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Statistics Toolbox™ 6.2

Product Description

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
Empirical cumulative distribution functions for two samples. Statistics Toolbox provides functions for graphing empirical distributions and testing them for equality. Click on image to see enlarged view.

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 eleven different control charts
  • Apply Western Electric and Nelson control rules to control chart data
Control charts showing process data and violations to Western Electric control rules. Statistics Toolbox provides a variety of control charts and control rules for monitoring and evaluating products or processes. Click on image to see enlarged view.
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