Jupyter Notebooks exploring empirical modeling and experimental design using Python.
Jupyter Notebook: Covers a start-to-finish analysis of the results of a factorial experimental design. Light on graphics, heavy on Pandas and Itertools libraries.
Jupyter Notebook: Dives into the details of a more complicated factorial design: with six input variables and three output variables, this analysis shifts toward using better tools (like the statsmodel library) to sift through the information without getting overwhelmed. Uses Seaborn to make pretty plots!
Jupyter Notebook: A brief analysis of effects, computed from a four-factor factorial design. Illustrates the use of quantile plots to identify the variables to which the system response is most sensitive.
Jupyter Notebook: A brief analysis of how to fractionate a full two-level factorial design.