| | | You can use the toolbox to define, analyze, and visualize a customized DOE. For example, you can estimate input effects and input interactions using ANOVA, linear regression, and response surface modeling, and visualize results through main effect plots, interaction plots, and multi-vari charts. 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 | | |