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en:learning:schools:s01:worksheets:ba-ws-08-1 [2015/09/22 16:22] (current)
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 +====== W08-1 Non-linear prediction ======
 +This worksheet revisits the regression and prediction topic but this time from a non-linear point of view. After completing this worksheet you should know how to use local regression models for fitting relationships within your data sets.
 +===== Things you need for this worksheet =====
 +  * {{section>​en:​resources:​templates:​tools#​R environment&​inline}}
 +  * {{section>​en:​resources:​templates:​tools#​R studio&​inline}}
 +  * your script and data from [[en:​learning:​schools:​s01:​worksheets:​ba-ws-02-1|W02-1:​ Reading CSV files]]
 +===== Learning log assignments =====
 +:!: First things first: the following analysis is build on top of your script from [[en:​learning:​schools:​s01:​worksheets:​ba-ws-02-1|W02-1]]. Please copy your script "​W02-1.R",​ rename the copy to "​W08-1.R"​ and use it for the programming tasks of this worksheet.
 +:-\ Please visualize once again the relation between animal activity and coverage. ​
 +:-\ Perhaps there are models that fit better than the linear regression! Let's try a polynomial regression using the loess() function. Add the prediction of the loess model in our scatterplot using the lines() function which works almost identical to regLine(). ​
 +:-\ Now let's check out how the loess model compares to the linear regression when it comes to predictions. Please compute a leave-one-out validation as in [[en:​learning:​schools:​s01:​worksheets:​ba-ws-06-1|W06-1]] but this time use the loess model. How do the error statistics compare to the linear prediction model?
en/learning/schools/s01/worksheets/ba-ws-08-1.txt ยท Last modified: 2015/09/22 16:22 (external edit)