Where are my statistician fags

Where are my statistician fags

Other urls found in this thread:

en.wikipedia.org/wiki/Multicollinearity
en.wikipedia.org/wiki/Generalized_linear_model
en.wikipedia.org/wiki/Panel_data
math.usu.edu/jrstevens/biostat/PoissonNB.pdf
twitter.com/NSFWRedditVideo

Hobbyist statistician here.

is statistics a viable career path

very

Better than engineering?

you should do what you enjoy more user. after spending years studying sth you don't like just because another user told you to do so is frustrating

As a career, yes. As a degree, maybe.

My presence almost surely converges to a value equivalent to here. Somebody put me out of my fucking misery

18 yr old retard that is taking AP Statistics reporting in

en.wikipedia.org/wiki/Multicollinearity

Always remember not to regress on variables with a highly negative correlation.

A high positive correlation would be just as bad

Remember that residual plots and scatter plots are NOT the same thing

undergrad here
Tell me GLMs get better after a bit

Tip: Companies love hiring college students and grads with experience, but NOT grads without experience. Take an internship in your desired field while in college.

>sth
I thought you had mistyped shit and then realized this was urban gorilla shorthand for something. You have a keyboard in front of you, use it. If you're on a phone you're getting raped by the ukrainian mystery dick scripts, and you should shoo shoo in any case.

I'm the economics/stats guy here, hoping to get into the MS in Stat program after I finish up my last couple of courses.

Same poster here, is anyone doing any undergrad research with a faculty member? If so, how did you get into it? I'd like to try to get some experience before applying for my masters.

I managed to do some by just asking the professor

...

Just use ridge, lasso, or PCR. You don't always need OLS.

Make sure your linear algebra is on point and review your calculus before starting your program.

Thanks, will do. I've been reviewing calculus but I still need to take linear algebra. I've an e-book on it though so I'll start working through it.

I should probably brush up on R as well I'm guessing.

I'll give that a shot, I still need to get a few classes out of the way first though.

enjoy your "how to read an ANOVA table" class

But muh gauss-markov theorem

Engineering is for brainlets only

Well,
The 'first' group could arive anytime, but when it does, the other group has 15 minutes to arive. 15 minutes being 25% of one hour; so 0.25?

Had my first stats class this semestre, was pretty fun. Shame that I need to wait until next autumn to take some more so I will just take Probability II, Stochastic Processes and applied stochastic processes(insurance math) and mathematical modelling this year.
In the autumn I will be able to take Linear Stats Models, Statistical Interference Theory, Analysis of Categorical Data and Financial Math, so it will be pretty lit semestre desu

Nice user! I'm still in my first year but I can't wait to take those classes too

What does this even mean?

This guy knows how to get work done and knows that first year graduate stats classes are basically Calculus VII with endless algebraic tricks that only extend to R^2 (and if you don't use them you won't have enough time to complete the exam).

No undergrad understands linear algebra. The undergraduate linear algebra course is less worse than useless at most universities. The beauty is best appreciated via analysis/PDEs and the special case of Hilbert spaces. The intuition is always geometric. The proofs are always algebraic. The historical motivation is always optimization. No more than 1 of these approaches are taught well in an of these are taught well given department; usually

>What happens when the response variable is discrete?
Discrete how? Dichotomous: logistic regression, counts: Poisson regression, categorical: classification/clustering.
>This user knows the answer.
Congratulations faggot. You're not the only statistician on this board.
>the material is hard
That depends on the department's goal. Most are just plug and chug MS programs that use Minitab, unfortunately. Theory is not heavily emphasized at the MS level any more.
>In industry R/Python is 20/80 overall but varies by industry. SAS basically pharma.
SAS is also used at big companies and government with institutional inertia. Matlab still has some pretty cool uses. Agree about the Stata/SPSS though.

>What does this even mean?
en.wikipedia.org/wiki/Generalized_linear_model

...

Holy shit, that's genius. How do you think of that?

>taking the difference between two uniformly distributed random variables is genius

Is GLS regression only ever used in panel data or is it useful under other circumstances?

Introduction to Probability Theory. Lots of homework and exam problems were like that, but usually harder (involving multivariable calculus, integrating a joint PDF over the area of interest).

Just finished a class on Mathematical Statistics(MLE, confidence intervals, hypothesis testing, regression, anova, nonparametric). Where do I go from here?

see and Looks like I need to retire that IQ test finally, now that someone has solved it.

What is the difficulty that glms are giving you?

