Identify how you will break up your experiment, for example by specific shift, material lot or the like.
• “Min-Run Res IV plus two” option: Ask for two extra runs to make your experiment more robust to missing data.
• User-defined base factors for design generators: You have more flexibility to customize fractional factorial designs.
• Expanded optimal capabilities—impose balance penalty, force categoric balance: This feature helps users equalize the number of treatments.
• Coordinate Exchange capability for optimal designs: Avoid the arbitrary nature of designs constructed from candidate point sets.
• In General or Factorial optimal designs, categorical factors can be specified as either nominal or ordinal (orthogonal polynomial contrasts): This affects the layout of analysis of variance (ANOVA).

• Design layout can now be modified via a right-click list with added columns for point type and other alternative attributes: Make your "recipe" sheet more informative.
• Add blocks D-optimally: This feature will be especially useful for mixture designs, which previously could not be blocked automatically.
• “Semifold”: In only half the runs needed by a normal foldover, augment Res IV designs to resolve specified 2FI's aliased in the original block of runs.
• Add center points, blocks and replicates without rebuilding the design: This is a real time-saver.
• Ignore or highlight a row of data or a single response while preserving the numbers.

• “Design model” choice added for statistical analysis: This is handy for data from experiments based on a computer-generated optimal design.
• From Alias List, Pareto Chart or Effects Plots views, right-click on effects to show aliases: Never lose sight of what really is being measured in fractional-factorial designs.
• Select alternative aliased effects: Choose what you think makes most sense based on your subject-matter knowledge.
• Backward stepwise regression is now applicable to factorial designs: This is useful for quickly analyzing general (categorical) factorials.
• Means and standard deviations for all experimental inputs (factors) and outputs (responses) are added to the Design Summary screen: This provides a handy assessment of your system.
• The user can define their preference for sums of squares calculations for both numeric and categoric factors to be sequential, classical, or partial: These distinctions are important for statisticians who want to do ANOVA in specific ways.
• Select optional annotated views for assistance interpreting the ANOVA.
• If your model is aliased, a warning will pop up prior to viewing the ANOVA for two-level fractional factorials, allowing you to make substitutions for aliased effects.
• Inspect F-test values on individual model terms and confidence intervals on coefficients.
• Take advantage of user preferences, ex: make a global change in the significance threshold (0.05 by default vs. 0.01 and 0.1).

• Row(s) in the design layout are highlighted when point(s) are selected on the diagnostics: The highlighting feature makes identification of problematic data much easier.
• Box-Cox transformation parameters added to the diagnostics report: Includes stats that may not appear on the plot.
• DFFITS: Spot influential runs via this deletion diagnostic that measures difference in fits when any given response is removed from the dataset.
• DFBETAS: See from this deletion diagnostic how model terms change due to an influential run.

