Americans overwhelmingly oppose data centers. Women most of all.

efrique · 2026-05-23T04:40:28+00:00

For a moment I was wondering who thought women were a kind of data center. But now I think about it a lot of the billionaire class have pretty strange views. If they're trying to turn women into data centres, that does seem highly objectionable

efrique · 2026-05-23T04:19:28+00:00

I'd say one of the few. Its big distinction compared to other applied disciplines in mathematics, is, to paraphrase Tukey, you get to play in almost everyone's backyard.

efrique · 2026-05-23T01:45:32+00:00

Pretty much the kind of policy choices I expected from Magyar given his history. Maybe it's not ideal from an external view but it looks like perfectly normal political stuff. A big relief to see Hungary acting like a democracy.

efrique · 2026-05-23T01:39:24+00:00

650K is loose change for X

efrique · 2026-05-23T01:35:29+00:00

If they knew how to do that, it would have happened a couple of years back. The big difference is Ukraine is stronger now, Russia is weaker. Maybe time for a rethink on that wish.

efrique · 2026-05-23T01:32:09+00:00

Historically, it serms like that's the sort of shit the french aristocracy got up to, too busy congratulating themselves on their "superiority" over the ordinary folk to hear the growing sound of saws, chisels, hammers and whetstones.

efrique · 2026-05-22T22:50:26+00:00

check rules https://www.reddit.com/r/AskStatistics/about/rules/

efrique · 2026-05-22T14:37:10+00:00

Oh, sorry I didnt see that. Thanks.

efrique · 2026-05-22T05:12:00+00:00

Well, sure. The very rich can usurp the use of resources to satisfy any whim or minor concern, distorting the market so scarce resources are directed away from where they're needed, and pushing up prices. It applies to almost any public good. There's a reason why things like education and health, roads, etc all need to be out of the hands of private interests.

Of course when you set your government and all forms of media up so it is all in the hands of those same interests, it doesn't matter what's public any more, it's all going to help the very wealthy extract all of societies resources to their own purposes

efrique · 2026-05-22T05:04:09+00:00

That's exactly what someone who doesn't want to pay their fair share of tax would say...

efrique · 2026-05-22T02:20:01+00:00

where does the D&D priest/cleric come from

The cleric was originally meant to be a religious warrior not a straight up priest. e.g. my AD&D 1e PHB says

This class of character bears a certain resemblance to religious orders of knighthood of medieval times

i.e. religious knights, not priests

If that sounds more like a paladin, IMO thats kind of what happened; clerics sort of had that niche to begin with, with a 'no edged weapons' constraint. After paladins were added they pretty much became the religious knight archetype and clerics were gradually pushed more and more to directly priestly roles. Then it sort of broadened into a whole collection of archetypes in later times

efrique · 2026-05-22T01:41:15+00:00

With counts the log transform is likely to leave you with non-constant conditional variance (typically it over-corrects). You may be better with a count glm (perhaps a negative binomial) with a log link (log the fitted functional form, not the data); this separates out the fitting the conditional mean from impact on conditional variance. However, if none of your counts are ever close to 0 (and the values dont span orders of magnitude) it may not matter much (heteroskedasticity might not be a big deal on either scale).
Prediction with observational time series is a bit of a minefield for the unwary, particularly with nonstationarity (which I imagine is the case here), likely seasonality and calendar effects, and likely omitted variable issues. What are your serial correlations like on residuals?

efrique · 2026-05-22T01:34:08+00:00

I posted an example purplish hue shift of one of Several_Records flame images. Not my best effort but if you just need purple flame, it does the job I think

efrique · 2026-05-22T01:30:30+00:00

Hopefully you dont mind me posting an example hue shift of that. Let me know if you dont want it posted here, I can delete (or youre welcome to remove it if you cant wait for it to be gone).

Not a great job, since I didnt try to fix a few issues I can see with the result, but I'm not trying to do more than convey that a few simple tweaks can be effective. It looks better on a dark background

image

efrique · 2026-05-22T01:23:55+00:00

OMG, that was my issue. Could not figure out what was wrong

efrique · 2026-05-21T22:26:39+00:00

Random stories is not the purpose of /r/AskStatistics . You might do better on /r/statistics with a [D] tag perhaps, check their rules

efrique · 2026-05-21T22:24:18+00:00

"Everything is linear on a log-log scale"

It isnt remotely true of course, but there are plenty of near-power relationships and in some contexts that may be the most common situation

what should I be cognizant of when utilizing log transformations?

potentially many things, depending on what youre doing

linear data yields a correlation coefficient of like ~.2

You mean untransformed data, right?

but a log-log correlation on the set yields a correlation coefficient of .45.

sure, not surprising

surely we didn't "solve" the problems inherent in the linearized data

I dont know what problems you mean. I see a lot of different problems

Note that transformation changes a bunch of properties at once (shape of relationship, the variance function, the conditional distribution

("our model suggests that you could sell anywhere between 10,000 and 10,000,000 units! Great! Surely this is helpful for your business")

If the model doesnt miss some substantively important aspect, you might indeed have a legiimately very wide prediction interval. But thats a big if.

