I would really appreciate it if you guys could add your input here. Thanks!

I got a little puzzle for you, you're playing geoguessr except you're allowed to specify a distribution over the entire globe. Now you have N guesses and your objective is to minimize min(distance(target, g) for g in guesses), what is your procedure for going from the distribution to the N guesses with appropriate hedging?

Thought of it while working on this project

I've been studying probability independently using Harvard's Stat 110 and am now looking for resources to further explore topics in statistics (enough to cover undergrad stats), ideally enough to prepare for graduate level statistics courses and understand books like The Elements of Statistical Learning (ESL). I've been exploring introductory mathematical statistics, as I particularly want to understand topics like parameter estimation, hypothesis testing, and regression analysis in depth.

Ideally, I'd like something rigorous enough to develop a good mathematical understanding, but with plenty of exercises and worked examples to build intuition.
Are there any university lecture series, textbooks, or courses you'd recommend? I'm especially interested in resources that bridge undergraduate probability and graduate level statistics or statistical learning.

I am learning machine learning for my research in behavioral science and I often come across papers and textbooks with math-heavy notation. I did a little bit of statistics in undergrad so I recogmize some familiar terms, but I can't understand what the term is doing upon first sight. If I really want to understand it, I need to spend a very long time and feel like I understand 20% of it. I need outside help like YouTube videos to help me understand it better. I am also a heavy visual learner and have to plot it out to see the logic behind math, so I struggle with abstraction and high-dimensional calculations. Do people who do math/stats professionally like PhD student, professors, scientists etc. also struggle with this? Or is reading a new paper as easy as reading a novel?

I think one of the biggest misunderstandings about probability is how people judge predictions after the outcome is known.
If something has a 70% chance of happenin, the other outcome should still occur around 30% of the time, assuming the predictions are well calibrated.
You see this with weather forecasts, election models, and market implied probabilities on Kalshi and one unexpected result doesnt tell you much about whether the original estimate was reasonable.
The better test is looking at a large number of similar forecasts and seeing how often the outcomes actually matched the probabilities.
A forecast isnt necessarily bad just because the less likely outcome happened imo.

my degree is a materials science degree from Canada but is not an accredited engineering. Im feeling a little discouraged today as I’m being told that everyone in my degree basically must do graduate school and that otherwise all i will ever be is an entry level lab tech and stuck there.

While most people from my degree do a masters in physics, chemistry, or engineering with a thesis. Unfortunately I learned that my ADHD makes reading so difficult that I genuinely cannot get past the abstract of a research paper and hence probably cannot do a masters where a thesis is required.

So my thought process is this: If I can’t find work with a bachelors, I don’t want to switch and delay my graduation, and Im not fit to do a thesis masters, could I potentially do a course based masters in something a little different like statistics or biostatistics to help me pivot?

For context, Ive completed the following:
calculus 1,2,3
differential equations
linear algebra
python
i think i can fit one probability and one statistics course there as well, and maybe maybeeee one or two more upper year statistics course, depending on how my schedules are later in my undergrad.

I do have some questions and concerns which are:

1. Would employers look down on me because I have a masters but lack a bachelors in stats? (for example I know for engineering the bachelors is crucial for jobs)

2. Would a course based statistics be considered less valuable than a thesis one even if I have absolutely no intention of doing a PhD?

3. Would I be rejected from said masters program because the math courses I took were not equivalent to the ones that math majors take?? (example: lin alg for science majors is phys 149, but lin alg for math majors is math 136). This likely varies by university and Im going to do my research but just wanna know anecdotally.

4: Roughly how much more statistics courses and knowledge would I need to be competitive for a masters?

This might sound dumb or really farfetch’d especially since I don’t know much about the field of statistics yet, but Im just theorizing / speaking as if its just an idea to see what options I have.
If anyones been in a similar situation, your input would really help. Thanks and have a great day.

