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en:learning:schools:s01:lecture-notes:ba-ln-04

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 en:learning:schools:s01:lecture-notes:ba-ln-04 [2015/09/28 09:17]tnauss [Time for practice] en:learning:schools:s01:lecture-notes:ba-ln-04 [2017/10/30 10:15] (current)aziegler Both sides previous revision Previous revision 2017/10/30 10:15 aziegler 2015/09/28 09:17 tnauss [Time for practice] 2015/09/22 16:22 external edit 2017/10/30 10:15 aziegler 2015/09/28 09:17 tnauss [Time for practice] 2015/09/22 16:22 external edit Line 24: Line 24: ===== Regression analysis ===== ===== Regression analysis ===== ==== Scatterplots ==== ==== Scatterplots ==== - <​html><​a href="​https://​www.flickr.com/​photos/​bisfogo/​15231277763"​ target="​_blanck"​ title="​fc2002_prich_vs_elev by BIS-Fogo, on Flickr"><​img src="​https://​farm8.staticflickr.com/​7494/​15231277763_cbfa1081de_n.jpg" width="​320"​ height="​320"​ alt="​fc2002_prich_vs_elev">​ + {{ :en:​learning:​schools:​s01:​lecture-notes:​15231277763_cbfa1081de_n.jpg ​|}} Since linear models can only explain linear relationships and hence only make sense if such a relationship is feasible to explain the dependency between the dependent and one or more independent variables. Since eyeball analysis is very effective in estimating dependencies in the 2D space, plotting the dependent variable as a function of the independent variable should be a standard task in data analysis prior to fitting a model. Since linear models can only explain linear relationships and hence only make sense if such a relationship is feasible to explain the dependency between the dependent and one or more independent variables. Since eyeball analysis is very effective in estimating dependencies in the 2D space, plotting the dependent variable as a function of the independent variable should be a standard task in data analysis prior to fitting a model. Line 42: Line 42: ''​y = a x + b''​. ''​y = a x + b''​. - <​html>​ <​img src="​https://​farm8.staticflickr.com/​7552/​15825103696_514791d44d_n.jpg" width="​320"​ height="​320"​ alt="​fc2002_prich_vs_elev_lm">​ + {{ :en:​learning:​schools:​s01:​lecture-notes:​15825103696_514791d44d_n.jpg ​|}} One standard function used in R for computing linear regressions is the lm() function which allows both simple and multiple linear regression. A minimalistic call of the function looks like One standard function used in R for computing linear regressions is the lm() function which allows both simple and multiple linear regression. A minimalistic call of the function looks like 