Link to picture:   Need a bit of help determining what I should do to fix this problem: The SEM wont run because it believes it's non-positive definite. Meaning that there may be singularity.  However these items are very different as i have items measuring autonomy, job feedback, coworker support, positive mood and employee engagement.  Can anyone suggest a solution to this problem?
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I am running a factor analysis in SPSS and get a "matrix is not positive definite" error from my correlation matrix. I've tried removing correlated variables, but I have to remove all variables down to those with correlations of r = 0.8 before the "positive definite" issue is resolved. This seems weird, as I have read that this problem normally arises where two or more vbls are nearly perfectly collinear (to me, r = 0.8 is a high correlation, but not near-perfect.) Can anyone suggest a way of dealing with this issue that doesn't just involve arbitrary removal of variables until the problem goes away? Or perhaps explain why I am getting the issue even when I seem to be removing all the really highly correlated variables?              [edit by Gottfried Helms:] By comment to an answer the OP says, there are 14 variables on 10 cases per correlation.
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so I used the root test, but i'm not quite sure if i'm allowed to. I think im performing the operations correctly,a and i keep ending up with $(1)^{\infty}$. So really my question is am i performing the operations wrong or do i have to use a different test?
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I was reviewing Low Quality Posts today and saw this message. Is this something new?     This post doesn't cite any references or sources. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed.
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\begin{table}     \begin{tabular}{|c|c|c|c|c|}     \hline     \multicolumn{5}{|c|}{\textbf{Table 1:}Unfiltered Water}\\     Concentration of analyte in sample   &amp; Avg &amp; Std Dev &amp;  RSD &amp; 90\% CI \\      \hline     Total hardness (ppm $CaCo_3$ equiv)  &amp; 97  &amp;    2   &amp;  2\%  &amp; \pm 5   \\     Calcium hardness (ppm $Ca^{2+}$)     &amp; 57  &amp;    3   &amp;  5\%  &amp;  \pm 5   \\     Magnesium hardness( ppm $Mg^{2+}$)   &amp; 40  &amp;    3   &amp; 7.5\% &amp;  \pm 5 \\     \hline     \end{tabular}   This table is giving me an error. That says   ! Missing $ inserted. &lt;inserted text&gt; $ l.41 ...Co_3 equiv\) &amp; 97 &amp; 2 &amp; 2\% &amp; \pm 5 \I've inserted a begin-math/end-math symbol since I think   you left one out. Proceed, with fingers crossed.  What is the cause of this error?  I can't seem to find any thing that is mismatched.
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Please see the model below (). The independent variables are properties of 2500 companies from 32 countries, trying to explain companies' CSR (corporate social responsibility) score.   I am worried about the VIF scores of especially the LAW_, GCI_ and HOF_ variables but I really need them all included in the model to connect it to the theoretical background the model is built upon. All variables are discrete numeric values, except the law LAW_ variables: they are dummies as to which legal system applies in the companies' country of origin (either english, french, german or scandinavian).    Amongst other articles, I have read  about dealing with collinearity. Often-suggested tips are removing the variable with highest VIF-score (in my model this would be LAW_ENG). But then other VIF-scores increase as a result. I do not have the proper knowledge to see through what is going on here and how I can solve this problem.   I have uploaded the corresponding data  (in SPSS .sav format). I would really appreciate somebody with more experience having a quick look and tell me a way to solve the collinearity problem without taking out (any or too many) variables.  Any help is greatly appreciated.  P.S. For reference, I am including a correlation table ():
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I'm a non-native speaker completely stuck trying to formulate a negative question. Full disclosure: I'd like to use it as a title for a university paper and I could not find any help around.  This is the question/title:     Why did the Industrial Revolution did not occur in China during the Ming/Qing period?   I highlighted the sources of my doubts.  In the title I'd like to ask why the Industrial Revolution (which happened in Britain) did not happen (during the same period) in China.  Should I rather say     Why did the Industrial Revolution not occur in China during the Ming/Qing period?   Thank you in advance  Edit: maybe this question should belong to "english language learners"
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I have a dataset where there is a high degree of multicollinearity, with all variables correlating positively with each other and the dependent variable. However, on some of the models I run I get a couple of significant negative coefficients. Basically there are two coefficients that depending on what variables I include in the model, I can manipulate their signs.  My understanding is that if the variance-covariance matrix only contain positive values, then all coefficients should also be positive. Is this correct?
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The following code produces a negative phantom (from the comment in ):   \newcommand{\negphantom}[1]{\settowidth{\dimen0}{#1}\hspace*{-\dimen0}}  However, when used inside an equation like  $$a\negphantom{\inf_{t&gt;0}t^2}b$$  I get an error. The solution is to do  $$a\negphantom{$\inf_{t&gt;0}t^2$}b$$  Is it possible to define negphantom in such a way that it respects being inside an equation or not? (the latter solution is not perfect, since the \inf is not in displaystyle).
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I googled .  Like (almost) always, SE was first.  However, they actually answered my question in a fancy box!    I read it, and clicked the link because I needed to know how many upvotes the answer got. It got 2 upvotes and is near the bottom, since there are many answers to this question.  The top (selected) answer has 317, and is also the description being pulled for the second top listing, as shown above.  Here is the question:   I would think this should be fixed, but maybe it is intentional, as the shown answer DOES agree with the top answer, and is short enough to fit properly.
