Abstract: A system, method, and computer-program product includes displaying a plurality of factor-setting user interface (UI) control elements configured to receive an input of characters for specifying a set of design of experiment factors for creating a design of experiment (DOE), displaying a plurality of factor type UI control elements configured to receive input for specifying a factor type of a plurality of factor types, displaying a plurality of dynamic rows of editable UI control elements configured to receive inputs of experimental values for the set of DOE factors, and displaying a composite factor UI control component configured to receive inputs for generating one or more control signals that add or remove one or more DOE factors of the set of DOE factors.

Abstract: A computing device receives a target value for a design quality metric. The target value indicates a desired quality of a design of an experiment and is related to an input parameter by a response curve. The computing device also validates the target value as being in a feasibility range for the design quality metric, and if so, determines a candidate value for the input parameter that yields a calculated value for the design quality metric. To determine the calculated value, the computing device iteratively adjusts the candidate value until the calculated value is within a predetermined tolerance of the target value. The computing device then updates an interactive graph visually representing the calculated value for the design quality metric as a function of the candidate value for the input parameter and outputs a graph visually representing the relationship that exists between the candidate value and the calculated value.

Abstract: A computing device receives a target value for a design quality metric. The target value indicates a desired quality of a design of an experiment and is related to an input parameter by a response curve. The computing device also validates the target value as being in a feasibility range for the design quality metric, and if so, determines a candidate value for the input parameter that yields a calculated value for the design quality metric. To determine the calculated value, the computing device iteratively adjusts the candidate value until the calculated value is within a predetermined tolerance of the target value. The computing device then updates an interactive graph visually representing the calculated value for the design quality metric as a function of the candidate value for the input parameter and outputs a graph visually representing the relationship that exists between the candidate value and the calculated value.

Abstract: A computing device receives user input for a design of an experiment. The user input indicates respective factor identities for factors in the design of the experiment, respective response identities for responses to options for the factors in the design, and a user-defined objective for the one or more responses. Additionally, based on the user input, the computing device displays a subset of a set of multiple model types and a user-definable amount of design runs for the design. Each design run presents settings according to the design for each of the factors. The computing device also receives settings indicating a user-selected model type from the subset and a user-defined amount of design runs, and based on the settings, selects one or more design construction criteria for generating the design. The computing device then generates the design according to the design construction criteria.

Abstract: A computing device obtains first and second factors for a nesting design and first and second sets of candidate options for the first and second factors, respectively. The computing device also receives a nesting indication that the second factor is nested within the first factor. The computing device generates the nesting design by portioning the first and second sets of candidate options into respective first and second different sets of groups. Based on the nesting indication, the computing device generates an assignment of groups for a design, and also selects options in the nesting design for the first and second factors based on the assignment of groups. The computing device also generates terms for a computer model for a computer simulation of the nesting design, as well as one or more computer-generated assessments of the nesting design based on the computer simulation.

Abstract: A computing device obtains settings for a design of an experiment. The settings include a first sample size that indicates an amount of members for a first group observed as part of the experiment, a design quality metric that assesses, based on the first sample size, a quality of the design for the experiment prior to conducting the experiment, and a parameter pertaining to the first group. The computing device also displays first and second graphs of the design quality metric on a graphical user interface. The first graph is displayed as a function of the first sample size and the second graph is displayed as a function of the parameter pertaining to the first group. The computing device also receives a first user indication to change one or more of the settings, and then updates both the first and second graphs to account for the first user indication.

Abstract: A computing device obtains settings for a design of an experiment. The settings include a first sample size that indicates an amount of members for a first group observed as part of the experiment, a design quality metric that assesses, based on the first sample size, a quality of the design for the experiment prior to conducting the experiment, and a parameter pertaining to the first group. The computing device also displays first and second graphs of the design quality metric on a graphical user interface. The first graph is displayed as a function of the first sample size and the second graph is displayed as a function of the parameter pertaining to the first group. The computing device also receives a first user indication to change one or more of the settings, and then updates both the first and second graphs to account for the first user indication.

