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Design-Expert offers you the latest technology for multi-factorial data analysis and design of experiments in a very user-friendly environment. Design Expert walks you through the classic stages of the screening, optimization (RSM) and validation and provides the flexibility to map complex tasks in a “simple” experimental design. Design Expert thus allows you to save time and costs of developing new products while achieving the best process conditions.
This includes so called hard-to-change factors (for split-plot designs ) and optimization of chemical formulations or mixture designs as well as combined designs which handle mixture and process factors in a single experiment.
Design-Expert provides the rotatable 3D plot. It helps you to visualize so-called response surfaces. The optimum is reached via the numerical optimization function. There, the optimal factor settings are determined simultaneously. The optimization platform controls mulitivariate optimization to allow, for example, multiple target values being simultaneously optimized. Thus, conflicts conflicts between target variables can be solved.
Arguments for Design Expert:
- Rotatable 3D graphics, interactive contour diagrams (isolines), best ternary representation,
- All classical experimental designs, d-optimal for screening and i-optimal for RSM,
- Latest split-plot designs and definitive screening designs,
- Best optimizing function (multiple target variables and optimizing with respect to factor settings),
- Fitted function exported as a formula to Excel
- Error propagation (propagation of error ) helps you to find robust settings
- Indispensable for formulation optimizing
Design-Expert – Best of breed in Design of Experiments!
Optimize your product or process with Design of Experiments (DOE). Design-Expert® offers features you won't find anywhere else in an incredibly easy-to-use format. This powerful program is a must for anyone wanting to improve a process or a product. With Design-Expert you can screen for vital factors, locate ideal process settings to achieve peak performance and discover your optimal product formulations.
Design-Expert offers an impressive array of design options. Design Expert provides great flexibility to handle categorical factors and allows them to be combined with mixture and/or process variables. After building your design, generate worksheets with your experiments laid out for you in randomized run-order. Add, delete or duplicate runs in any design with the handy design editor.
With annotated statistical analysis and an extensive context-sensitive help system, you can easily interpret the outputs. Interactive 2-D graphics support use of your mouse to drag contours or set flags that display coordinates and predicted responses. Rotatable 3-D plots make response visualization easy.
With the powerful optimization features in Design-Expert, you can maximize desirability for dozens of responses simultaneously. There are also unique tools for generating and graphing propagation of error (POE), thus allowing you to achieve six-sigma objectives for reducing variation. Maximize, minimize or hit targets with factor levels set to give you robust results.
Powerful, Yet Easy to Use
Designed as a specialized DOE software package, Design-Expert offers features for ease of use, functionality and power that you won't find in general statistical packages. You'll discover a wide variety of designs, the flexibility to modify designs, unique evaluation capabilities, tools for response modeling, graphics to simplify interpretation, multiple response optimization, POE capabilities, an intuitive interface and a greatly expanded help system.
A Tremendous Variety of Designs Meet All Your Experimental Needs
- Standard two-level full and fractional factorials (up to 512 runs) for testing up to 21 factors simultaneously, now also with minimum-aberration blocking choices
- General (multilevel) factorial designs (up to 32,000 runs) using factors with mixed levels
- Taguchi orthogonal arrays
- High-resolution irregular fractions, such as 4 factors in 12 runs
- Placket-Burman designs for 11, 19, 23, 27 or 31 factors in 12, 20, 24, 28, 32 or 64 runs respectively
- “Min-Run Res IV” (two-level factorial) designs for 5 to 50 factors: Screen main effects with maximum efficiency in terms of experimental runs.
- Response Surface Method (RSM) designs, including central composite (small, face-centered, etc.), Box- Behnken (3-level), hybrid and D-Optimal
- Mixture designs, such as simplex-lattice, simplex-centroid screening (for up to 24 components) and D-optimal
- Combined mixture and process designs (mix your cake and bake it, too!)
- 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
Enjoy Incredible Flexibility in Design Modification
- Define your own generators for fractional factorial designs
- Impose linear multivariable constraints on RSM or mixture designs
- Add categorical factors to RSM, mixture or combined designs
- Create a factorial candidate set for RSM designs when only specific factor levels are available
- Ignore a row of data while preserving the numbers
Build Confidence with Statistical Analysis of Data
- 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
- Select optional annotated views for assistance interpreting the ANOVA
- Inspect F-test values on individual model terms and confidence intervals on coefficients
- Automatically select effects using Lenth's criteria or probability values
- Take advantage of new user preferences, for example, make a global change in the significance threshold (0.05 by default vs. 0.01 and 0.1)
Take Advantage of Powerful Tools for Response Modeling
- Change models from RSM to factorial and back and from Scheffe (mixture) to slack (during design building and at model selection)
- Add integer power terms to the model, for example, quartic
- Select terms for model, error, or to be ignored (allows analysis of split-plot and nested designs)
Simplify Interpretation with Terrific Graphics
- A quick summary of the design type as well as factor, response and model information is available by clicking on the design status 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-versus-actual graphs with a 45º line)
- Graph alternative aliased interactions
- 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 (and see actual design points when they're on a slice!)
- Set flags to reveal the predicted response at any location
- Drag 2-D contours using your mouse
- Rotate 3-D graphics and see projected 2-D contours
- 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, 3-D, etc.)