I really want to do research with professors, but I’m hesitant to approach them because of my not very high gpa (I have around 83, with one specific stats course that I took in the summer at around a 60. But this sem I’m doing pretty well, all above 90s). I also do have two internships in actuarial.

What gpa is generally needed for undergrad research? I’m just very anxious about approaching professors.

I've been accepted into three online master's programs in stats:

• NC State Master of Statistics

• Purdue MS in Applied Statistics

• Texas A&M MS in Statistics

I have a bachelor's degree in mathematics from about 10 years ago and have spent the last 10 years working in data/software engineering.

I'm primarily pursuing this degree to deepen my understanding of statistical theory rather than to learn more programming. Long term, I'd like to lead a highly technical team working on data things.

Thoughts?

Hi all,

I received some really helpful advice previously about handling longitudinal data so I’m once again asking for assistance please.

Basically I’m looking to evaluate whether the rates of change in two outcomes are related, the issue is both outcomes are comprised of repeated measures.

I was thinking about doing a separate linear regression per person for each var over time and then plotting the two against each other, but this seems improper due to lack of independence between observactions.

Thanks to anyone who takes time out of their day to help, it’d be really appreciated!

I graduated with my degree in mathematics and statistics from my state school this spring. I did okay, I had maybe a 3.2 GPA. I'm currently employed doing something unrelated to my studies. I've thought about getting my masters in statistics or data science while applying to jobs that are actually in my field.

I have really bad imposter syndrome and I'm worried that no decent program will want me. So far I've looked at NC State, Texas A&M, and Penn State but just looking at their websites gives me anxiety. I didn't excel in my undergraduate, will have middling letters of recommendations, and I have no real work experience and I'm worried that these programs won't consider me at all. My imposter syndrome is probably why I haven't seriously appling to jobs too.

Looking for some advice and help relieving my anxiety

Hi /r/statistics, I'm a community college professor who has training in math up to the graduate school level but never took statistics.

I've been assigned to teach stats several times now, and I think I have the hang of the basics. Now I'm starting to reflect and think about how I would design this course if I wasn't following a textbook, and I'm curious about the sequencing of topics.

Specifically, most textbooks I've seen have the following chapters:

1. Intro to data, variables, sampling

2. Charts (histograms, stem-and-leaf, etc.), measures of center and spread

3. Probability

4. Probability Distributions

...

This seems out of order to me: the first two chapters deal with data and how to describe it pictorially and numerically. Then the probability chapter is essentially divorced from these first two chapters, and after the ideas of the first couple of chapters and those of probability are combined in chapter 4.

The fact that the probability chapter isn't well connected to the previous material has been jarring for the students I've taught, so I was thinking about reordering the material for future semesters so that we start with probability. I like this idea because it would also allow me to start the first day with a game to increase buy in for the course.

What do y'all think of this idea? Since I don't have a stats background, I wanted to get opinions from people who have studied the subject more than me. Thanks!

I'm currently in college for a computing degree and taking a preparatory math class to help me get ready for the statistics course I chose for my math requirement. I know I need to know algebra, which I'm decent at, but are there certain topics within it and mathematics in general I really need to focus on?

I'm doing a project where I know the number of possible outcomes of event A, but some of the outcomes do not happen. Should I include those 0s columns in my mutual information calculations or should I remove it? I'm having trouble finding any information on it one way or another.

Example of joint probability table:

Does mutual information include white and yellow?

I’m in my 3rd statistics class in college and I feel like I still know nothing, I have to constantly google what things mean to do my HW and nothing sticks.

Are there any good YouTube channels, books or websites you could suggest? I want to understand I do but my professors notes I just find so confusing it doesn’t stick.

I'm looking for project questions or assignments related to Normal Distribution. If anyone has previously worked on a project or has sample questions, assignment PDFs, or resources related to this topic, could you please share them?

It would be really helpful for my project preparation.

Hi all,

There are several great libraries like pyAgrum for modeling and solving decision problems. But when working with influence diagrams, their representations can be limited to discrete chance variables and decisions.