Abstract: An apparatus includes a processor to: generate variants of an experiment design based on varied parameters; for each variant, estimate terms based on the model, and derive an optimality value; present a table of the variants including a column for each varied parameter and a column for the optimality value, a row for each variant, and a bar graph for each column depicting a distribution of the values therein; present function controls operable to select a function to perform on row(s) of the table in response to selection of a bar of a bar graph of a column; in response to selection of a function, change the current function to the selected function; and in response to a selection of a bar of a bar graph of a column, perform the current function on row(s) based on instances of the value associated with selected bar.

Abstract: An apparatus includes a processor to: present prompts guiding selection of first subset terms for estimation of variants of an experiment design based on a model thereof, and second subset terms for estimation using Bayesian modification; monitor for selections of term; in response to each change in the first subset terms, determine whether all first subset terms can be estimated based on the model; in response to the first subset including too many terms, present an indication of too many terms being selected for estimation based on the model; in response to the first subset including fewer terms than can be estimated based on the model, present an indication that more terms could be selected for estimation based on the model; and for each variant of the experiment design, estimate each first subset term based on the model, and estimate each second subset term using Bayesian modification.

Abstract: An apparatus includes a processor to: present prompts guiding selection of first subset terms for estimation of variants of an experiment design based on a model thereof, and second subset terms for estimation using Bayesian modification; monitor for selections of term; in response to each change in the first subset terms, determine whether all first subset terms can be estimated based on the model; in response to the first subset including too many terms, present an indication of too many terms being selected for estimation based on the model; in response to the first subset including fewer terms than can be estimated based on the model, present an indication that more terms could be selected for estimation based on the model; and for each variant of the experiment design, estimate each first subset term based on the model, and estimate each second subset term using Bayesian modification.

Abstract: An apparatus includes a processor to: generate variants of an experiment design based on varied parameters; for each variant, estimate terms based on the model, and derive an optimality value; present a table of the variants including a column for each varied parameter and a column for the optimality value, a row for each variant, and a bar graph for each column depicting a distribution of the values therein; present function controls operable to select a function to perform on row(s) of the table in response to selection of a bar of a bar graph of a column; in response to selection of a function, change the current function to the selected function; and in response to a selection of a bar of a bar graph of a column, perform the current function on row(s) based on instances of the value associated with selected bar.

Abstract: A computing device receives a request for a design of a combinatorial test for a test system. The device receives a run indication of a total quantity of test cases for the design, a factor indication of a total quantity of factors, and/or a strength indication for a covering array. The device generates an updated design by: selecting test case(s) to remove from a first design; or adding test case(s) to the first design. The first design comprises a set of test cases that represent the covering array according to the strength indication. The updated design is constrained to the total quantity of test cases as indicated by the run indication. The device outputs a respective setting for each test condition for at least one test case of the updated design for testing the test system.

Abstract: A computing device receives a request for a design of a combinatorial test for a test system. The device receives a run indication of a total quantity of test cases for the design, a factor indication of a total quantity of factors, and/or a strength indication for a covering array. The device generates an updated design by: selecting test case(s) to remove from a first design; or adding test case(s) to the first design. The first design comprises a set of test cases that represent the covering array according to the strength indication. The updated design is constrained to the total quantity of test cases as indicated by the run indication. The device outputs a respective setting for each test condition for at least one test case of the updated design for testing the test system.

Abstract: A computing system receives a request for computer-generated likelihood(s) for candidate evaluations of an industrial product set according to a measurement system analysis (MSA). The MSA comprises tests for evaluating, according to a measurement standard, the industrial product set. The request indicates a metric set representing metric(s) each quantifying an estimate of contribution to variation in evaluating the industrial product set according to the MSA. The system generates a design comprising a respective input set for each test of the MSA. The respective input set comprises setting(s) for conducting a test of the MSA. The design is designed to isolate candidate sources for contributing to the variation in evaluating the industrial product set according to the MSA. The system (e.g., prior to the MSA) outputs, based on the metric set and the design, the computer-generated likelihood(s) for the candidate evaluations of the industrial product set according to the MSA.

