Hi everyone, I have a cyclic/Latin-square-style counterbalanced repeated-measures design (as shown in image above), and I’m trying to work out the right way to use a Generalized Linear Mixed Effects model to analyse the data for it as I’m new to GLMMs. My aim is to compare how different cell-culture compositions affect the firing rates (expected to be of lognormal or tweedie distribution) of neural cultures while they’re being “trained” to perform tasks.

The study uses 100 neural cultures split evenly into two experiments of 50. Experiment 1 uses Culture Type A and Experiment 2 uses Culture Type B (so culture type is nested within experiment, not crossed). Each experiment’s 50 cultures are prepared in two independent batches of 25, and within every batch the cultures are made up with one of three cell-culture compositions (A, B, or C) - giving four batches and all three compositions in both experiments.

Each culture is then trained under 9 environmental conditions in a counterbalanced order, performing 2 tasks per environmental condition at 600 trials per task.

The GLMM model I currently planning to use is:

Firing rate ~ Culture Type + Batch + Cell Culture Composition + Environmental Condition + Task + (1 | Culture ID)

However, I feel that culture ID is insufficient as a random effect and it should include the other variables too, although I am unsure of what combination to use, especially since there’s so many of them. I am also worried about having to add dispersion models in the case of over/under-dispersion as well, since my response variable is not normally distributed. Hence, I would like to seek comments and advice on my current model from those who are experienced in working with GLMMs. Thank you very much in advance for the help.

This is an image of how my experimental design looks: https://imgur.com/a/v4pU3tN

Hi everyone,

I'm in my early 30s and hold a Bachelor's degree in Management and a Master's degree in Management with a focus on Business Analytics, both from a Swiss university. I've worked in the public sector for several years, first in financial analysis and then in an HR-related role.

I currently have a stable job paying around CHF 100,000 per year, but I'm no longer fulfilled by it. My work involves very little quantitative analysis, even though statistics, statistical modeling, forecasting, and data analysis are what interest me most. I'm proficient in Excel and have some knowledge of R, SQL, and statistics, but little recent professional experience in these areas. I enjoy statistical methods and analysis more than intensive programming.

For over a year, I've been actively trying to transition into more analytical roles (finance, controlling, statistics, data analysis, etc.). Despite numerous applications and several interviews, I haven't managed to find anything. I'm also wondering whether my background exclusively in the public sector is holding me back.

I had set myself the goal of leaving my current position within the next few months, as I'm concerned that staying longer in a role with little quantitative work will make it increasingly difficult to transition into a more analytical career.

I'm now considering a PhD in statistics, econometrics, biostatistics, data science, operations research, or a related quantitative field. The idea of conducting research, deepening my knowledge, and potentially developing statistical methods genuinely appeals to me. However, my long-term goal would be to work in quantitative research or R&D, ideally in industry, rather than pursue an academic career.

I'd also like to remain in French-speaking Switzerland and am not considering relocating outside this region, even temporarily.

However, I'm hesitant about this decision, as it would mean taking a salary cut of approximately 20–50% for three to five years, with no guarantee of improving my career prospects. I also wonder whether my interest in pursuing a PhD is partly driven by my desire to leave my current job.

I'd particularly appreciate hearing from people who have completed or are currently pursuing a PhD in Switzerland:

1. Has anyone started a PhD after 30, following several years of professional experience? Would you make the same decision again?

2. What does your day-to-day work look like? How much time do you actually spend on research and modeling compared to teaching and administrative tasks?

3. Has your PhD significantly improved your career prospects outside academia, particularly in quantitative research or R&D?

4. Is it realistic to pursue a quantitative PhD with a Master's in Management (Business Analytics) rather than a strong academic background in mathematics or statistics?

5. Is it possible to enter applied statistical research without a PhD, for example as a junior researcher or research assistant, after several years working in a different field?

6. What would you do in my position? Keep looking for an analytical role, try to join a research team, pursue additional training in statistics, or seriously consider a PhD?

I'm willing to accept a more junior position or a reasonable salary reduction to change careers, but I'd prefer to avoid committing several years to a PhD if a more direct path exists.