>panel data
Can you elaborate on this?

What was the textbook?

>Mathematical Statistics
>(MLE, confidence intervals, hypothesis testing, regression, anova, nonparametric)
Weird, that sounds like a non-mathematical statistics class. Math stat classes usually cover foundational stuff like measure theory.

le chi squared xDDD

Hogg

I know measure theory, so I think that teaching it in a stats class is kind of silly, but I know most stat people don't reach that far in the math sequence.

not sure i interpreted the question correctly

>Hogg
To continue studying math stat at the next level, then you want Casella and Berger's Statistical Inference (solutions to most of the problems are available online).

If you want a good linear models book, then look for Rencher's Linear Models in Statistics (mostly theory but has all solutions in the back of the book), but a more applied book would be Kutner's Applied Linear Regression Models.

If you've taken analysis and linear algebra (not that baby level augment matrix with zero vector and solve. A real linear algebra course) then look at The Elements of Statistical Learning (free pdf on authors website and full solutions elsewhere). If your math is not really there, then the authors also made a free book titled Introduction to Statistical Learning with R. The difference between the two is similar to baby Rudin and papa Rudin.

Any other questions, or even what are you trying to accomplish by studying?

Not going to comment on that one. You already killed one of my IQ tests today.

I got my degree in mathematics, and I want to learn more applied mathematics, so thats why I took mathematical statistics. The mathematical background for statistics is something that I don't need to worry about. I suppose that I can go start learning about machine learning, since that's where all the money is now.

en.wikipedia.org/wiki/Panel_data

wouldn't you need to know the percentage of ten-digit phone numbers that don't connect anywhere

No. The level of this problem is one that I would not expect a MS degree holder to solve. Hint: use Bayesian methods.

but then it being the correct number tells you nothing about whether or not the line is busy and P(line busy | right #) and P(line busy) cancel out and your just left with P(right # | line busy) = P(right #) = 50%

am i supposed to put an uninformative prior on holden's 50% sureness because he's a dumb twat or should i collect some data on how often dumb twats type the wrong number into their phones

just restrict your modem to a domain excluding this one. you'll be fine

P(correct # | busy signal) = [ P(busy signal | correct #) * P(correct #) ] / P(busy signal) = [ .01 * 0.5 ] / .01 = .5

I got the same answer as you. This question is flawed.

I've gotta work with GLMs for a project but have almost no practical knowledge on them since my prof is a theory spouting retard
>im no brainlet i swer

statistics are fake shit

Statisics undegrad here as well. Loving this shit. Banging bitches and banging Bayes.

I suggest taking a look at "Statistical Rethinking" by Richard McElreath. It's very example-driven and there's a great chapter on GLMs. Also the author has a sense of humor which is always nice.

Does anybody else feel these feels?

>Can't do a pen and paper statistics test to save my life
>Can do programming much better than my classmates
>Get shitty marks
>End up in a good job, because machine learning is more useful to businesses than rote learning the UMVUE derivation for a gamma distribution.

In my linear algebra course I learned to do least squares using transposed matrices
In my statistics course I learned to do it using sums of squared errors

I tried using both methods on the same problem and I did not get the same answer, although the line of best fit was very similar. Are these methods intrinsically different, or did I fuck up something? I calculated all the sums directly in the expression so there was no rounding error

LOL

thats pretty much me rite now

Question for y'all.

I'm trying to draw a graph that looks like the Standard Normal Distribution when it's plotted on a graph where the x-axis is logarithmic, instead of linear. (Sorry if I'm abusing terminology, I have very little background in maths.)

The idea I'm going for would put 10,000 at the mean, 100,000 at +1 standard deviation, 1,000,000 at +2 standard deviations, and so on. In the opposite direction, I'd have 1,000 at -1 standard deviation, 100 at -2 standard deviations, and so on.

Is there a function like this? If so, what would it look like, or how would you derive one?

Happy to answer any clarifying questions, or talk about my project if anyone's interested. Thanks in advance for any suggestions.

>pic unrelated

wait what are GLMs lol I was looking at the pic fml

Is Stan the way to go for Bayesian modeling?

he doesn't use the superior WINBUGS

>doesn't even use hybrid Monte Carlo
>Windows only
Nah.

If the logarithm of x is normally distributed x follows something called a (creatively named) lognormal distribution.

