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 en:learning:schools:s01:worksheets:ba-ws-08-1 [2015/09/22 16:22] en:learning:schools:s01:worksheets:ba-ws-08-1 [2015/09/22 16:22] (current) Line 1: Line 1: + ====== 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?