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Over at stats.stackexchange we are having a minor kerfuffle over whether a title is using incorrect grammar. It has been edited and re-edited several times. It would be great to get some arbitration and a clear explanation.   The original title was "What questions is your data team hoping to answer?" which was edited to "What questions are your data team hoping to answer?", then edited back. See the question .
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Can someone please help me identify the fonts used in these two receipts. I'm looking for downloadable free fonts that I can use to reproduce the same looking results.  The two receipts used different fonts.
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I just renewed my antivirus (Kaspersky's) yesterday. After that, I logged onto Chemistry.SE (which I frequent often) but then I was greeted with this  Which was definitely not how it looked like earlier that morning. I wasn't able to  Open the reputation and badges tab Vote on questions, answers and comments (even answers to MY questions) Comment on questions or answers. 'Favorite' a question Attach tags to a question Post questions  etc., etc. But when I logged on to the Physics and Biology SEs (which I also frequent), the page format hadn't changed, but I still faced the same problems as above. Does anyone know how to fix this? P.S- I'm not a computer/formatting whiz, so you'd have to try avoiding the jargon when helping me out, sorry.... (Also I use Chrome...in case that's important.) Also @Oded recommended checking out the browser developer tools and having a look at the CSS, but as I said, I'm no Software/formatting whiz...so I'm still lost....  On @Shadow Wizard 's advice I've taken a screen shot of the 'error' notification (I had to scroll down to get all 10 'errors' on screen, hence the need for separate screen-shots)
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Context:  I'm modeling for a lending company and I'm modeling our loan approval process. I have some basic dimensions relative to our business figured out:   Time(day)    Account    Opportunity    Channel   And I and getting to the modeling of fact table(s) around the loan approval and credit decisioning process. An opportunity will get one or more "Credit Reviews" which have a series of steps they go through before arriving at a "Credit Decision". My instinct is to model the fact grain at the credit review step level or maybe the credit review level (or have one fact for each grain).  I'm working with some analysts who are used to using a “canned report”, one flattened table extracted from the source system that is at the opportunity level, and finds a "best credit review", and do a lot of their reporting from that.   I'm trying to explain my credit approval fact and how it would work and they think it's "overly complicated" and seem to want a fact table that is one-to-one with the opportunity, which is a dimension. And contains information on only the "best" credit review. In my mind that seems odd. I haven't run across a fact table that is at the same granularity (one-to-one) with a dimension. Have you ever heard of this? It feels like they are just trying to reproduce what they are used to, a non-dimensionally modeled data mart table at the opportunity level.   I'm trying to explain the utility of having a fact table that is at a higher granularity than they are used to in order to support many questions about the data rather than one, but I seem to be running into a wall getting this concept across. I’m not sure who is right here.  My question is this: Is it every appropriate, say in this situation, to model a fact that has the same cardinality as a dimension (one-to-one) with a core dimension? What would you recommend for this situation?
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This question was asked  but no one gave a good answer. So I think it's a good idea to bring it up again and also I would like to add some more comments/questions.    The first question is what is the difference between "PLS path modeling" and "PLS regression"? To make it more general, what are structural equation modeling (SEM), path modeling and regression? To my understanding regression focuses more on prediction while SEM focus is on the relationship between response and predictors and path modeling is a special case of SEM?  My second question is how trustworthy is PLS? Recently it's been subject to many criticisms as highlighted in  and  which leads to the rejection of papers based on PLS in high tier journals such as Journal of Operations Management ( is the note from the journal editor):     We are desk rejecting practically all PLS-based manuscripts, because we have concluded that PLS has been without exception the wrong modeling approach in the kinds of models OM researchers use.   I should note my field is spectroscopy, neither management/psychology nor statistics. In the papers linked above the authors are talking more about PLS as a SEM method, but to me, their criticism looks applicable to PLS regression as well.
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Question(s): What is the idea and intuition behind quasi maximum likelihood estimation (QMLE; also known as pseudo maximum likelihood estimation, PMLE)? What makes the estimator work when the actual error distribution does not match the assumed error distribution?  The  for QMLE is fine (brief, intuitive, to the point), but I could use some more intuition and detail, perhaps also an illustration. Other references are most welcome. (I remember going over quite a few econometrics textbooks looking for material on QMLE, and to my surprise, QMLE was only covered in one or two of them, e.g. Wooldridge  (2010), Chapter 13 Section 11, pp. 502-517.)
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Does anyone know how to prove the following:       Suppose $A$ and $B$ are both positive definite and $A - B$ is positive semi-definite.   Show that $B^{-1} - A^{-1}$ is also positive semi-definite.    I really appreciate any comments!
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I'm looking at , which was closed as off-topic a few days ago. Just a few minutes ago, the OP radically revised it; it doesn't even resemble the original question anymore. In fact, the two versions only share about seven words and some punctuation. Here's a screenshot of the most recent revision:    So, the question for the community is this: Is this proper, or should it be rolled back?  Edit: I went ahead and rolled it back because it has a couple of answers already (responding to the old version). But I would still like to know the community consensus on this.