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  "body": "\n## Summary\n\nResponse surface methodology is a set of statistical techniques for learning how a response depends on several input factors.\nIts goal is to find the factor settings that make the response best.\nIt fits low-order polynomial models to data from designed experiments and moves through the factor space in a sequence of experiments.\nGeorge Box and K. B. Wilson introduced it in 1951, and it is widely used to improve industrial processes.\n\n## Explanations\n\n### L1 human\n\nThink of the response, such as the yield of a chemical process, as an unknown hilly landscape over the settings of temperature and pressure.\nA few runs near the current settings show which way is uphill, and a flat plane fitted to them gives the direction to walk.\nNear the top the landscape bends, so a quadratic surface is fitted instead and its peak is computed.\nFor example, if the fitted surface near the top is $y = 80 - (x_1 - 1)^2 - 2 (x_2 + 0.5)^2$, the best settings are $x_1 = 1$ and $x_2 = -0.5$.\n",
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