Thanks in advance for your experiences and advice!

Hello everyone! I made a multiple Regression model with many different prediction variables, with one of them being type of medication (dummy coded, compared with one particular medication as a reference).

The outcome parameter/dependent variable is the side effect of weight gain in kg.

I want to rank the medication according to their association with the outcome parameter compared to the reference medication. The suggestion that I found on stack-exchange seems to be touse the unadjusted B Parameter because the adjusted one is not suited to categorical variables.

But I wanted to check here because I trust this sub - is this correct? And if yes, could someone explain it to me in a simple way? :)

I have done a fair amount of multilevel bayesian modelling in R and Stan, and read books like McElreath's "Rethinking Statistics", so I am familiar with probability distributions, conditional probabilities and concepts like entropy and deviance, but I would like to get a deeper understanding of the underlying probability theory and statistical theory, to make more informed decisions in my modelling. I am comfortable reading math, but I haven't familiarized myself much with stuff like measure theory or advanced linear algebra (my mathematical background is more related to number theory, so not directly applicable). Are there some good books that present theory for readers with an applied background? (I am also familiar with applied frequentist modelling, but I feel like bayesian is closer to probability theory already)

Hello all,
I know this question gets asked a lot but my background is different from all the threads I have searched.
I have a degree in engineering and have been studying probability for both personal and professional reasons.
I am finishing up “introduction to probability” by Hwang & Blitzstein. Absolutely loved it, but I feel like theres no unifying theory that connects all the definitions introduced. What are the best recommendations for learning measure theoretic probability given my background:

- Familiar: linear algebra, multivariate calculus, combinatorics (from Hwang)
- Not familiar: real analysis, measure theory, graduate level statistics

Is there a single textbook that can take me from zero to familiar or do i need to tackle these topics separately?

Thank you.

Hello there!

Question: Is a BS in Statistics too theoretical?

Context: I've been looking through the curriculum. I am concerned that it might be to theoretical to the point where recruiters will ultimately pass over me.

I'm still in my research phase and would greatly appreciate some insight.

******if I were to follow through, I would want to look for Data Analyst roles. I also recognize that a lot of jobs are now looking for higher than a BS.

Hi everyone!

I’m currently a Master’s student in Data Science and AI. While I'm comfortable with standard machine learning pipelines, deep learning, and algorithmic modeling, I’ve realized I have a gap: I want to deeply master classical statistical inference and Bayesian statistics.

Beyond wanting to bridge that gap to better understand modern AI architectures (uncertainty quantification, probabilistic machine learning, etc.), I’ve honestly just grown to love the mathematics and theory behind it.

Given my background in linear algebra, multivariate calculus, and programming, I’d love to get recommendations from this community:

1. Textbooks & Theory: What are the gold-standard books to take someone from standard ML intuition to rigorous inferential and Bayesian proficiency (without hand-waving)?

2. AI-Adjacent Bridging: Are there resources that specifically connect classical/Bayesian statistics to modern AI, probabilistic programming (like PyMC or NumPyro), or Bayesian deep learning?

3. Study Strategy: For someone learning out of pure passion alongside a demanding data science master's, what pitfalls should I avoid?

Any advice, roadmap suggestions, or favorite hidden-gem resources would be deeply appreciated! Thanks!

Sorry for the long rant - this is the reflections of a non-statistician on a very pressing problem in the biological sciences.

I am researcher often working with multivariate datasets derived from the same samples, but with very different nature. An example would be 100 stool samples analyzed by their microbiome (the microbial composition) as well as for metabolomics (the chemical composition). The purpose here would be to associate the microbes with the metabolites, principally to see which microbes produce what metabolites and ideally correlate this to the status of the host. A wonderful example would be to observe how Roseburia produces butyrate and this protects the colon.

I am, however, missing some statistical rigour in these types of analysis. Most often, each type of data is processed individually and integration is merely done by discussion. Occasionally, spurious and cherry-picked variables are regressed in order to produce some level of statistical evidence. More ambitious scientists use dedicated packages, such as MOFA or DIABLO, for a more encompassing integration, but it seems to require a lot of fine-tuning and rarely produce proper conclusions.