• Display grid lines on 3D graph back-planes: This feature provides a better perspective on the varying height of a response surface.
• Save graphs to files in enhanced Windows metafile (EMF), PNG, TIFF, GIF, BNP, JPEG, and encapsulated Postscript (EPS) formats: Many publications do their artwork in one of these file types .
• Full-color contour and 3D surface plots: Graduated or banded colorization adds life to reports and presentations.
• 3D surface plots for categorical factors: See colored bars towering above others where effects are greatest.
• Pareto chart of t-values of effects: Quickly see the vital few effects relative to the trivial many from two-level factorial experiments.
• Magnification feature: An incredible tool for expanding a graph that is originally a small sliver and difficult to interpret.
• Points on 3D graphs: See "lollipops" protruding from surfaces where actual responses were collected.
• Crosshairs window: Predict your response at any place in the response surface plot.
• Grid lines on contour plots: See more readily what the coordinates are at any given point.
• Select the details printed on flags planted on contour plots.
• Confidence bands on one-factor plots: Get a good feel for the uncertainty in a predicted response as a function of the factor level.
• Color-codes for positive versus negative effects: Assess plus or minus impacts on half-normal and Pareto plots.
• Smart tic marks: Get more-reasonably rounded settings straight off.
• A quick summary of the design type as well as factor, response and model information is available by clicking on the design summary node.
• Discover significant effects at a glance with half-normal or normal probability plots, made easier by including points representing estimates of pure error (if available from your design).
• See the Box-Cox plot for advice on the best response transformation.
• View a complete array of diagnostic graphs to check statistical assumptions and detect possible outliers (bonus feature: predicted vs. actual graphs with a rotatable best-fit line).
• See the effects plot in the original scale after transforming the response.
• Observe variation in predictions by viewing the least significant difference (LSD) bars on the model graphs.
• Poorly predicted regions on contour maps are shaded to give you confidence in your predictions.
• Slice your contour plots using a simple slide bar: See actual design points when they're on a slice!
• Drag 2-D contours using your mouse.
• Rotate 3-D graphics and see projected 2-D contours.
• Set flags to reveal the predicted response at any location.
• Edit colors, text and more to produce professional reports.
• See all effects on one graph with trace and perturbation plots.
• Plot the standard error of your design on any graph type (contour, 3D, etc.).

• Display grid lines on 3D graph back-planes: This feature provides a better perspective on the varying height of a response surface.
• Save graphs to files in enhanced Windows metafile (EMF), PNG, TIFF, GIF, BNP, JPEG, and encapsulated Postscript (EPS) formats: Many publications do their artwork in one of these file types .
• Full-color contour and 3D surface plots: Graduated or banded colorization adds life to reports and presentations.
• 3D surface plots for categorical factors: See colored bars towering above others where effects are greatest.
• Pareto chart of t-values of effects: Quickly see the vital few effects relative to the trivial many from two-level factorial experiments.
• Magnification feature: An incredible tool for expanding a mixture graph that is originally a small sliver and difficult to interpret.
• Points on 3D graphs: See "lollipops" protruding from surfaces where actual responses were collected.
• Crosshairs window: Predict your response at any place in the response surface plot.
• Grid lines on contour plots: See more readily what the coordinates are at any given point.
• Select the details printed on flags planted on contour plots.
• Confidence bands on one-factor plots: Get a good feel for the uncertainty in a predicted response as a function of the factor level.
• Color-codes for positive versus negative effects: Assess plus or minus impacts on half-normal and Pareto plots.
• Smart tic marks: Get more-reasonably rounded settings straight off.
• A quick summary of the design type as well as factor, response and model information is available by clicking on the design summary node.
• Discover significant effects at a glance with half-normal or normal probability plots, made easier by including points representing estimates of pure error (if available from your design).
• See the Box-Cox plot for advice on the best response transformation.
• View a complete array of diagnostic graphs to check statistical assumptions and detect possible outliers (bonus feature: predicted vs. actual graphs with a rotatable best-fit line).
• See the effects plot in the original scale after transforming the response.
• Observe variation in predictions by viewing the least significant difference (LSD) bars on the model graphs.
• Poorly predicted regions on contour maps are shaded to give you confidence in your predictions.
• Slice your contour plots using a simple slide bar: See actual design points when they're on a slice!
• Drag 2-D contours using your mouse.
• Rotate 3-D graphics and see projected 2-D contours.
• Set flags to reveal the predicted response at any location.
• Edit colors, text and more to produce professional reports.
• See all effects on one graph with trace and perturbation plots.
• Plot the standard error of your design on any graph type (contour, 3D, etc.).

Achieve Six-Sigma Goals

• Explore propagation of error (POE) for transformed responses.
• For purposes of POE, enter your own response standard deviation or set it at zero.

