StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
3043564
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
2
CommunityOwnedDate
CreationDate
2010-06-15T08:23:07.797
FavoriteCount
0
LastActivityDate
2010-06-16T09:14:50.277
LastEditDate
2010-06-16T09:14:50.277
LastEditorUserId
170792
OwnerUserId
170792
ParentId
3042117
PostTypeId
2
Score
33
ViewCount
0
LastEditorDisplayName
text
Body
Just to add to what Dirk said about the Condition Number method, a rule of thumb is that values of <code>CN > 30 indicate severe collinearity</code>. Other methods, apart from condition number, include: 1) the determinant of the covariance matrix which ranges from 0 (Perfect Collinearity) to 1 (No Collinearity) <pre><code># using Dirk's example > det(cov(mm12[,-1])) [1] 0.8856818 > det(cov(mm123[,-1])) [1] 8.916092e-09 </code></pre> 2) Using the fact that the determinant of a diagonal matrix is the product of the eigenvalues => The presence of one or more small eigenvalues indicates collinearity <pre><code>> eigen(cov(mm12[,-1]))$values [1] 1.0876357 0.8143184 > eigen(cov(mm123[,-1]))$values [1] 5.388022e+00 9.862794e-01 1.677819e-09 </code></pre> 3) The value of the Variance Inflation Factor (VIF). The VIF for predictor i is 1/(1-R_i^2), where R_i^2 is the R^2 from a regression of predictor i against the remaining predictors. Collinearity is present when VIF for at least one independent variable is large. Rule of Thumb: <code>VIF > 10 is of concern</code>. For an implementation in R see <a href="http://finzi.psych.upenn.edu/R/library/HH/html/vif.html" rel="noreferrer">here</a>. I would also like to comment that the use of R^2 for determining collinearity should go hand in hand with visual examination of the scatterplots because a single outlier can "cause" collinearity where it doesn't exist, or can HIDE collinearity where it exists. 
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POScreening (multi)collinearity in a regression model
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. USGeorge Dontas
UserOwnerUserId
1. USGeorge Dontas
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. This table or related slice is empty.
PostsParentIdCreationDate
1. This table or related slice is empty.
VotesPostIdCreationDate
1. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
2. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
3. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
CommentsPostId
1. COThanks Γιώργος, +2 for this one! Great answer!
 singulars
 PostPostId
 PO
 UserUserId
 USaL3xa

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.