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.
- Design of Experiments (DOE)
Planning experiments with systematic data collection
- Analysis of Lifetime Data
Nonparametric and semiparametric methods for analyzing reliability and survival data
- Statistical Process Control
Statistical methods for quality control and production process monitoring