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: A method of automatically applying a hypothesis test to a data set. The method reduces errors made in failing to appreciate predicate assumptions of various statistical tests, and elicits a series of indications from the user regarding characteristics of interest embodied by the data set to select an appropriate statistical test. The system also reduces errors in constructing competing null and alternative hypothesis statements by generating a characterization of the data and defining null and alternative hypotheses according to the indications, selected statistical test, and conventions adopted with respect to the tests. The system also establishes a significance level, calculates the test statistic, and generates an output. The output of the system provides a plain interpretation of the quantitative results in the terms indicated by the user to reduce errors in interpretation of the conclusion.

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 applying a hypothesis test to a data set. The method reduces errors made in failing to appreciate predicate assumptions of various statistical tests, and elicits a series of indications from the user regarding characteristics of interest embodied by the data set to select an appropriate statistical test. The system also reduces errors in constructing competing null and alternative hypothesis statements by generating a characterization of the data and defining null and alternative hypotheses according to the indications, selected statistical test, and conventions adopted with respect to the tests. The system also establishes a significance level, calculates the test statistic, and generates an output. The output of the system provides a plain interpretation of the quantitative results in the terms indicated by the user to reduce errors in interpretation of the conclusion.

Abstract: A method of automatically applying a hypothesis test to a data set. The method reduces errors made in failing to appreciate predicate assumptions of various statistical tests, and elicits a series of indications from the user regarding characteristics of interest embodied by the data set to select an appropriate statistical test. The system also reduces errors in constructing competing null and alternative hypothesis statements by generating a characterization of the data and defining null and alternative hypotheses according to the indications, selected statistical test, and conventions adopted with respect to the tests. The system also establishes a significance level, calculates the test statistic, and generates an output. The output of the system provides a plain interpretation of the quantitative results in the terms indicated by the user to reduce errors in interpretation of the conclusion.

Abstract: A method of automatically applying a hypothesis test to a data set. The method reduces errors made in failing to appreciate predicate assumptions of various statistical tests, and elicits a series of indications from the user regarding characteristics of interest embodied by the data set to select an appropriate statistical test. The system also reduces errors in constructing competing null and alternative hypothesis statements by generating a characterization of the data and defining null and alternative hypotheses according to the indications, selected statistical test, and conventions adopted with respect to the tests. The system also establishes a significance level, calculates the test statistic, and generates an output. The output of the system provides a plain interpretation of the quantitative results in the terms indicated by the user to reduce errors in interpretation of the conclusion.

Abstract: A method of automatically applying a hypothesis test to a data set. The method reduces errors made in failing to appreciate predicate assumptions of various statistical tests, and elicits a series of indications from the user regarding characteristics of interest embodied by the data set to select an appropriate statistical test. The system also reduces errors in constructing competing null and alternative hypothesis statements by generating a characterization of the data and defining null and alternative hypotheses according to the indications, selected statistical test, and conventions adopted with respect to the tests. The system also establishes a significance level, calculates the test statistic, and generates an output. The output of the system provides a plain interpretation of the quantitative results in the terms indicated by the user to reduce errors in interpretation of the conclusion.

Abstract: A method of simulating a process. Students are assigned a role related to the process and conduct interviews and investigations via electronic mail to document the process. During the simulation, the students learn the steps involved in the process, and the students learn what steps delay the process and identify the “choke” points in the process. With this information, the students can take action to alter and/or avoid those steps to improve the process.

Abstract: A method of displaying visual information to a user of a video display. The method includes defining a dynamic portion of the visual display, providing a mask that obscures the dynamic portion of the visual display for an observable period of time, and providing a second display of content to the dynamic portion of the visual display during the observable period of time, the second display of content being occluded by the animated content during the observable period of time.

Abstract: A method of, and tool for, enhancing a student's knowledge and skill in Six Sigma concepts. The tool is computer-based and enhances and evaluates a student's knowledge of and skill in the DMAIC process of Six Sigma. The tool allows the student to apply his newly-obtained knowledge of Six Sigma methodologies to a simulated real-world situation.

Abstract: A method of teaching a technique to a user. The technique includes a body of acts, steps, methods, rules, fundamentals, principles, cannons, etc. attempting to accomplish a desired aim. The method further includes teaching a lesson to a user and providing a computer-based game to the user to be played by the user. The computer-based game supports the teachings of the lesson. Also disclosed are methods of learning the technique.