Polyhedrika

Design of Experiments (DOE) is a very useful process improvement methodology.  Microsoft Excel has some powerful data analysis tools, which I have, successfully, used for DOE. When it comes to analyzing cause and effect, we want to know what process factors affect our outputs.   Correlation and Regression could give us an indication of the main factors, and the extent they affect the outputs. In some cases, there may be interactions among the different factors, or the mathematical relation between factors and outputs may not be linear. In these cases, it is useful to run a systematic set of experiments, to test all possible combinations of factors, to relate them to the outputs. Factorial Experiment Example We want to minimize process loss, and after some brainstorming among the process specialists, we concluded that 5 factors may affect loss. Based on current factor levels, we have selected the following levels to experiment with:    Download this Excel file DOE_with_Excel.xlsm fro

  Correlation is a mutual relationship or connection between two or more continuous variables. Regression is a mathematical model to define that relationship. Process Data Analysis analyzed transfer functions Y = f ( X ) where X was an attribute. Now we will analyze the case of X being a variable. Download Excel file Regression.xlsx from OneDrive to your PC to run the following examples. Correlation We have collected Natural Gas Demand data in sheet Correlation : This is actual daily demand during the month of January and the average local temperature recorded on those days.  From the date time stamp we have computed the day of the week (1 being Monday).  We are looking for factors that may affect demand and two possible factors may be Temperature and DOW. We will use Excel Data Analysis: Correlation Results: We detect a negative ( - 0.85 ) significant correlation between demand and temperature: the lower the temperature the higher the natural gas demand. This is what we would expec