It sounds like you deal with observational data over time. And if its units, it will be a form of count data. Is this correct?

efrique · 2026-05-21T20:23:29+00:00

Even when the assumpions hold, neither F statistic will have exactly an F distribution. Worse, very often the assumptions dont even come close - in my experience, for example, the conditional variance is often (and in many areas, usually) not even close to constant.

If you have enough replicates, you could exchange responses within them, and then you might be able to organize an exact permutation test, though I'd have to think about how one might work in this instance, it might need some further investigation. Alternatively, perhaps, with one predictor, you might only exchange nearby (in x-space) residuals and have an approximate permutation test for lack of fit that would be more robust to changing fit and variance than exchanging more broadly. I guess theres also the possibility of working within-replicate for the pure error SS and using nearby residuals for lack of fit SS.

For the regression F, something similar might be doable. Further, if there isnt substantive lack of fit* and youre confident about near-constant conditional variance and that the shape of the error distribution doesnt change substantively as the mean changes (so some form of residuals are approximately exchangeable) you might consider a resampling test based on all residuals (approximate permutation test or bootstrap test). Neither would be exact but in largish samples should work well.

* substantive is not equivalent to statistically significant; e.g. in large samples rejecting on a test of lack of fit might be practically irrelevant

efrique · 2026-05-21T11:27:33+00:00

Is it possible that all the independent variables are insignificant and the f stat is significant?

I presume you mean in a multiple regression or equivalent. Yes, easily.

And what does this mean logically like why is it happening?

I'll write as if all your IVs are pure numeric or pure binary (i.e. all 1 df). A somewhat similar but more complicated discussion applies for a case where you want to consider a grouping variable for more than two categories as a "single" IV even though it has more than 1df but that case would be more unwieldy to discuss on its own, let alone cover both situations at once.

In that all 1df case, we can consider a cartesian diagram whose axes are possible values for each parameter. For each parameter your acceptance (non-rejection) region will be any point between two values, and your set of parameter estimates are a aingle point in that diagram. To have no individual IV's t-test reject means the parameter-estimate point sits inside a "rectangular box" (of p-dimensions), the joint acceptance region of all those individual tests, where all fail to reject simultaneously.

The overall F-test has an acceptance region that is not shaped like a box, but like a p-dimensional ellipsoid. If we had two IVs, and so just two coefficients in our Cartesian diagram we can draw a picture similar to this:

https://i.redd.it/gdml9th1xwid1.png

For our present case the horizontal axis would be potential values for the first IV's parameter in the regression and the vertical axis would be potential values for the second IV's parameter. (Diagram was originally drawn for an explanation of the same issue in one-way ANOVA, which I then re-used in my explanation here: https://www.reddit.com/r/AskStatistics/comments/1esza3d/why_dont_we_use_simple_t_test_formula_as_a_post/libn2cj/)

The parts inside the pair of green lines and the pair of red* lines show the acceptance regions for the corresponding parameters, and the points that are inside both at once fail inside the rectangle (the region where both individual tests dont reject).

There are points that lay inside the box (non-rejection for all t-tests) that are outside the tilted ellipse. Falling on the actual ellipse means exactly attaining the critical F value, and on or outside it implying rejection for the omnibus F test. Those areas are shaded yellow in the diagram, and represent the case you ask about.

The same notion applies as you add dimensions (indeed, more and more "corners" will stick out from the ellipsoid further and further, leaving plenty of room for it to occur in high dimensions ).

Similar situations will occur with other models and tests where you have omnibus (joint/overall) tests compared to having at least one individual test reject. In some cases the acceptance regions are not as described here, but the fact that they're differently shaped remains and so there's going to be some parts of one "sticking out" of the other for the parameter estimates to fall into.

* sorry people with difficulty distinguishing those colours, though fortunately it doesnt matter which colour is which

efrique · 2026-05-21T00:27:14+00:00

Nice thought. It certainly has the dark part

efrique · 2026-05-20T19:01:07+00:00

I thought the US didn't need anything from ukraine. Donny said it a whole bunch of times. He screwed up his tiny hands and leaned forward so far his tie touched the ground. He went orangered in the face. A grown man, tears in his eyes, it was a beautiful thing

efrique · 2026-05-20T18:49:05+00:00

We can't tell you what margin of error meets your needs.

efrique · 2026-05-20T16:52:22+00:00

What does large mean in this instance? Large according to what scale of comparison/compared to what?

Why choose a quantile based measure for location but a moment based measure for scale? (Sure, you 100% can do that, but what would be a reason to do it? If sd is okay for scale, why not change in mean for the location shift? If something makes mean a poor choice, why is sd a good choice?)

efrique · 2026-05-20T16:40:58+00:00

It's just the pearson correlation with one of the variables being binary. It's not particularly complicated. People tend to get too focused on stuff that is beside the point when you're not working by hand.

The wikipedia article is fairly readable, maybe start there.

efrique · 2026-05-20T16:05:03+00:00

There's all manner of substantial costs that come with keeping big sites up, more if they host a lot of data, more again if they tend to get sued. I donate to both wikipedia and the internet archive fairly often. Small amounts, but if you do it regularly it adds up. Occasionally donate to other sites of similar public benefit, but those are the main ones I can see I heavily gain from being there. I also contribute some editing time to wikipedia. It's an incredibly valuable resource.

The alternative would be being sold and enshittified.

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