Everytime I start my MacBook Pro (which is set to automatically boot into my account) I get the error message "unable to unlock login keychain" with 3 options - continue (which will give me errors as I use my computer whenever I need the login keychain) create new keychain (I know this creates a new keychain, but does it copy all my keys?  Or are they lost now?)  or Update keychain password.  I've used the password which I think should work, but it doesn't.  Now my machine auto logs in, and I get the error, and at the moment I'm clicking continue.  I've tried using keychain repair, which finds no errors.  Please: How should I trouble shoot my keychain errors?  How do I fix my keychain errors?  Is there any way to keep all my old passwords, or have I lost those?
How do I reset my main keychain and completely delete the old one? I changed my password and its been coming up with all of these popups asking for my new password when I first login. If anyone can help with updating my keychain I would appreciate it or you can just help me completely delete and reset it.
Reputation misaligned in new topbar Using math in TikZ Ubuntu 18.04 opening system setting logs me out I've recently installed Ubuntu 18.04 in a dual-boot configuration with Windows 10. I noticed today that, when trying to open system settings, Ubuntu seems to log me out to the lock screen. When I log back in, all previously-running applications are now closed. Very unsure what to do from here, any help much appreciated. I will be landing at New Delhi airport. The worth of new laptop  (Lenovo Yoga Idea Pad 13) is about $1000 and old one (Macbook Pro) is > 1 lakh INR. I want to leave the new laptop in India. I have a copy of bill with me but not the original bill. I want to know if I have risk of being charged with custom duty. Where and why do I have to put the "template" and "typename" keywords? How do you test non-live sites before switching DNS? I have a few websites that I need to move to a new server - how would I test that they work before switching DNS? What URL would I navigate to? pgfplots: lines and markers in different colors With pgfplots, I am creating a simple coordinate plot with error bars like this:    \documentclass[crop,10pt]{standalone} \usepackage[english]{babel} \usepackage{pgfplots}      \begin{document}%         \setlength{\linewidth}{246pt}%              \begin{tikzpicture}             \begin{axis}[             width=\linewidth,             ytick pos = left,             xtick pos = left,             clip=false,             axis on top,             tick align = outside,             yticklabel style={%             /pgf/number format/fixed,             /pgf/number format/precision=1,             /pgf/number format/fixed zerofill         },             ytick scale label code/.code={},             xlabel={X},             ylabel={Y},     ]             \addplot[mark=*]             plot[             error bars/.cd,             y dir=both,             y fixed=0.0001     ]             coordinates{(1,0.0005) (1.1,0.0013) (5,0.0015)};             \end{axis}             \end{tikzpicture}     \end{document}   In the original plot I have a lot more points. I would now like the line and the markers (including the error bars) to have different colors to better distinguish the line from the error bars. Is that somehow possible? The only way I found so far is to plot the data just as a line in one color and then plot it again just as markers in another color. This works, but I guess there must be some better solution to this. Thanks for the support, John Is there a way to save selection by faces I'm trying to find a way to save selection faces, tried Vertex Group, the issue with Vertex Group is that it adds the faces that are marked by 4 vertices that are added to the Vertex Group, there are some cases were this doesn't apply, the image explains what I mean. So is there a way to save my selected faces, so I don't have to go through the whole process of reselecting them each time I need to do so? If you check the image, I didn't assign the hatched faces to the Vertex Group, still when I select the Vertex Group, they get selected, I understand that they got selected because the four boundary vertices are assigned to the Vertex Group, I need to save only face selection, not vertex selection. After taking a statistics course and then trying to help fellow students, I noticed one subject that inspires much head-desk banging is interpreting the results of statistical hypothesis tests.  