So I randomly came across the Golden Ratio, kind of.

It’s much easier to explain by showing you the formula.

x + (x * x%) = 100

I ended up with 61.803398875.

Turns out 1.61803398875 is the Golden Ratio.

This has probably been found before, and is nothing significant, but I thought I’d just share it, found it quite interesting I came across it.

submitted by Nevin Manimala Nevin Manimala /u/Courosi_
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Bittersweet and heartbroken about trading my car in

It’s very nearly the day that I’m going to be trading in my 2011 Civic Si for an 86. I wanted something tighter, sportier, and every drive I’ve taken with an 86 was just magical in a way that only the MX5 seemed to compare. She’s also nearing the end of her problem-free life cycle and I want something that can last me a decade of dailying. I’ve longed for an 86 for so many years and now that the day comes that I trade my precious Si, I’ve gotten really emotional the past couple of days knowing that they may very well be my last with her.

This was the car I learned to drive stick on. The first car I stalled. The first car whose clutch I burned up while panic-stabbing the throttle while rolling back on an empty hill. The first car I modified, realized the mods were shit, and reversed back. The first car that really helped me get into the world of car enthusiasts. I bought this years ago from someone who frankly riced the shit out of her, and I spent a fair bit of money restoring her to something that could be considered stock. I’ve met a good amount of people, other car enthusiasts and Si fans, through this car that are still friends today because of it. I’ve made some precious memories with this car despite me not owning it for very long in the grand scheme of things.

I was driving to work today and decided to make a quick stop at the car wash in order to give her one last wash, and I couldn’t help but tear up as I hit three solid consecutive heel-toes around a high speed corner I love to take. And I found myself deep down hoping that the 86 I was looking at yesterday gets sold before I get the chance to buy it. But it’s such a ridiculously good deal with the exact loadout I wanted that I would be foolish to pass up the opportunity to finally grab the car I’ve been lusting after for years.

I still have some spare original stock parts that I’ll probably keep as a memento of her. But I just can’t shake this feeling that I’m making a huge mistake, that I’m “betraying” the car. It’s completely illogical. I’m extremely excited at the prospect of owning an 86 and creating years of new memories, but I’m so saddened that I’m letting go of my Si. I saved her and restored her to great condition, but it’s time to move on. Hopefully she takes care of her next owner as much as she took care of me.

How do you guys deal with stuff like this?

submitted by Nevin Manimala Nevin Manimala /u/Ayatori
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Linear regression!

Hi everyone,

So, I’m going to be a new math teacher this year, and at some point I will be teaching linear regression. Throughout my high school and college career I have never been taught this, and I am horrible with statistics about Nevin Manimala.

Correct me if I’m wrong, but to my knowledge of watching videos and such, linear regression is simply finding the line of best fit, given a set of data, in which we try to find if there is a strong, weak, or no relationship, similar to scatterplots.

I would like to have some more expertise before I plan this lesson and teach it.

Any explanations are welcome! Some examples or explain like I’m 5 would be wonderful as well.

Edit: this course is an honors Algebra I course for freshman high school!

submitted by Nevin Manimala Nevin Manimala /u/surikomi
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What trend that we will someday look back and cringe “this is so 2018” ?

I would say:

  • Contrast stitching everywhere. Contrast stitching brought the otherwise monotone interior with a little bit of a spice. What happened however, one manufacturer saw another car with contrast stitching, and said “we must do better”, and now we have contrast stitching everywhere from the glovebox to the center of the steering wheel.

  • Motorized everything It seems now that everything has to be motorized. In 20 years, only half of those will work anymore, and we will see many Tesla Model 3s with damaged window trim because people use the mechanical door release all the time.

  • Multiple screen This is probably controversial, but I think we will have a single larger screen (Volvo S90) as opposed to multiple smaller screens (Ranger Rover Velar) as development shifts more and more to software and the manufacturer has more flexibility when they are not constrained by physical barriers.

  • Touch-sensitive buttons Im talking about buttons outside of the screen that you operate using touch. Or one that still requires pressure, but all mounted on a flat surface. Its been deprecated from consumer electronics, to either going back to actual physical button or they go all-in and moved it to the screen as the screen gets larger.

  • LED Animation Now it seems cool to have LED that animates when you unlock the car, in the turn signal, but it is obviously overdone and will go out of fashion very soon

  • Fancy car keys Basically all car keys that are a) oversized, b) needs to be charged, c) has a tiny LCD screen. Those functionalities will be moved to apps, and car keys will go back to the small, practical one that provides the essential functionality, as a backup to your phone that does all the mumbo jumbo.

Bonus: Imagine a 50 year old Doug DeMuro saying “Of course this was 2018, and everything had contrast stitching and everything was motorized”

submitted by Nevin Manimala Nevin Manimala /u/ptrkhh
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Can you prove that every possible combination of numbers appears in the decimal places of Π?

Furthermore, is the assumption, that Pi‘s decimal places are evenly and randomly distributed over 0-9 valid? If not, how could you prove that in a infinite string of random numbers every possible combination of numbers does appear?

submitted by Nevin Manimala Nevin Manimala /u/_Vollkorntoast_
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Checking My Understanding of the Difference in SSE between Multivariate and Univariate Multiple Linear Regression

Hello all! I’m interested in a check of my understanding of the difference between multivariate multiple regression and univariate multiple regression.

After watching this incredible video, it seems that the regression coefficients are the same whether you solve for them univariately or multivariately. This is because the regression coefficients are selected so that error is set to 0 while also controlling for other predictors. Because error is set to 0 (whether it is univariate error or multivariate error), the partial derivatives used to create the formulas that estimate regression coefficients turn out to be the same and so yield the same values.

These regression coefficients then allow for the estimation of the SSE. In the univariate case, SSE is computed as the squared difference between the observed and predicted criterion variables. In the multivariate case, SSE is computed univariately for each of the criterion variables, and these SSEs are then summed (representing the trace of the error matrix). This difference in the SSE quantity that is minimized is what allows, in the multivariate case, for the simultaneous consideration of the intercorrelations between predictor and criterion, as well as the intracorrelations among these same variables.

Have I adequately captured the differences between multivariate and univariate multiple linear regression as well as explained the reason why they are different?

I appreciate your feedback!

submitted by Nevin Manimala Nevin Manimala /u/dbzgtfan4ever
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Who cares about algebraic geometry?

Apologies for the facetious title. They don’t really study algebraic geometry at my university.

I understand that on one level, algebraic geometry is simply interesting and therefore worth studying on its own. A professor once answered my “Who cares about homology theory?” question by explaining that a bunch of mathematicians had invented a great game, like chess, and now we’re all going to enjoy playing it.

But massive fields of math like AG become so because they tend to be extremely applicable (if not directly to the material world, then at least to other fields of math). What are some of these applications?

submitted by Nevin Manimala Nevin Manimala /u/another-wanker
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