Random variation can make it difficult to determine whether samples taken under different conditions are actually 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 widely used parametric and nonparametric hypothesis testing procedures, including:
Selecting a Sample Size (Example)
Calculate the sample size necessary for a hypothesis test.
Functions for design of experiments (DOE) enable you to create and test practical plans to gather data for statistical modeling. These plans show how to manipulate data inputs in tandem to generate information about their effect on data outputs. Supported design types include:
You can use Statistics 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, then visualize results through main effect plots, interaction plots, and multivari charts.
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. With SPC functions, you can: