This set of codes is a collection of functions which wrap around the core packages (mentioned below) and generate design-of-experiment (DOE) matrices for a statistician or engineer from an arbitrary range of input variables. There are a couple of DOE builder Python packages but individually they don’t cover all the necessary DOE methods and they lack a simplified user API, where one can just input a CSV file of input variables’ range and get back the DOE matrix in another CSV file. However, a researcher will surely be benefited if there exists an open-source code which presents an intuitive user interface for generating an experimental design plan from a simple list of input variables. Unfortunately, majority of the state-of-the-art DOE generators are part of commercial statistical software packages like JMP (SAS) or Minitab. Need for careful design of experiment arises in all fields of serious scientific, technological, and even social science investigation - computer science, physics, geology, political science, electrical engineering, psychology, business marketing analysis, financial analytics, etc… Options for open-source DOE builder package in Python? Related concerns include achieving appropriate levels of statistical power and sensitivity. For example, these concerns can be partially addressed by carefully choosing the independent variable, reducing the risk of measurement error, and ensuring that the documentation of the method is sufficiently detailed. Main concerns in experimental design include the establishment of validity, reliability, and replicability. There are multiple approaches for determining the set of design points (unique combinations of the settings of the independent variables) to be used in the experiment. What is Experimental Design?Įxperimental design involves not only the selection of suitable independent, dependent, and control variables, but planning the delivery of the experiment under statistically optimal conditions given the constraints of available resources. In its simplest form, a scientific experiment aims at predicting the outcome by introducing a change of the preconditions, which is represented by one or more independent variables, also referred to as “input variables” or “predictor variables.” The change in one or more independent variables is generally hypothesized to result in a change in one or more dependent variables, also referred to as “output variables” or “response variables.” The experimental design may also identify control variables that must be held constant to prevent external factors from affecting the results. And to do good science with data, one needs to collect it through carefully thought-out experiment to cover all corner cases and reduce any possible bias. A well-planned DOE can give a researcher meaningful data set to act upon with optimal number of experiments preserving critical resources.Īfter all, aim of Data Science is essentially to conduct highest quality scientific investigation and modeling with real world data. This exercise has become critical in this age of rapidly expanding field of data science and associated statistical modeling and machine learning.
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