Talk:Partition of sums of squares

message: This page is so narrowly focused as to be an embarrassment. I've added the expert needed tag -- don't have time to rewrite myself (way too many other stat-related pages need rewriting). In the meantime, anyone interested in really learning about sums-of-squares should fine a good book -- Scheffe mentioned above, books on experimental design or ANOVA, or to really understand the mathematics, books on the geometry of least squares and linear models.
--Zaqrfv (talk) 21:15, 13 August 2008 (UTC)
thread_title:  page needs a rewrite. 

message: Is there a name for the root sum of squares?  Like "geometric mean" except not that, obviously.  Geometric sum or something?  Is there a Wikipedia article on it?  —Preceding unsigned comment added by 71.167.61.18 (talk) 19:16, 4 November 2009 (UTC)
thread_title:  Root sum of squares 

message: i was not able to understand what is all about sum of square.....  —Preceding unsigned comment added by 121.54.117.246 (talk) 12:03, 17 February 2010 (UTC)
thread_title:  sum of squares 

message: This article could usefully be expanded to include a section on the matrix generalization of sums of squares to , the sums of squares and cross-products matrix. Or should that be a separate article? --Qwfp (talk) 11:45, 20 February 2011 (UTC)
thread_title: Sums of squares and cross-products matrix

message: :Feel free to state your position on the renaming proposal by beginning a new line in this section with *Support or *Oppose, then sign your comment with ~~~~. Since polling is not a substitute for discussion, please explain your reasons, taking into account Wikipedia's policy on article titles.
*Comment if moved, it definitely should be replaced by a disambiguation page. That hatnote is enormous. And there's least squares from the first section of this talk page, that is missing from the hatnote as well. Not to mention the method of integration... 65.93.15.125 (talk) 12:31, 25 February 2011 (UTC)
thread_title: Survey

message: The following parts must also be proven. They are unclear to many including me.
1. The requirement that the model includes a constant or equivalently that the design matrix contains a column of 1s ensures that Sigma1n Epsilonihat=0 
2. (For all j=1,...,p) Sigma1n EpsilonihatXij=0 212.174.38.3 (talk) 07:40, 21 November 2016 (UTC)
thread_title: Incomplete Proof of TSS=ESS+RSS 

message: According to this edit. Graham87 14:15, 9 September 2018 (UTC)
replies: text: No: p isn't the number of groups, it's the number of predictors, which is immaterial to the proof. The inclusion of p in the theorem is merely to be explicit about is meant by "a linear regression model ... including a constant". It could simply link to Linear regression#Introduction for the definition, at the expense of making this article less self-contained. Qwfp (talk) 19:36, 9 September 2018 (UTC)
thread_title:  the derivation is apparently "missing the number of groups p completely" 

message: wide overlap fgnievinski (talk) 22:35, 23 August 2022 (UTC)
replies: text: Agree with the sentiment, but oppose the merge on the grounds that the scope of the articles is sufficiently different to warrant separate discussion. Squared deviations from the mean focuss on applications in statistics, which are important, whereas Partition of sums of squares covers the topic more broadly, with examples from linear regression. There readership are different, and best served by having the pages kept separate. Klbrain (talk) 20:05, 9 March 2023 (UTC)
replies: text: Closing, given the uncontested objection and no support. Klbrain (talk) 12:19, 15 April 2023 (UTC)
thread_title:  Proposed merge of [[Squared deviations from the mean]] into [[Partition of sums of squares]] 

message: It is complete unclear to me why these sums should be 0
:2 \hat{\beta}_1 \underbrace{\sum_{i = 1}^n \hat{\varepsilon}_i x_{i1}}_0 + \cdots + 2 \hat{\beta}_p \underbrace{\sum_{i = 1}^n \hat{\varepsilon}_i x_{ip}}_0 
 
Geometrically I can understand that \hat{y} is the projection of y on the space spanned by the x's, and hence y-\hat{y} is perpediculer to each x, But analytically it is not at all clear.
Madyno (talk) 18:22, 7 October 2022 (UTC)
thread_title:  Proof