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Autocorrelation: How to Manage it

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When a process variable has only random variation, each output is independent of the previous ones.This is what happens in a lottery. In some processes this independence does not happen.   If we control our daily weight, for instance, our weight today is correlated to the weight of the previous days: it has autocorrelation . Weight Autocorrelation One common case of autocorrelation is shown by our body weight.  Our weight today is correlated with: Yesterday's weight:      53% The day before:           39%    Body weight has inertia: you don't expect sudden changes. A similar effect happens when you control a heavy aircraft or a ship: its masive weight prevents you from making a sharp turning or make a sudden stop.  This opposing force to change is what is called Inertia . This inertia definition applies to moving objects and it is proportional to the object mass.  Inertia also applies to fluids: a tank accumulating a fluid will also have this inertia effect. If we try to con

Natural Gas Demand Forecast

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Natural gas demand is highly seasonal, therefore forecasting the final consumption is essential to manage the complete supply chain. Temperature is a main factor affecting home gas consumption for heating. We will analyse the correlation between gas consumption and temperature. Download this Excel file with examples to your PC from OneDrive: Gas Forecast.xlsx   This chart of local consumption during a winter month, leads us to believe that one main factor in consumption is ambient temperature. This may be due to its wide use for heating. We can think of other factors that may affect consumption such as the day of the week so we will analyse this actual consumption data with these two possible factors.  We obtain the day of the week with an Excel formula from the date. We can get the average temperatures of the corresponding geographical area during this period from AEMET (Agencia Estatal de Meteorología) in aemet.es Day of the Week Calculation We obtain the day of the week with this Ex

Multiple Response Optimization with Design of Experiments (DOE)

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  Design of experiments, is a systematic experimentation, with a process critical factors, in order to correlate these factors to process responses. In DOE with Excel we obtained a linear relationship, between several factors and one response.  In Response Surface DOE  this was extended to non-linear relations. In both cases we were optimizing one single response. Now we will analyse some cases where more than one response needs to be optimized. Problem Description Download file Multiple Response.xlsm from OneDrive to your PC. We want to maximize yield and minimize cost in a process where we have identified three critical factors which may affect both. These are the factors and levels we want to experiment with: Full Factorial DOE We start with a full factorial with two central points DOE and run a simulation of the experiments in sheet YieldCost Simul  we then add the interaction columns (green headers) for the analysis: We now use Excel Data Analysis > Regression to analyse Y