Statistics Toolbox

Hypothesis Testing

Random variation often makes it difficult to determine whether samples taken under different conditions really are different. Hypothesis testing is an effective tool for analyzing whether sample-to-sample differences are significant and require further evaluation or are consistent with random and expected data variation.

Statistics Toolbox supports the most widely used parametric and nonparametric hypothesis testing procedures, such as:

• One- and two-sample t-tests
• One-sample z-test
• Nonparametric tests for one sample, paired samples, and two independent samples
• Distribution tests (Chi-square, Jarque-Bera, Lilliefors, and Kolmogorov-Smirnov)
• Comparison of distributions (two-sample Kolmogorov-Smirnov)
• Autocorrelation and randomness tests
• Linear hypotheses tests on regression coefficients

Statistical Process Control

Statistics Toolbox provides a set of functions that support Statistical Process Control (SPC). These functions enable you to monitor and improve products or processes by evaluating process variability. SPC functions let you:

• Perform gage repeatability and reproducibility studies
• Estimate process capability
• Create 11 control charts
• Apply Western Electric and Nelson control rules to control chart data

Control charts showing process data and violations of Western Electric control rules. Statistics Toolbox provides a variety of control charts and control rules for monitoring and evaluating products or processes.