This is machine translation

Translated by Microsoft
Mouseover text to see original. Click the button below to return to the English version of the page.

Note: This page has been translated by MathWorks. Click here to see
To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Industrial Statistics

Design of experiments (DOE); survival and reliability analysis; statistical process control

Statistics and Machine Learning Toolbox™ provides tools for designing experiments, analyzing reliability and survival data, process quality control, and data surveillance.

Design of experiments helps determine how certain factors impact the outcome (response) of a process. You can design experiments including full and fractional factorial, D-optimal, quasi-random, and response surface designs, or visualize experiment results.

Survival analysis studies the time until an event occurs. Visualize and estimate parameters, compute survival and hazard functions, and fit semi-parametric models to censored or uncensored lifetime data.

Statistical process control techniques monitor and assess the quality of industrial processes. Measure process capability, perform gage repeatability and reproducibility study, and monitor process data using control charts.