 # SEMATECH e-Handbook of Statistical Methods Related:  DoER Resources

Design of Experiments (DOE) Tutorial Design of experiments (DOE) is a powerful tool that can be used in a variety of experimental situations. DOE allows for multiple input factors to be manipulated determining their effect on a desired output (response). By manipulating multiple inputs at the same time, DOE can identify important interactions that may be missed when experimenting with one factor at a time. When to Use DOE Use DOE when more than one input factor is suspected of influencing an output. DOE can also be used to confirm suspected input/output relationships and to develop a predictive equation suitable for performing what-if analysis. DOE Procedure Acquire a full understanding of the inputs and outputs being investigated. The negative effect of the interaction is most easily seen when the pressure is set to 50 psi and Temperature is set to 100 degrees. Conduct and Analyze Your Own DOE Conduct and analyze up to three factors and their interactions by downloading the 3-factor DOE template (Excel, 104 KB). Summary

ChemWiki: The Dynamic Chemistry E-textbook - Chemwiki Data Evaluation and Comparisons Introduction → Presentation of data comparison techniques, and the steps for evaluating set of data Hypotheses → Definition of statistical hypotheses about datasets t-tests → t-tests for comparing the means of different datasets One- & Two-tailed tests → Testing whether a mean is greater than, less than, or not equal to, another mean F-test → Testing differences between standard deviations of datasets, for comparing precision You have now seen how to generate a calibration curve for an instrument from a set of linear data, and then use the curve to determine the concentration of an unknown sample from a measured signal. Let's say you just taken a number of concentration readings from a sample of unknown concentration, and you want to determine whether the difference between your measured value and the stated value is statistically significant, or simply do to a random error. There are a few steps for evaluating a dataset or comparing multiple sets of data. © Dr.

Exploratory data analysis In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA. Overview Exploratory data analysis, robust statistics, nonparametric statistics, and the development of statistical programming languages facilitated statisticians' work on scientific and engineering problems. EDA development John W.