- Maximize, minimize or target specific levels for both responses and factors
- Set weight and importance levels to prioritize responses for desirability
- Choose 2-D contour, 3-D surface, histogram or ramp desirability graphs
- Include categorical factors
- Set factors at constant levels
- Add equation-only responses, such as cost, to the optimization process
- Look at the overlay plot to view constraints on your process or formulation
- Predict responses at any set of conditions (including confidence levels)
- Discover optimal process conditions or formulations
Achieve "Six-Sigma" Goals
- Explore propagation of error (POE) for mixtures, crossed designs and transformed responses, as well as RSM
- For purposes of POE, enter your own response standard deviation or set it at zero
Save Time with Design-Expert's Intuitive Interface
- Easily maneuver through the program: down trees, through wizards, and across progressive toolbars
- Quickly select the next step with incredibly easy-to-use push-buttons
- Open reports and graphs for automatic updating
- View numerical outputs spreadsheet style
- Cut and paste graphics to your word processor or presentation, or numbers to and from a spreadsheet
- Export any grid 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 95, 98, 2000, NT 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
Find the Answers to your Questions in the Expanded Help System (All new!)
- Greatly improved context-sensitive help provides immediate response
- Better guidance helps you choose the best model
- A bonus help section provides "quick start" advice to novices
- Special user tips offer hints not normally found in help systems
Design Expert 11 – Trial
Design Expert 11 trial is available for DOWNLOAD directly from Stat-Ease beeing the manufacturing company of Design Expert. Design Expert 11 trial is valid for 45 days. During download you will be requested to register at Stat-Ease.
|Operating System||OSX 10.10 (Yosemite
OSX 10.11 (El Capitan)
macOS 10.12 or higher
|Windows Vista SP2, 7, 8, 10|
|Minimum CPU||1 GHz||1 GHz|
|Min. RAM||2 GB||2 GB|
|Disk Space||250 MB||250 MB|
Important changes and improvements in Design Expert 11:
Hard-to-change factors handled via split plots
- Two-level, general and optimal factorial split-plot designs: Make it far easier as a practical matter to experiment when some factors cannot be easily randomized.
- Half-normal selection of effects from split-plot experiments with test matrices that are balanced and orthogonal: The vital effects, both whole-plot (created for the hard-to-change factors) and sub-plot (factors that can be run in random order), become apparent at a glance!
- Power calculated for split plots versus the alternative of complete randomization: See how accommodation of hard-to-change factors degrades the ability to detect certain effects.
Other new design capabilities
- Definitive screening designs: If you want to cull out the vital few from many numeric process factors, this fractional three-level DOE choice resolves main effects clear of any two-factor interactions and squared terms (see screen shot of correlation matrix - more on that later).
- On the Factorial tab select a simple-sample design for mean-model only: Take advantage of powerful features in Design-Expert software for data characterization, diagnostics and graphics - for example with raw outputs from a process being run at steady-state.
Much-improved capabilities to confirm or verify model predictions
- Entry fields for confirmation data and calculation of mean results: Makes it really easy to see if follow-up runs fall within the sample-size-adjusted prediction intervals.
- Enter verification runs embedded within blocks as controls or appended to your completed design: Lend veracity to your ultimate model by these internal checks.
- Verification points displayed on model graphs and raw residual diagnostics: See how closely these agree to what's predicted by your model.
New and more-informative graphics
- Adjustably-tuned LOESS fit line for Graph Columns: Draw a curve through a non-linear set of points as you see fit. *(Locally weighted scatterplot smoothing.)
- Color-coded correlation grid for graph columns: Identify at a glance any factors that are not controlled independently of each other, that is, orthogonally; also useful for seeing how one response correlates to another.
Greater flexibility in data display and export
- Journal feature to export data directly to Microsoft Word or Powerpoint: Fast and formatted for you to quickly generate a presentable report on your experimental results.
- Improved copy/paste of Final Equation from the analysis of variance (ANOVA) report to Microsoft Excel: This not only saves tedious transcription of coefficients but it also sets up a calculator for you to 'plug and chug', that is, enter into the spreadsheet cells what values for the inputs you'd like to evaluate and see what the model predicts for your response.
- New XML* script commands for exporting point predictions: Helpful for situations where one wants to automate the transfer of vital outputs from Design-Expert to other programs. *(Extensible Markup Language)
More powerful tools for modeling
- All-hierarchical model (AHM) selection: Sort through all possible models up to the one you designed the experiment for, but all the while maintain hierarchy of terms so you do not end up with something ill-formulated.
- Special quartic Scheffé polynomial included in automatic selection for mixture modeling: Sometimes this added degree (4th!) of non-linear blending helps to better shape the response surface – making it better for predictive purposes.
More choices when custom-designing your experiment
- Enter a single factor constraint for response surface designs: Creates a 'hard' limit on inputs that cannot go beyond a certain point (such as zero time) physically or operationally.
More capability for numerical optimization
- Include Cpk* as a goal: Meet quality goals explicitly. *(A process capability index widely used for Six Sigma and Design for Six Sigma programs.)
Enhanced design evaluation
- One-sided option added to FDS* graph: Size your design properly for a verification experiment done to create a QBD** design space. *(Fraction of design space) **(Quality by Design - a protocol promoted by the US Food & Drug Administration (FDA).)
Many things made nicer, easier, more configurable and faster
- Diagnostics report now can be sorted by any of the statistics listed: This enables a more informative ordering than by run number (the default).
Niceties that only statisticians might truly appreciate
- Mean correction for transformation bias when responses displayed in original scale: All you need to know is that our statisticians figured out how to eliminate a tricky, little-known bias!
- Propagation of error (POE) carried out to the second derivative: Makes POE more accurate - that's a good thing!
- Allow averaging of categoric factors when viewing a graph: Convenient for getting the big picture of where to find robust operating settings.
- Display confidence bands with or without POE added: Easier to match output with other programs that do not offer POE features like this.
- Add unblocked results to evaluation of blocked experiments: Aids in comparing designs on the basis of matrix measures.
Good news for network administrators
- >New more flexible and easier-to-use license manager with greater power to serve enterprise users: For example, network 'seats' can be checked out to individual laptops and multiple opening of the program on a specific computer will only use one seat.