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  2. Median polish - Wikipedia

    en.wikipedia.org/wiki/Median_polish

    The median polish is a simple and robust exploratory data analysis procedure proposed by the statistician John Tukey. The purpose of median polish is to find an additively-fit model for data in a two-way layout table (usually, results from a factorial experiment ) of the form row effect + column effect + overall median.

  3. Auto detailing - Wikipedia

    en.wikipedia.org/wiki/Auto_detailing

    Detailing is more than a cleaning process to make a vehicle look good; it is a systematic approach to help extend its life with methods and products that reduce damaging environmental elements such as dirt, sun, harsh winters, etc. [3] Appropriate maintenance or restoration of vehicles to keep them looking outside and inside as if they came from an auto dealer's showroom increase their resale ...

  4. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints. [ 4 ] [ 5 ] Curve fitting can involve either interpolation , [ 6 ] [ 7 ] where an exact fit to the data is required, or smoothing , [ 8 ] [ 9 ] in which a "smooth ...

  5. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness of fit typically summarize the discrepancy between observed values and the values expected under the model in question.

  6. Kernel smoother - Wikipedia

    en.wikipedia.org/wiki/Kernel_smoother

    The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).

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  8. Autoregressive moving-average model - Wikipedia

    en.wikipedia.org/wiki/Autoregressive_moving...

    It is good practice to find the smallest values of p and q which provide an acceptable fit to the data. For a pure AR model, the Yule-Walker equations may be used to provide a fit. ARMA outputs are used primarily to forecast (predict), and not to infer causation as in other areas of econometrics and regression methods such as OLS and 2SLS.

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