Cornerstone

Design of Experiments (DoE) is a structured approach to planning experiments to efficiently and effectively investigate the relationship between factors and the outcomes of interest.

DoE in Cornerstone supports advanced techniques like constraints in design space, inclusion runs, accessible candidate set for algorithmic designs, and methods to perform robust (Taguchi) or mixture designs.

helps you to extract the full potential of your data with interactive graphical tools like brushing and labeling. Cornerstone offers various basic and advanced graph types to adequately visualize your data.

is a statistical technique used to model the relationships between your dependent (responses) and independent variables (predictors). You can create and refine regression models that describe one or multiple responses as a function of one or more scaled or categorical predictors.

It supports arbitrary high order expansion, stepwise regression, Box-Cox transformation, and residual analysis in an unsurpassed compact workflow.

stands for Multivariate Analysis of Variance. It offers analysis techniques to compare groups based on multivariate data. You can investigate whether the groups are different and how the differences are determined by the analyzed variables. MANOVA can also be used for discriminant analysis.

is used to simplify and interpret complex datasets. It is particularly useful for reducing the dimensionality of data while retaining most of the variation present in the original dataset. It can also be combined with Regression Analysis in case of correlated predictors that otherwise cannot be adequately analyzed (Regression on principal components).

helps decision-makers understand the implications of uncertainty in models and make more informed decisions based on the sensitivity of outcomes (responses) to different factors (predictors).

In Cornerstone, the Sensitivity Analysis can be combined with Regression to enhance the interpretability, robustness, and reliability of the regression model.

are numerical values that summarize and describe the main features of a dataset. They include measures of central tendency (such as mean, median, and mode), measures of dispersion (such as range, variance, and standard deviation), and other descriptive metrics (such as quartiles and percentiles).

Test statistics are used to determine whether to reject the null hypothesis during hypothesis testing. Cornerstone supports many different situations to apply hypothesis tests. Besides, the results are informative and don’t require further interpretation of the values.

involves selecting a probability distribution that best describes the variability in the data generated by a process. Distribution Fitting helps in understanding the underlying probability distribution of process data, which is crucial for process control, prediction, and decision-making.

Process Capability analysis assesses the ability of a process to produce output within specified tolerance limits. By combining distribution fitting with process capability analysis, you can better understand the variability in your processes and assess the ability to meet specifications, thereby facilitating continuous improvement efforts and enhancing overall quality performance.

are graphical tools used in Statistical Process Control (SPC) to monitor and analyze the stability and performance of a process over time. As an integral component of the Six Sigma methodology, control charts play a vital role in overseeing and enhancing processes.

By utilizing these concepts of process improvement, the approach intentionally targets the tasks of engineers to ensure effective implementation and sustained progress

grants access to the R environment, famous for statistical computing and graphics. This synergy empowers users to leverage cutting-edge statistical methods alongside powerful visualizations (brushing) for enhanced data analysis.

Explore the R integration in Cornerstone, here.