The fundamental issue is the difference in data topology: microbiome data is by design highly zero-inflated count data, often expressed as ratios, whereas metabolomic data is highly spurious numeric data. Both are usually of high dimension - often between 100 and several 1000 - and the distribution of each feature appears log-normal. This, by the way, is also the case for other common omics technologies, such as transcriptomics and proteomics. In contrast, the dependent variable is often binary, i.e. healthy vs. sick.

We have sophisticated methods to handle individual datasets, all of which rely on years of insight into the data topology in order to carefully transform and fit it with proven models - good examples are ANCOMB2 for microbiome data or DESeq2 for transcriptomes. The details here are key and are hard to generalize for multiple topologies.

One approach often used is to normalize and scale the datasets, aiming at making it gaussian and scale-invariant enough to use 'normal' statistics, such as regression and PCA. The motivation here is noble and the logic understandable, but the mathematics is in the way: although a log transform would be obvious for an apperant lognormal distribution, the zero-inflation makes most numbers undefined, and even if these are kept at zero, the result a bimodal distribution rather than a normal one.

This is probably enough of my ranting, but I hope my point made it through.

My actual question to you statisticians is how you would approach this. Are there some algorithms I am not yet aware of? This is a huge issue in modern biology, so if anyone needs a good problem to solve, this would be it.

“hi please read this folder of regulatory guidance documents, published papers, and the textbook, then take input data from the X branch on git and do an appropriate analysis in R with plots and tables and also write up the statistical analysis section of this CSR, then pretend youre a reviewer, what comments would you have? Then address the comments. Please push your script and all results to an “analysis” folder, then summarize the results in an email to the VPs”

Unless its a very complex study (its not) the AI will do just as good as me or better and in 30 minutes instead of a month. How much longer will I be employed at this point? Maybe theyll keep me on as a consultant for 2 hours a week to review what the AI did?

Good evening. I would like some book recommendations if that's ok to do in this sub.
Next semester I will take a class that covers the following topics:

- Foundations of Bayesian Inference and Spatial/Temporal Dependence
- Statistical analysis of integer-valued time series
- Conformal Prediction
- Bayesian nonparametric statistics for change point detection

Its not an exaggeration to say that I know absolutely 0 probability and statistics, for instance I don't even know what a random variable or expected value is. I have about 3 months to prepare for this class. What books would you guys recomend?

If you think this is impossible tho (which I am starting to suspect), just let me know so I can drop the class

Hey guys. I suck at stats excuse my dumbness. So if I do a 2x3 factorial anova. If get a significance interaction or significant rowxcolumn f score/ interaction, I perform a post hoc to test simple comparions using the cell means right ? Here, do I also perform a post hoc on the main effects using the marginal means if either was significant ? Or there’s no need ? If the f score for the interaction was not significant then I perform post hoc on the main effects in the case that either or both are Sig. is that correct ?

I need help working out what to do with some data. Little information about the data:

We have 2 groups (control and intervention). Data collection is occurring over multiple time points (TP1, TP2, TP3, TP4). At each time point, the number of observations can vary, and different individuals may respond at one time point but not another. I am using unpaired and paired analyses (where possible).

We are calculating t-tests and using Wilcoxon tests for sensitivity when comparing between groups. For comparisons between time points (TP1 vs TP2; TP2 vs TP3; TP3 vs TP4; TP1 vs TP3; TP1 vs TP4; TP2 vs TP4), we use t-tests and Wilcoxon tests, followed by a linear mixed-effects model to assess significance between groups and time points. Lastly, doing Hedges’ g calculations to assess the magnitude of change (for effect size, using paired data).

I’m having a little difficulty visualizing the comparison and sharing the results. I’m not sure I’m running the right tests.

Hello , I am enrolled in Bscn nursing and taking statistics. I’m struggling with the content especially hypothesis testing. We have topics like correlation , simple linear regression , chi-square, estimation, measures of impact coming up.