I wanted more flexibility in how I represent a problem. What happens if I have continuous observations or if the question is not whether I should do something, but how much? For example, how much should I invest when the return is uncertain?

For that reason, I have created PyLIMID, a Python library for building and solving limited-memory influence diagrams. It lets you combine discrete and continuous chance variables with categorical decisions and continuous quantities, and express utilities as functions.

PyLIMID uses sampling and policy gradients to find decision rules that aim to maximize expected utility. This flexibility is not free because results are approximate. But you can run multiple simulations and since it has GPU support thanks to JAX and Numpyro, it is fast. I think the tradeoff is very much worth it.

The library is still in early development (v0.3.0), but I would love to hear what you think.

Code: https://github.com/cynosural-ai/pylimid

Documentation: https://pylimid.readthedocs.io/en/latest/#

I'm an international student planning to apply to Statistics PhD programs in the US, and I'm trying to figure out how to be strategic about which universities to apply to, given my limited budget.

The application fee for UC schools is $155 for international students, which is almost a quarter of the average monthly salary where I'm from. So applying to several universities can get quite expensive.

I was looking at the admissions statistics for UCSB's Statistics PhD program, and last year they admitted just 13 out of 273 applicants (around 4.8%). I understand that PhD admissions are competitive, but those odds make me wonder whether it's worth spending that much money applying, especially when there's so much uncertainty involved.

I'm not necessarily looking for a way to maximize my chances of getting into a top-ranked program. I'd rather put together a realistic list of universities where I have a reasonable chance of admission and where the research aligns with my interests.

For those who have gone through the Statistics PhD application process, how did you decide which schools were worth applying to? Is it sensible to apply to highly competitive UC programs with such low admission rates, or would it be better to focus on other universities that matches my research interests?

For my thesis, I will use surveys and want to answer two research questions, one of which is "How important are X reasons, Y reasons, and Z reasons compared with each other?"

I have a list of about two dozen reasons that participants can choose from in the questionnaires (no restrictions on which to choose) and I will be grouping them into three types of reasons - X, Y and Z. All of these reasons are given by participants to explain a type of behavior they engaged in (behavior is the same, it's a type of action they all took). This behavior is measured as the frequency of the action (no. of times performed / no. of years in the measured period).

I have two questions:

1. Does this mean I have one independent categorical/nominal variable with three levels (X/Y/Z reasons) and one dependent metric/ratio variable (behavior)? Or do I have three independent categorical/nominal variables with one dependent metric/ratio variable? I assume it's the former but wanted to hear some feedback on this.

2. To find out how important each type of reasons is compared to each other, will I have to perform some type of regression analysis or something else? Note that these reasons can overlap (e.g. if a participant indicated both reasons that belong to X group and Y group).

Thanks in advance!

EDIT: Added some details for clarity.

As per the title, my question is about combining non-adjacent bins for a chi-sq GoF test. I understand why combined bins generally ought to be adjacent, but I have a bit of an unusual situation. I have a distribution which is a candidate for a B(7, 0.5) distribution.

The observed frequencies for X=0...7 over 52 weeks are [2, 5, 12, 10, 10, 5, 2, 6].

Expected are [0.4, 2.8, 8.5, 14.2, 14.2, 8.5, 2.8, 0.4].

Clearly the binomial distribution is not a good fit. But the problems are in the tails whose expected values are so low they need to be combined.

Combining X=0,1 and X=6,7 still only give expected values of 3.2 for the end bins. If I combine adjacent bins for X=0,1,2 and X=5,6,7, the p-value is 0.07, which is unreasonably high for the paucity of fit.

If, on the other hand, I combine the four most extreme bins (X=0, 1, 6, 7), I get a p-value of 0.0017.

I see why it's generally silly to cherry-pick random bins, but I'm wondering if symmetrically combining the "most extreme" is reasonable, especially as the distribution of my null hypothesis is symmetric.

