http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/ The blog of John D. Cook Sat, 11 Feb 2012 22:42:11 -0500 http://wordpress.org/?v=2.8.4 hourly 1 http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/comment-page-1/#comment-48558 chuck Thu, 14 Oct 2010 15:11:35 +0000 http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/#comment-48558 So let me get this straight. If the plot is concave up, the y data is transformed using the bottom of the ladder. Then if it is concave down, y is transofrmed using the bottom of the ladder. If I'm thinking correctly, there is no way to know exactly which transformation to use for a set of data that needs to be transformed. We are working onthis in AP Stats now and alot of the plots we transform are indeed curved, but the correlation coefficient is .9+. Even if the correlation is high, I think that you still must linearize it if it visually does not look to be straight. I've been having trouble recently in statistics so I would really appreciate any help. So let me get this straight. If the plot is concave up, the y data is transformed using the bottom of the ladder. Then if it is concave down, y is transofrmed using the bottom of the ladder. If I’m thinking correctly, there is no way to know exactly which transformation to use for a set of data that needs to be transformed. We are working onthis in AP Stats now and alot of the plots we transform are indeed curved, but the correlation coefficient is .9+. Even if the correlation is high, I think that you still must linearize it if it visually does not look to be straight. I’ve been having trouble recently in statistics so I would really appreciate any help.

]]> http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/comment-page-1/#comment-25323 ciro romero Wed, 30 Sep 2009 12:42:37 +0000 http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/#comment-25323 I also write to your email, sorry for that. So you mean to plot the residual of all the transformations, and use the transformation in which the residuals look most normal?. So, as increases normality increases linearity as well?. What about using correlations. I mean, if the relationships are nonlinear, after transformations the correlations should improve. I am using path analysis; therefore, I need to work with linear relationships. Nonnormality is not a problem to me because I could fit the model with generalized least squares instead of maximum likelihod. Regards, and thanks a lot for your help. I also write to your email, sorry for that. So you mean to plot the residual of all the transformations, and use the transformation in which the residuals look most normal?. So, as increases normality increases linearity as well?. What about using correlations. I mean, if the relationships are nonlinear, after transformations the correlations should improve. I am using path analysis; therefore, I need to work with linear relationships. Nonnormality is not a problem to me because I could fit the model with generalized least squares instead of maximum likelihod. Regards, and thanks a lot for your help.

]]> http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/comment-page-1/#comment-25290 John Tue, 29 Sep 2009 20:05:56 +0000 http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/#comment-25290 @ciro: You just have to plot the residuals and see which transformation makes the residuals look most normal. You could do a test for normality, but that's overkill. This sort of thing isn't rigorously justified to begin with, so there's no point getting too fussy about it. @ciro: You just have to plot the residuals and see which transformation makes the residuals look most normal. You could do a test for normality, but that’s overkill. This sort of thing isn’t rigorously justified to begin with, so there’s no point getting too fussy about it.

]]> http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/comment-page-1/#comment-25289 ciro Tue, 29 Sep 2009 19:36:06 +0000 http://www.johndcook.com/blog/2008/03/07/how-to-linearize-data-for-regression/#comment-25289 How to know was transformation was the best? Can I use correlation to decide which transformation was better? How to know was transformation was the best? Can I use correlation to decide which transformation was better?

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