Is anyone able to advise on how best to master these subjects ? Or do I need to get a tutor. Thank you

I work in analytics and we send a weekly forecast to ppl with zero stats background for ages they either treated our number like a promise or ignored it. last month we tried a few things, quick notes:

- one single number: worst, ppl treat it like a promise n get annoyed when its off

- 80% and 95% bands: nobody read them lol

- "70% chance its between X and Y" best by far, its how ppl already read the weather.

- a simple up vs down % got the most replies from the team.

- a heatmap of ranges: managers loved it, everyone else found it a bit much

still testing, curious how others do this

With all the recent developments in time series foundation models, I've been wondering how much they actually improve forecasting compared to traditional statistical approaches.

Models like Chronos-2 and TabPFN-TS are getting attention, but methods like ARIMA, ETS, and other statistical forecasting techniques are still widely used.

And then there's the middle ground: gradient-boosted models, N-BEATS, N-HiTS, and other deep learning approaches.

I think the interesting question isn't necessarily which model is the most advanced, but which one performs best under realistic conditions.

For example:

• Do foundation models consistently outperform statistical baselines on intermittent demand?

• How much difference does feature engineering make when promotions and external variables are involved?

• Are more complex models worth the additional computational costs?

• How should we compare models when forecast uncertainty matters as much as point accuracy?

• Does the best-performing model change across different product hierarchies and demand patterns?

I'm helping organize a two-day, hands-on Time Series Bootcamp at Packt on November 28–29, where three forecasting practitioners will benchmark statistical, ML, deep learning, and foundation models using the same retail dataset.

Here is the full workshop details

The instructors are Graeme Davidson, Lei Ma, and Jeffrey Tackes. The sessions will cover time-aware backtesting, model comparisons, uncertainty estimation, hierarchical reconciliation, and ensembling.

I am currently a freshman stats major and I am really considering adding a pre-med/pre-dental track. I wanted to see if there were any benefits of a stats major in a pre med environment or if I should consider switching majors instead. I enjoy statistics and probability but I don't know if there are inherently any benefits to it when going to a pre-med/pre-dental track

Hello! I am enrolled in a Data Analysis master’s degree at a Management university. I had previously learned a little bit of Statistics during my undergraduate degree. However, not to the level I am expected to know now. I am learning Applied Statistics (learning about the Central Limit Theorem, z- and t-tests, ANOVA, chi square, Mann-Whitney-Wilcoxon tests, etc).
I am completely and utterly lost. I have e-mailed the professor for tips and books, but he sent me a reference for a book that I can’t find online (because it’s not in English).

I am looking for advice or help of any kind. This is one of the books that’s in the obligatory bibliography of my course (Keller, Gerald; Statistics for management and economics) but I found it too difficult to understand. I am a beginner and I need to understand the concepts. To me, highly complex mathematical formulas are just gibberish.

So, if you have any resources, such as books, videos, manuals, courses (that are not paid or are relatively cheap), I would be more than grateful.

Thank you in advance and I am open to any further questions you might have!

I’ve been working on a game system. In it, you roll multiple dice, and if the dice falls under or at a specific number, that is counted as a success. The success number can be between 1 and 5.

I’m trying to determine how to make a table that shows the likelihood of success on a number of dice between 1 and 5. I know the average of a d6 is 3.5, but I dont know how to use this information. I’ve sort of guesstimated that it looks like this, but I wanted to double check with people that know what they’re talking about. Tje table linked shows the number of dice rolled on the top, the success number on the left side, and then everything in the middle should be the average number of successes for that number of dice.

As a student at Cornell wishing to do a master's in statistics, I noticed that my school offers a few master's programs, but I'm not sure which is the best fit for me. Currently, I have the choice between an MPS in statistics and data science (Master of Professional Studies in applied statistics), a master of Science in statistics, or an alternative option, a Master of Engineering (MEng) in computer science. Which is the best choice for me as a statistics + information science major with a cs minor?

FYI: The career paths I wish to go for are actuarial, data science/data analysis, biostatistics, ML, and Risk management (mathematical finance-related)

Croosposting because no one can seem to agree and at this point im genuinely curious.

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.