• Import and export text files to get responses: Something do-able by anybody.
• Right-click on any response cell and “ignore” it: This feature allows you to ignore a response data point without having to ignore the entire row.
• Keystroke option (Ctrl+/-) to move through alternate solutions from numerical optimization: This saves mousing around.
• From the Design node, display mixture constraints coded in actual, real, or pseudo values: An important distinction for understanding the experimental region of formulation.
• More flexibility in handling various file types when opening files: Very helpful default that automatically recognizes any data in the Design-Ease (.de*) or Design-Expert (.dx*) format—including ones produced from older versions.
• On plots of effects simply draw a box around the ones you want selected for your model: This is much easier than clicking each one with your mouse.
• Set row status to normal, ignore or highlight: This allows users control over their design matrix.
• Sort by row status — normal, ignored or highlighted: Most real-life experiments do not go as planned so it’s good to easily assess the damage.
• Numerical optimization solutions are now carried over to graphical optimization and point prediction: Explore the results of the numerical optimization on other screens.
• Cut and paste graphics to your word processor or presentation, or numbers to and from a spreadsheet.
• Easily maneuver through the program: down trees, through wizards, and across progressive toolbars.
• Tab flow through all fields on the screen: Quicker for data entry than having to click your mouse in a new location.
• Quickly select the next step with incredibly easy-to-use push buttons.
• Open reports and graphs for automatic updating.
• View numerical outputs spreadsheet style.
• Export any spreadsheet view as ASCII text, for example, design layouts or ANOVA reports.
• View several graphs simultaneously using the handy pop-out option.
• 32-bit architecture provides maximum performance on Windows XP, Windows Vista, Windows 7, and beyond.
• Access graphic and spreadsheet options instantly with a simple right click.
• Choose significant terms to plot from the pull-down list on the Factors Tool.

• Fraction of design space (FDS) graph for design evaluation: This enhancement, suggested to us by DOE guru Douglas Montgomery, provides very helpful information on scaled prediction variance (SPV) for comparing alternative test matrices — simple enough that even non-statisticians can see differences at a glance and versatile enough for any type of experiment — mixture, process or combined.
• Bookmarks for reports with a toolbox to facilitate selection: This will save you a lot of time scrolling through long statistical outputs such as the design evaluation and analysis of variance.
• Annotation option on reports: This will be a boon to those who may be unfamiliar with all the esoteric statistics needed for design evaluation.
• Customizable design evaluation content and power levels: Use the OPTIONS button to select which statistics to display, specific power levels to report, and whether to display the standard error or variance on the graph (with the option to scale by N—the number of runs in the design).
• Specify model terms to ignore (during evaluation) so they don’t display in the alias list: For example, don’t bother showing interactions of four or more factors.
• Evaluation can be done on either a design or a particular response: Shows the effect when data is missing from a specific response, but not all responses.

• “Screen tips”: Press the new tips button for enlightenment on the current screen—this is especially helpful for novice users.
• Tutorial movies: See Flash demo’s of features via Screen Tips—a very effective way to show how to navigate through the software.
• Internet links: These are helpful connections to further information.
• Better guidance helps you choose the best model.
• A bonus help section provides "quick start" advice.

• XML (eXtensible Markup Language) capability: Export design files or reports in a viewable format that can be manipulated for further processing. (The XML tool also allows import of designs created externally.)
• Write transfer functions in format (.vta) readable by VarTran® software (Taylor Enterprises): This sets the stage for statistical tolerancing and sensitivity analysis leading to more robust designs.
• Scripting capability: Run Design-Expert software in batch mode so it can be tied into more comprehensive lab ware or used to cycle through massive quantities of data, for example from computer-based simulations.

Extras!

• Free technical support
• Limited free statistical support
• Helpful tutorials to illustrate the most powerful features
• 30-day money-back guarantee

Supported Operating Systems

• Windows XP
• Windows Vista
• Windows 7

System Requirements

Check out these and other features in Design-Ease, Version 8 software with a fully-functional trial.

For a free 45-day trial version of the Design-Ease 8 for Windows program, please click here to sign up.

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