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 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 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: 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: 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.