[math]
\texttt{> rnorm(1, c(0, 100))} \\
\texttt{[1] 0.1432776} \\
\texttt{> rnorm(2, c(0, 100))} \\
\texttt{[1] -0.2667315 99.2165480} \\
\texttt{> rnorm(3, c(0, 100))} \\
\texttt{[1] -0.9371833 99.1430490 -0.5432836}
[/math]

Who the hell designed R.

So it alternates between simulating the first and second components of the mean vector?
I don't see why that's particularly odd. What would you want it to do?

>What would you want it to do?
Something that doesn't feel completely arbitrary. Why allow for vector arguments to the mean in the first place? I could easily see someone unaware of rmvnorm thinking they were going to get an iid sequence of samples from a multivariate normal with mean (0, 100) out of that.

maybe it's a holdover from a time before apply and replicate existed?

I could also see doing something like
x

r is annoying until you try and use python for similar use cases

>t.doesn't know about pseudorandom number generators or how they work

Agreed. I would take Matlab over Python any day of the week.

user's complaint was how it was switching between the two specified means, which your snippet doesn't even address

> tfw the more I study math&stats the more I love R

it's so comfy for anything related to data analysis too

>lognormal distribution
This is exactly what I was looking for, thank you very much.

>Can do programming much better than my classmates

what do you mean by "programming" in context of statistics

How can anyone hate R?
It's so interactive it's like playing a video game.

Plenty of languages have integrated console/IDE things like RStudio.

Hi, literal statistics brainlet here, going to study pure maths next year and taking statistics in 2019 (can study 2 careers at the same time)

I know how discrete and continuous random variables work, I know how to work with some moments (basic ones) and I understand the first half of Meyer's introductory prob/stat, but statistical inference is just too much for me

What are some good introductory books on the subject? Videos, tips, anything can be helpful

need this too

It depends on how well you know probability theory, the way my school taught statistics was a proper introduction to probability theory (without really going too much into the measure theory), and then a formal look at statistical inference. It was divided into 2 courses (the second course being statistical inference), and both covered the book: "Mathematical Statistics and Data Analysis" by John Rice.

I highly recommend this book, the first half is all probability theory, so you can fill in the gaps you are missing for that, and the second half covers a formal theory of statistical inference that (importantly) ALSO covers non-parametric inference, which is a vital for any statistician to know about today. I am pretty sure there is a PDF of this book floating around on the internet somewhere

for those of you getting into it ann anastasi is thee late goddess of stats. get any edition of her classic textbook and you will pick it up.

Quality Control Manager here, you called?

DAMN! need this! but cant find pdf

Did anyone had to do robocode? If not, it basically is a field with fighting tanks. I want to model their locations. Where should I look for?

Kalman filters.

>Book cooking and overfitting data: the field

Why do undergrads call everything a field. It's so pompous. Just call it a subject.

Killer, I'm way more familiar with Python's syntax so that's very good to hear. Still want to get somewhat proficient at R though.

>Counts: Poisson regression
No. Binomial or bust.

wrong. if the first group arrives after 0:45, the overlap is discarded

Go away brainlet
math.usu.edu/jrstevens/biostat/PoissonNB.pdf

I'm not sure what that's supposed to tell me. Yes, in some cases the negative binomial is also preferable to the Poisson.

Is your complaint that I should have said negative binomial or binomial or bust? That seems like just a longer way of saying the same thing.

The only time you would use a poisson is if you lacked the information required to use a better distribution. And, ideally, you should probably shoot for Bernoulli, and just model the individual trials, but I won't go so far as to say Bernoulli or bust. Getting trial level data can be challenging, so I'll let you pass with a binomial.

>Can't do a pen and paper statistics test to save my life

I knew this feel until I got a very old fasioned prof this last semester who had tenure and wasn't afraid to humiliate you in front of the whole class if you fucked up - I had to learn out of fear

Will it work with several tanks, such that the probability of two tanks being close is very small?

I don't know. I have no idea what robocode is or what kind of information you have access to.

>If you see Stata or SPSS or JMP run for the hills.
Do people actually use JMP professionally? I have undergrads use it when they need to do stats but don't know R, but I try to get them off JMP as quick as I can.

I am guessing that you have never worded in industry?

Its hilarious how people assume that just because you CAN do something unscrupulous with the knowledge you get from a field, that all practitioners of that field do those unscrupulous things

>All chemists make bombs and drugs
>All engineers make weapons systems

Almost done with my Senior year and took AP Stat last year, god-tier course. Love it but what kind of jobs could I get related to statistics? I already have a stable 30$ per hour '''''''CS''''''' job doing Node and Angular programming, so should I even consider a career in stat or just keep doing this?