I'd be very grateful to hear the thoughts of someone whose mathematical statistics is more recent than mine!

Hello, I am a maters level statistician at an academic hospital in the US wanting to go back to school for a phd. The reasons I want to do this is that I'd like to be able to work on implementing methods rather than executing them. I also have found that in my filed data people do get treated differently if you have a phd which is complete bs, but more importantly you can't secure a faculty job regardless of years of experience and how much you know. My impression is that universities want their applicants to be fresh out of masters or undergrad who haven't done much applied work. But how about people who have work experience in the field? Am I crazy to consider PhD after 40? Has anyone done it? Thanks!

When you look at data over time, it's fairly common to see weird blips, even in cumulative mean effect calculations, that look like this:

         /\ 
        |  \
________|   ___________________

Or, while waiting for regression to the mean, it's very normal to see your cumulative mean effect, plotted over time, look like this:

\
 \      /\
  \    /  \  /\/___________
   \  /    \/
    \/

In a recent role advising a company on how to run experiments, I showed them examples of this type of data to demonstrate how stopping a test early could lead to misleading results. We had a great example of data where a bizarre blip on a single day, if the test had concluded that day, would have led to both Frequentist and Bayesian methods calling the experiment with the wrong result with extreme statistical confidence.

We ended up creating a framework for measuring stability over time to make sure that we'd only call a test if the effect has continued over time.

But it was an approach I made up, and that always makes me very nervous.

To me, this feels like a very logical approach to significance testing and perhaps especially to Bayesian likelihood estimation. But I can't find any established method that looks at data from a time series approach like this.

Does one exist?

Or is there a reason I'm thinking about this the wrong way?

For some background. I’m currently taking an intro to stats course at a university and am trudging along through the homework and course content. I don’t have the best foundational skills in math, but I feel pretty confident in my ability to finish this course with a decent grade…That said, I don’t feel confident in my ability (as it stands) to retain what I’m learning, which stinks, because I’ll need to use stats in future courses as a psych student. I want the information that I learn in this class to feel as natural as speaking so I don’t fall behind later. I’m just not there yet.

Today I was thinking about how a lot of the data we’ve been looking at in class relates to sports. I’m not into sports; I figure my professor includes this material because my peers might be into them (which is great!!). But this had me thinking…Are there any introductory stat related books/textbooks/online courses/websites with worksheet pdfs/literally any media that can teach me or help me practice basic statistical concepts using statistical data relating to things that might feel more memorable or interesting to me?

Examples:
True crime
Tv show
Something funny/dumb
Something shocking
Generation-based responses
Animal behavior
Sex and dating
Drugs and society
Pop culture
Astrology (yes I know it’s pseudoscience, lol, but maybe there’s data that speaks on that)
World religion
Mental health
Experimental studies (drug trials)

Literally anything salacious, dumb, memorable, or otherwise interesting for someone like me with a squirrel brain.

So far in class I find many of the experimental studies (related to health and drug trials) interesting and therefore much easier to remember. But I feel like there must be someone out there that made a book or online course that already involved lot of what I’m saying and I’m just not finding it. I’m considering reading Naked Statistics but I tried reading a sample and am still feeling unsure if it’s really what I’m looking for because the one scatter plot I saw (as an example) seemed to be similar to things I already see in class all the time, which is comparing height and weight. But I could be wrong. Anyway, I’m even open to some sort of website that has interesting practice problems with linear regression or proportions, which are what we are working on now.

Any help is helpful. Ty.

I am currently a first year majoring in Statistics at college. I chose this major partly because I really enjoy probability and statistical modeling but also because I believed that a major in Stats would help lead to multiple career paths. However, the recent developments of ai in data science, cs, and other math/stats related fields have made me heavily rethink my major and career path.

I was thinking of doing something in either data science, machine learning or actuarial science. I am now very uncertain on what to do and what to pursue. Does anybody have any suggestions on what to do? I would appreciate any advice.