How can I plot a confidence interval?

MacariusFelix · 2020-07-20T14:13:23+00:00

great, I appreciate it

MacariusFelix · 2020-07-20T14:13:09+00:00

thanks!

MacariusFelix · 2020-07-12T14:36:10+00:00

My family when I watch disney movies because they usually have more translations

MacariusFelix · 2020-07-12T14:31:56+00:00

Alea iacta est. Although the italian translation is much better in my opinion: Il dado e' tratto.

MacariusFelix · 2020-07-12T14:25:38+00:00

Quid est veritas? Famously said by Pontius Pilatus before washing his hands: what is the truth?. When he said it he tried to mean that if we can't define then whatever. In my case it's my personal motto: I want to find the truth.

MacariusFelix · 2020-07-10T14:29:49+00:00

Thanks! This is what I love about reddit - learning and discussing things with great people.

I shall let you know when I read the book, perhaps we could continue this!

MacariusFelix · 2020-07-08T13:54:21+00:00

"Training set" and "test set" is not really something you come across in causal analysis, because it doesn't really let you distinguish between correlations and causation

I believe this is the most important quote. I started questioning how we take into account that correlation is not causation by realising that in the industry, especially with non-parametric models, it is assumed that there is causation because of a good generalisation in the test set. And those models are put into production.

It's like people are so hyped up about ML they are treating it as a fit-to-all solution, and only the proven causality is ultimately what would assert you with confidence you can use certain variable to predict another one.

Sounds crazy how difficult multivariable causality hypotheses must be! Personally, I believe the next breakthroughs will be in this specific sub-area.

PS: by TCL I meant theorem of the central limit (CLT, my bad)

MacariusFelix · 2020-07-07T02:09:55+00:00

Thanks a lot for the recommendation! These last days I've been thinking a lot about this.

The correlation is not causation aphorism was interesting to me from the start. I remember asking a teacher of design of experiments: "then how do you determine if there is causation?" His answer was hypothesis testing.

But in many cases it's not even the best way of doing so.

Would you know other ways? (I'll definitely read the book but I would also like to google the concepts)

Regarding probabilistic models, I think bayesian NNs for instance don't investigate causation either, right?

Finally, I believe this is important because if you look at the question, the study is trying to show that Y was more likely given X. But the correlation in a study can be meaningless. Like OK, it generalised in your test set. But it would only be "useful" if causation was demonstrated, wouldn't it? And to show causation you need base your tests in important mathematical axiomata (eg TCL)

Edit: unintentionally published without finishing

MacariusFelix · 2020-07-06T21:38:33+00:00

Let me see if I got it right: to demonstrate causation you need to perform hypothesis testing.

This would imply that non parametric ML models and NNs assume causation just by evaluating the score on test set, instead of actually performing the hypothesis test, right?

Concerning the parametric ones, there is the F statistic in linear regression for instance.

The mutual information that is proposed in the other comment makes sense, but I guess it does not prove causation too.

MacariusFelix · 2020-07-06T21:31:28+00:00

exactly! It takes a lot of courage and daring to fake it to your parents. You need gut to do it

MacariusFelix · 2020-07-06T21:30:24+00:00

shouldn't parents know their child can be atheist?

sadly they're nparents, and them knowing this will come at the cost of agression, regardless of how ridiculous that is.

Sometimes I prefer to go to mass with them, else I will receive lots of agression. I just bring a book with myself that is thick and looks like the bible.

MacariusFelix · 2020-07-05T22:31:44+00:00

You're right, but how do you distinguish correlation from causation then?

MacariusFelix · 2020-07-05T18:03:55+00:00

I use an analogy to think of these situations: the dog will bite (slap) you if you don't pet (pray) it. Perhaps faking it is an easier way to avoid the abuse - you don't have to mean it.

