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en:learning:schools:s01:lecture-notes:ba-ln-08 [2015/09/28 09:17]
tnauss [Time for practice]
en:learning:schools:s01:lecture-notes:ba-ln-08 [2017/10/30 10:22] (current)
aziegler
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 While the linear regression models assume a linear relationship between a dependent (e.g. y) and one ore more independent variables (e.g. x), non-linear models do not have this restriction. As a drawback, non-linear models can be quite complicated to define if one is looking for a non-linear model which describes the entire data set but local regression models do not have this drawback. While the linear regression models assume a linear relationship between a dependent (e.g. y) and one ore more independent variables (e.g. x), non-linear models do not have this restriction. As a drawback, non-linear models can be quite complicated to define if one is looking for a non-linear model which describes the entire data set but local regression models do not have this drawback.
  
-<​html><​a href="​https://​www.flickr.com/​photos/​bisfogo/​15248487043"​ target="​_blanck"​ title="​fc2002_prich_vs_elev_loess by BIS-Fogo, on Flickr"><​img src="​https://​farm8.staticflickr.com/​7521/​15248487043_47a0b90844_n.jpg" width="​320"​ height="​320"​ alt="​fc2002_prich_vs_elev_loess"></​a></​html>​ +{{ :en:​learning:​schools:​s01:​lecture-notes:​15248487043_47a0b90844_n.jpg ​|}}
 Non-parametric local regression models (loess) use simple linear or quadratic models (i.e. polynomial functions of first or second degree) which are used for a local weighted fit on the data set using a moving window. For example, if the span of this window is set to 7, then only the neighboring 3 lower and higher values are considered for the fit of the central value. A cubic (i.e. x<​sup>​3</​sup>​) weighing function ensures that the actual central value has the largest influence on the fit with decreasing weights towards the end of each sides span. Non-parametric local regression models (loess) use simple linear or quadratic models (i.e. polynomial functions of first or second degree) which are used for a local weighted fit on the data set using a moving window. For example, if the span of this window is set to 7, then only the neighboring 3 lower and higher values are considered for the fit of the central value. A cubic (i.e. x<​sup>​3</​sup>​) weighing function ensures that the actual central value has the largest influence on the fit with decreasing weights towards the end of each sides span.
  
en/learning/schools/s01/lecture-notes/ba-ln-08.txt · Last modified: 2017/10/30 10:22 by aziegler