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Statistics with Matlab. Hypothesis Tests, Analysis of Variance and Design of Experiments

Statistics with Matlab. Hypothesis Tests, Analysis of Variance and Design of Experiments

          
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About the Book

Hypothesis testing is a common method of drawing inferences about a population based on statistical evidence from a sample. For example, the z-test (ztest) and the t-test (ttest) both assume that the data areindependently sampled from a normal distribution. Statistics and Machine LearningToolbox functions are available for testing this assumption, such as chi2gof, jbtest, lillietest, and normplot. You can use the Statistics and Machine Learning Toolbox function anova1 to perform one-way analysis of variance (ANOVA). The purpose of one-way ANOVA is to determine whether data from several groups (levels) of a factor have a common mean. That is, oneway ANOVA enables you to find out whether different groups of an independent variable have different effects on the response variable y. You can use the Statistics and Machine Learning Toolbox function anova2 to perform a balanced two-way analysis of variance (ANOVA). To perform two-way ANOVA for an unbalanced design, use anovan. The Statistics and Machine Learning Toolbox function multcompare performs multiple pairwise comparison of the group means, or treatment effects. The options are Tukey's honestly significant difference criterion (default option), the Bonferroni method, Scheffe's procedure, Fisher's least significant differences (lsd) method, and Dunn & Sidak's approach to t-test. You can use the Statistics and Machine Learning Toolbox function anovan to perform Nway ANOVA. Use N-way ANOVA to determine if the means in a set of data differ with respect to groups (levels) of multiple factors. Traditional experimental designs ("Full Factorial Designs," "Fractional Factorial Designs," and "Response Surface Designs") are appropriate for calibrating linear models in experimental settings where factors are relatively unconstrained in the region of interest. In some cases, however, models are necessarily nonlinear. In other cases, certain treatments (combinations of factor levels) may be expensive or infeasible to measure. D-optimal designs are model-specific designs that address these limitations of traditional designs. In practice, you may want to add runs to a completed experiment to learn more about a process and estimate additional model coefficients. The daugment function uses a coordinate-exchange algorithm to augment an existing D-optimal design. MATLAB shows how to improve the performance of an engine cooling fan through a Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC). Statistical process control (SPC) refers to a number of different methods for monitoring and assessing the quality of manufactured goods. Combined with methods from the design of experiments, SPC is used in programs that define, measure, analyze, improve, and control development and production processes. These programs are often implemented using "Design for Six Sigma" methodologies. This bok develops hypothesis test, ANOVA models, ANCOVA models, MANOVA models and MANCOVA models. It also develops Traditional experimental designs ("Full Factorial Designs," "Fractional Factorial Designs," and "Response Surface Designs") and D-Optimal designs. Also improve Design for Six Sigma approach using Define, Measure, Analyze, Improve, and Control (DMAIC). Finaly, he book develops Statistical process control (SPC) implemented using "Design for Six Sigma" methodologies.


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Product Details
  • ISBN-13: 9781979352321
  • Publisher: Createspace Independent Publishing Platform
  • Publisher Imprint: Createspace Independent Publishing Platform
  • Language: English
  • ISBN-10: 1979352321
  • Publisher Date: 16 Dec 2018
  • Binding: Paperback


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