Flexible Features to Refine and Extend the Analysis

Response Transformation

In situations where a response is not normally distributed, or where the error variance of each observation is not constant, you may wish to apply a response transformation. This allows you to analyze the response using a different scale (e.g., you could use ln(Y) instead of Y). If you aren't sure whether to use a transformation, DOE++'s Select Transformation tool can help you decide and even recommend a transformation for you.

Enhanced in Version 10!

Optimization for Your Desired Outcome

DOE++’s Optimization Folio provides a collection of powerful tools that you can use to explore the factor level combinations that produce response values within the limits you specify. This includes the ability to search for the factor level combination that produces the most desirable output, as well as the ability to display all the combinations that keep the output within the specified limits.

In Version 10, you can now choose to hold factors fixed at specified levels during the optimization. We've also made it easier and more intuitive to specify the optimization settings.

Response Prediction for Untested Treatments

DOE++'s Prediction window makes it easy to enter your own combinations of factor settings to predict the resulting response values — complete with confidence metrics — based on the fitted model.

Variability Analysis

For designs with more than one replicate, DOE++ allows you to determine the variability of the response(s) across runs and to analyze that standard deviation information. This offers valuable insight into the sources of variation within the experimental data.

Obtain Response Data via Simulation

With the new Simulation Worksheet, you can link an experiment design to a diagram in BlockSim/RENO to obtain simulated response data. The simulated data can then be analyzed in DOE++ in order to investigate the effect of one or more settings on the simulation results. This empowers you to make useful predictions about how different factors influence responses such as cost or availability, without having to invest the time and resources needed to conduct an experiment.

Weibull++ Reliability Life Data Analysis ALTA Accelerated Life Testing Data Analysis DOE++ Experiment Design and Analysis RGA Reliability Growth and Repairable System Analysis BlockSim System Reliability and Maintainability Analysis RENO for Risk Analysis via Discrete Event Simulation Lambda Predict Reliability Prediction Xfmea FMEA and FMECA RCM++ Reliability Centered Maintenance MPC MSG-3 Maintenance Program Creation XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management ALTA Accelerated Life Testing Data Analysis BlockSim System Reliability and Maintainability Analysis DOE++ Experiment Design and Analysis MPC MSG-3 Maintenance Program Creation Lambda Predict Reliability Prediction RCM++ Reliability Centered Maintenance RENO for Risk Analysis via Discrete Event Simulation RGA Reliability Growth and Repairable System Analysis Weibull++ Reliability Life Data Analysis Xfmea FMEA and FMECA XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management ALTA Accelerated Life Testing Data Analysis BlockSim System Reliability and Maintainability Analysis DOE++ Experiment Design and Analysis MPC MSG-3 Maintenance Program Creation Lambda Predict Reliability Prediction RCM++ Reliability Centered Maintenance RENO for Risk Analysis via Discrete Event Simulation RGA Reliability Growth and Repairable System Analysis Weibull++ Reliability Life Data Analysis Xfmea FMEA and FMECA XFRACAS Web-based FRACAS Orion eAPI Web-based Asset Management    ReliaSoft.com Footer

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