It seems that students easily learn how to perform the calculations required by a given test but get hung up on interpreting the results.  Many computerized tools report test results in terms of "p values" or "t values".  How would you explain the following points to college students taking their first course in statistics:   What does a "p-value" mean in relation to the hypothesis being tested?  Are there cases when one should be looking for a high p-value or a low p-value? What is the relationship between a p-value and a t-value? bound on the cardinality of the continuum? I hope not Suppose we don't believe the continuum hypothesis. Using Von Neumann cardinal  assignment (so I guess we believe well-ordering?), is there any "familiar" ordinal number $\alpha$ such that, for non-tautological reasons, $\aleph_\alpha$ is provably larger than the cardinality of the continuum? I would hope not since it would seem pretty silly if something like $\alpha = \omega_0$ worked and we could say "well gee we can't prove that $c = \aleph_1$, but it's definitely one of $\aleph_1, \aleph_2, \ldots , \aleph_{73}, \ldots$". I (obviously) don't know jack squat about set theory, so this is really just idle curiosity. If a more precise question is desired I guess I would have to make it     For any countable ordinal $\alpha$ is the statement: $c &lt; \aleph_\alpha$ independent of ZFC in the same sense as the continuum hypothesis?   assuming that even makes sense. Thanks! Some questions might be asked on three different sites, each site needing three different answers. For example, a question about a text editor.  So perhaps there should be a way to link the questions on the three different sites. Adding a link manually in the questions is certainly possible, but perhaps a feature officially supporting this will be beneficial. I overseasoned my food, what can I do to remove the strong smell? Choosing between LM and GLM for a log-transformed response variable I'm trying to understand the philosophy behind using a Generalized Linear Model (GLM) vs a Linear Model (LM). I've created an example data set below where:  $$\log(y) = x + \varepsilon $$   The example does not have the error $\varepsilon$ as a function of the magnitude of $y$, so I would assume that a linear model of the log-transformed y would be the best. In the example below, this is indeed the case (I think) - since the AIC of the LM on the log-transformed data is lowest. The AIC of the Gamma distribution GLM with a log-link function has a lower sum of squares (SS), but the additional degrees of freedom result in a slightly higher AIC. I was surprised that the Gaussian distribution AIC is so much higher (even though the SS is the lowest of the models).   I am hoping to get some advice on when one should approach GLM models - i.e. is there something I should look for in my LM model fit residuals to tell me that another distribution is more appropriate? Also, how should one proceed in selecting an appropriate distribution family.   Many thanks in advance for your help.  [EDIT]: I have now adjusted the summary statistics so that the SS of the log-transformed linear model is comparable to the GLM models with the log-link function. A graph of the statistics is now shown.  Example  set.seed(1111) n &lt;- 1000 y &lt;- rnorm(n, mean=0, sd=1) y &lt;- exp(y) hist(y, n=20) hist(log(y), n=20)  x &lt;- log(y) - rnorm(n, mean=0, sd=1) hist(x, n=20)  df  &lt;- data.frame(y=y, x=x) df2 &lt;- data.frame(x=seq(from=min(df$x), to=max(df$x),,100))   #models mod.name &lt;- "LM" assign(mod.name, lm(y ~ x, df)) summary(get(mod.name)) plot(y ~ x, df) lines(predict(get(mod.name), newdata=df2) ~ df2$x, col=2)  mod.name &lt;- "LOG.LM" assign(mod.name, lm(log(y) ~ x, df)) summary(get(mod.name)) plot(y ~ x, df) lines(exp(predict(get(mod.name), newdata=df2)) ~ df2$x, col=2)  mod.name &lt;- "LOG.GAUSS.GLM" assign(mod.name, glm(y ~ x, df, family=gaussian(link="log"))) summary(get(mod.name)) plot(y ~ x, df) lines(predict(get(mod.name), newdata=df2, type="response") ~ df2$x, col=2)  mod.name &lt;- "LOG.GAMMA.GLM" assign(mod.name, glm(y ~ x, df, family=Gamma(link="log"))) summary(get(mod.name)) plot(y ~ x, df) lines(predict(get(mod.name), newdata=df2, type="response") ~ df2$x, col=2)  #Results model.names &lt;- list("LM", "LOG.LM", "LOG.GAUSS.GLM", "LOG.GAMMA.GLM")  plot(y ~ x, df, log="y", pch=".", cex=3, col=8) lines(predict(LM, newdata=df2) ~ df2$x, col=1, lwd=2) lines(exp(predict(LOG.LM, newdata=df2)) ~ df2$x, col=2, lwd=2) lines(predict(LOG.GAUSS.GLM, newdata=df2, type="response") ~ df2$x, col=3, lwd=2) lines(predict(LOG.GAMMA.GLM, newdata=df2, type="response") ~ df2$x, col=4, lwd=2) legend("topleft", legend=model.names, col=1:4, lwd=2, bty="n")   res.AIC &lt;- as.matrix(     data.frame(         LM=AIC(LM),         LOG.LM=AIC(LOG.LM),         LOG.GAUSS.GLM=AIC(LOG.GAUSS.GLM),         LOG.GAMMA.GLM=AIC(LOG.GAMMA.GLM)     ) )  res.SS &lt;- as.matrix(     data.frame(         LM=sum((predict(LM)-y)^2),         LOG.LM=sum((exp(predict(LOG.LM))-y)^2),         LOG.GAUSS.GLM=sum((predict(LOG.GAUSS.GLM, type="response")-y)^2),         LOG.GAMMA.GLM=sum((predict(LOG.GAMMA.GLM, type="response")-y)^2)     ) )  res.RMS &lt;- as.matrix(     data.frame(         LM=sqrt(mean((predict(LM)-y)^2)),         LOG.LM=sqrt(mean((exp(predict(LOG.LM))-y)^2)),         LOG.GAUSS.GLM=sqrt(mean((predict(LOG.GAUSS.GLM, type="response")-y)^2)),         LOG.GAMMA.GLM=sqrt(mean((predict(LOG.GAMMA.GLM, type="response")-y)^2))     ) )  png("stats.png", height=7, width=10, units="in", res=300) #x11(height=7, width=10) par(mar=c(10,5,2,1), mfcol=c(1,3), cex=1, ps=12) barplot(res.AIC, main="AIC", las=2) barplot(res.SS, main="SS", las=2) barplot(res.RMS, main="RMS", las=2) dev.off() How to install the latest stable version of Chromium? Is there an easier way to install Chromium than what is described ?  Furthermore, is there an easier way to stay up to date with current Chromium releases rather than having to re-build Chromium each release? (a script which automatically grabs and installs the latest build possibly?).  Related: Prove $\mathbb{Z}_2 \times \mathbb{Z}_2$ is not isomorphic to $\mathbb{Z}_4$ jQuery Datepicker with text input that doesn't allow user input Is it possible to get a fully transparent window in Pygame (see the desktop through it)?  I've found how to create a window without a frame, but there doesn't seem to be any obvious way to make it transparent.  I'd be willing to tie into system-specific technology/frameworks as long as there are solutions for both Windows and Mac OS X, but I'm not sure which direction to be looking.  The only topic I was able to find , which isn't something I can do for this particular project (needs to be Pygame). Consider the group $\mathbb{Q}$ under addition of rational numbers. If $H$ is a subgroup of $\mathbb{Q}$ with finite index, then $H = \mathbb{Q}$.  I just saw this on our exam earlier and was stumped on how to show this. Any ideas? C++ virtual function from constructor Prove that any two nontrivial subgroups of $\mathbb{Q}$ have nontrivial intersection I need to prove that any two nontrivial subgroups of $\mathbb{Q}$ have a nontrivial intersection as part of a larger proof that $\mathbb{Q}$ cannot be represented as a nontrivial direct product.    (Yes, I realize that there are other ways to prove that $\mathbb{Q}$ cannot be represented as a nontrivial direct product, but I am not interested in those, and will not accept answers showing me how to do that).  Based on the density of the rationals, I guess this idea kind of makes sense, but I am doing this proof for an abstract algebra class, and I don't think real analysis proofs would be accepted.   Everywhere I've looked online has said this is "trivial" or "easy to see" without bothering to actually prove it for those of us mere mortals who don't see it as such. As for myself, I have literally no idea where to even get started proving this. Could somebody help me figure this out (without giving me cutesy examples that aren't really all that related with the expectation that I should be able to figure it out from looking at those - that's not really how my brain works)?  Thank you.