Abstract: Disclosed is a computer-implemented method of generating a plurality of selectable design experiments in a design of experiments (DOE) process for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method entails receiving as user input into a computing apparatus, a base number of trials and a number of center point trials. From the computing apparatus, a graphical user interface is generated on a display connected with the computing apparatus. This includes presenting a display containing a plurality of experiment each defined, at least partially, by a combination of number of replicates (replicates count) and effect size. The display further includes a predicted power value for each combination of replicates count and effect size.

Abstract: A method of automatically analyzing data from at least one data set including a plurality of process factors of interest and a process output of interest to determine the relationship between the factors of interest and the output of interest at a given significance level and preserving model hierarchy. The method includes the steps of calculating the effects of the factors of interest against the process output of interest, developing a model including the significant factors of interest and respective estimated coefficients and omitting the insignificant factors of interest, generating a representation of the model, and generating at least one graphical representation of the factors of interest.

Abstract: Disclosed is a computer-implemented method of generating a plurality of selectable design experiments in a design of experiments (DOE) process for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method entails receiving as user input into a computing apparatus, a base number of trials and a number of center point trials. From the computing apparatus, a graphical user interface is generated on a display connected with the computing apparatus. This includes presenting a display containing a plurality of experiment each defined, at least partially, by a combination of number of replicates (replicates count) and effect size. The display further includes a predicted power value for each combination of replicates count and effect size.

Abstract: A computing system is disclosed having one or more processors, a display, and a memory coupled to the one or more processors. In this computing system, the memory stores program instructions executable by the one or more processors to display a graphical user interface on the display. The graphical user interface includes at least one user interface data object associated with a stored data set and displaying visual attributes corresponding to properties of the data set, including an attribute corresponding to data type.

Abstract: A method of automatically designing a plurality of experiments for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method uses a computer to elicit input from a user to determine at least one characteristic of the data set including a quantity of the plurality of factors and whether one or more of the plurality of factors has greater than two levels, selects a design from a plurality of experiment designs based on established conventions for each of the plurality of experiment designs, the design applicable to the elicited at least one characteristic of the data set, and provides a design of experiments including a standard order of the experiments and a run order of the experiments, the design of experiments indicating the combinations of factors and levels for each experiment.

Abstract: A method of automatically designing a plurality of experiments for analyzing at least one data set from a process to determine a relationship of a plurality of process factors of interest to a process output of interest. The method uses a computer to elicit input from a user to determine at least one characteristic of the data set including a quantity of the plurality of factors and whether one or more of the plurality of factors has greater than two levels, selects a design from a plurality of experiment designs based on established conventions for each of the plurality of experiment designs, the design applicable to the elicited at least one characteristic of the data set, and provides a design of experiments including a standard order of the experiments and a run order of the experiments, the design of experiments indicating the combinations of factors and levels for each experiment.

Abstract: A method of automatically analyzing data from at least one data set including a plurality of process factors of interest and a process output of interest to determine the relationship between the factors of interest and the output of interest at a given significance level and preserving model hierarchy. The method uses a computer to carry out the steps of calculating the effects of the factors of interest against the process output of interest, developing a model including the significant factors of interest and respective estimated coefficients and omitting the insignificant factors of interest, generating a representation of the model, and generating at least one graphical representation of the factors of interest. The graphical representation provides a first representation corresponding to factors of interest identified as significant and a second representation corresponding to factors of interest identified as insignificant.