Abstract: A computing system receives a request for computer-generated likelihood(s) for candidate evaluations of an industrial product set according to a measurement system analysis (MSA). The MSA comprises tests for evaluating, according to a measurement standard, the industrial product set. The request indicates a metric set representing metric(s) each quantifying an estimate of contribution to variation in evaluating the industrial product set according to the MSA. The system generates a design comprising a respective input set for each test of the MSA. The respective input set comprises setting(s) for conducting a test of the MSA. The design is designed to isolate candidate sources for contributing to the variation in evaluating the industrial product set according to the MSA. The system (e.g., prior to the MSA) outputs, based on the metric set and the design, the computer-generated likelihood(s) for the candidate evaluations of the industrial product set according to the MSA.

Abstract: A computing system receives a request to generate computer-generated data for an experiment. The computer-generated data comprises generated inputs defining setting(s) for a plurality of factors for a design of the experiment. The generated inputs are generated to be representative of a respective design space of different design spaces for the design of the experiment. The system receives first characteristic(s) for specifying generation of the computer-generated data associated with a first design space. The system receives second characteristic(s) for specifying generation of the computer-generated data associated with a second design space. The system, responsive to the request, generates a design suite that comprises the computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by the first design space, and represents, in a second set of design cases of the design suite, settings constrained by the second design space.

Abstract: A computing system receives a request for a computer-generated design of an experiment. A design space is defined by candidate inputs for each factor of the experiment. The system receives a disallowed combination indication indicating a user-defined disallowed combination for the design space. The disallowed combination constrains a first set of values from a first set of candidate inputs from being assigned to the first factor if the second factor is assigned one of a second set of values from a second set of candidate inputs. The system determines additional constraint(s) on computer generation of the design. The system evaluates if a user-defined model can be generated according to the disallowed combination and the constraint(s). The system generates a computer suggested modification of the user-defined model such that the design can be generated according to the computer suggested modification.

Abstract: A computing system determines a design space for designing a design system. The computing system receives a request to identify input(s) that will provide a response of the design system that advances a user-defined goal for the design system. The computing system, responsive to the request to identify input(s), generates a design for the design space that provides design cases for generating the response of the design system and obtains responses of the design system generated for multiple design cases of the design for the design system. The computing system selects at least one design case based on obtained responses of the design system for the multiple design cases, and based on the at least one design case, outputs an indication of suggested input(s) for the design system. The suggested input(s) advances the user-defined goal for the design system and is not disallowed according to disallowed combination(s).

Abstract: A computing system receives a request to generate computer-generated data for an experiment. The computer-generated data comprises generated inputs defining setting(s) for a plurality of factors for a design of the experiment. The generated inputs are generated to be representative of a respective design space of different design spaces for the design of the experiment. The system receives first characteristic(s) for specifying generation of the computer-generated data associated with a first design space. The system receives second characteristic(s) for specifying generation of the computer-generated data associated with a second design space. The system, responsive to the request, generates a design suite that comprises the computer-generated data that represents, in a first set of design cases of the design suite, settings constrained by the first design space, and represents, in a second set of design cases of the design suite, settings constrained by the second design space.

Abstract: A computing device receives a request for a design of an experiment. The device generates a data representation of a matrix Y that defines a supersaturated design for the design of the experiment. The generating the data representation is by: generating a data representation of a matrix X according to an obtained design; computing an indication of correlation between effects of factors of a matrix Y; and generating, based on the indication of correlation, the data representation of the matrix Y that is the transposition of the matrix X or is the transposition of a matrix X*. The matrix X* is a first subset of the matrix X such that the transposition of the matrix X* represents a same number of factors as the transposition of the matrix X. The device outputs a setting for each test condition of the supersaturated design for the experiment.