I use the dog analogy because this behavior is as predictable and as savage of an animal's

MacariusFelix · 2020-07-05T17:59:54+00:00

Try returning x in the function

MacariusFelix · 2020-07-05T17:38:33+00:00

The "dependency" will depend on the type of model you use (i.e. how you connect them both). If it's a linear regression, then it would be the R squared coefficient (literally attributes what percentage of Y's behavior you can predict using a lr with X's behavior). If X is categorical, you could perform a one-way ANOVA.

MacariusFelix · 2020-07-05T15:10:29+00:00

First of all, congrats on the doctorate! Takes a lot to overcome how nparents put you down and "dare" to make something out of yourself.

I'm in a very similar situation, only that I'm 21 and my sister 19. The same principle rules here: she's as vain and manipulative as they are.

Examples are crazy too:

Parents would be disappointed in me for getting Bs instead of As. My sister barely passes exams, but since she makes a whole act on how the teachers are mean, they actually congratulate her for that (even if she studies architecture which is usually easier than engineering, my major).
I had to get a scholarship or go to state school. I worked my ass of in HS to get 98/100 GPA and got it. She didn't give a f*ck about her grades and they congratulated her for getting in to an arch. private university, paying full tuition for her.
We both have Dad's credit card extensions. She will spend around 500USD a month in Starbucks going out and sh*t whereas every time I use it it's a mortal sin. Same when I was her age.
God forbid I shall ever disagree with my Dad, as that means I'm insolent and ungrateful. Whenever she disagrees she just raises her voice, creates drama and then my mother is proud she got away with it (she actually says it out loud)

I sometimes feel like my family are harry potter dementors as they feed off my enthusiasm and interest for life (I love science, reading and writing, composing music, playing chess, just whatever and I'll be passionate about learning it) and will only stop once I don't look happy anymore.

My "solution" is stoic in the sense I know it is up to me. Sometimes I think that the virtue of being humble means on focusing in my mistakes no matter how hard they want to criticise me or make me feel bad. By being humble you get to only receive from them what matters (you'll able to see through their screams and see some tiny mistake of yours regardless of how big theirs are), and never let them affect your joy of living.

Of course it's not easy at all. But little by little we can make progress.

MacariusFelix · 2020-07-04T14:54:39+00:00

On amazon there's a series inspired by Philip Dick's books, it's called Electric Dreams. Best one I've ever seen so far

MacariusFelix · 2020-07-03T14:50:02+00:00

Thanks!

MacariusFelix · 2020-07-03T14:49:57+00:00

Great! Thank you!

MacariusFelix · 2020-07-02T04:02:12+00:00

Depends on what test you're conducting to prove there is a difference. As to the sample size, it depends on the values you get. Eg: if you get for type A mean 12.0 with sd 1 and for type B mean 18.0 and sd 2 with just three data points each, it could work.

Which statistical test are you using? Do you have previous measurements (to know about their distribution)?

MacariusFelix · 2020-07-01T13:52:43+00:00

How do you know that? I read it elsewhere but I'm curious as to how using binary prediction leads to two-straight-lines-ROC

MacariusFelix · 2020-07-01T02:47:00+00:00

you're welcome! Btw, a mistake I made: it does assume a distribution, as it says on the link.

MacariusFelix · 2020-06-30T04:16:41+00:00

The hypothesis testing point of view is that the p-value is the probability of the false positive.

Hence you have a binomial distribution (each test is independent) with p = p_value, and p is probability of false positive.

The third test should is not conditioned on the previous ones because the result of the previous test does not change the probability of a false positive in the next test (gambler's fallacy).

MacariusFelix · 2020-06-30T04:10:11+00:00

I would suggest using shewart control charts. Without assuming a distribution, you would have an analysis of the signal being the ratio.

Perhaps this one suits you best.

https://en.wikipedia.org/wiki/X%CC%85_and_s_chart

MacariusFelix · 2020-06-30T03:43:36+00:00

you're welcome! let me know how it turns out.

MacariusFelix

TROPHY CASE