I’m not really interested in becoming an MLE maybe BA or Data Science if my grades improve. I wonder in the age of AI if it’s worth learning how to code or if my time would be better spent just looking at AI code and seeing if it’s doing what I want. I seriously don’t want a job where I have to code frequently because it’s not my thing.
Interested in your thoughts
For instance
- The board pairs a little over 1/3 of the time
- If the flop is rainbow a flush comes in about 4% I think?
- Front door flush is about 36%
- Higher straights are much more difficult. I think they are about 1/3, but Broadway straights are immune to this. 56 on a 478 flop is much more vulnerable than a 234 flop
In aggregate I think I've come close to the an answer (about 25%) by just putting in hands against straights multi-way, but does anyone have a way to get to a more precise answer?
I'm confused about what to do for my data. I can't use the common equal variance tests in my software because those are for one-way models only. May I ask for suggestions?
In JMP, I've discovered a heteroscedacity test (no name for the method, just heteroscedacity test) where it shows the variances of the two one-way models and the whole two-way model graphically with no p-value involved. However, some results suggest that variances are not equal in some one-way models, but when you look at the whole model, variances are equal. If this method is correct, should I just use the whole model graph when testing for the assumption for equal variances? Or should all the one-way models and two-way model show equal variances for the data to achieve the equal variance assumption?
Residuals are used in testing for assumptions, not raw data, right?
Suggestions are highly appreciated.
Although I feel like a dummy when going back to relearn the basics, I do gain a different and deeper understanding of the formulas. Is it always going to be like this? Do you guys deep into your career still go back to the basics and deepen your understanding? Is it still like this 10 years later? Its my first time studying in uni and I dont like the idea that I have to sometimes just accept that I wont have full clarity over the mathematical tools im using, and that only later on the tools gain clarity. Sorry for any misspellings english is my third language.
Hi! I’m an undergraduate student currently working on our research, and I’d like to ask for advice regarding our questionnaire.
Our questionnaire has two independent sections that measure different constructs:
Behavior - 5-point Likert scale
(Never-Always)
Perception - 6-point Likert scale
(Strongly Disagree-Strongly Agree)
These are separate constructs, and we are not planning to combine them into one overall score or compare/correlate the two scales. We simply want to analyze and interpret each section separately.
So my question is: is it statistically/methodologically acceptable to have different Likert scales in different sections of the same questionnaire, when the sections measure independent constructs and each section has its own separate computation and interpretation?
Thank you!
We have released a new version of anvl, which brings fast array computing to R and aims to serve as a basis for implementing costly numerical algorithms natively in R.
It runs on GPU and CPU and has support for autodiff as well!
It has gotten a lot more features, so you can check it out: https://github.com/r-xla/anvl We have also started the process of submitting it (and it's dependencies) to CRAN!
we have 1 day gap for my intro to stats class and it is GRUESOME - I am really bad at maths. Our professor barely teaches 1 problem and moves on and the problems we get in the exam are not straight forward, we have to figure out what test to use. We literally follow different formulas besides the one given in tbs so referring to textbooks for explanations is hard because they keep using their formulas to explain it making it all so confusing
The main issue is I am having trouble remembering the formulas, because every step has 1 formula, etc. (ie. ANOVA, T-test)
I have -
Chi-square test, Mann-Whitney U test, Wilcoxon Signed Rank test, Kruskal-Wallis test, Friedman’s test, Scatter plot; Correlation methods: Pearson’s correlation, and Spearman’s rank correlation, Z-scores; t-test – One-sample t-test, independent samples t-test, Paired samples t-test; One-way Analysis of Variance (ANOVA), Range, Quartile Deviation, Average Deviation, Variance, Standard Deviation, – Bar graph, Pie chart, Line graphs, Histogram, Frequency polygon, Frequency curve, Ogive; Measures of Central Tendency: mean, median, mode – calculation
Literally any and all tips will be appreciated
Hey, so I have a BS in Biochemistry, an MPH in Epidemiology with a graduate minor in Biostatistics, and I'm currently doing an MA in Behavior Analysis.
I love statistics and its application to health behavior and human interactions. I knew I wanted to do my PhD in Biostatistics during my MPH but I felt insecure to apply because my degree didn't have Biostatistics in the title. Silly, I know that now.
However, I haven't had experience with Biostatistical research since my master's which I graduated from in 2022. I did do qualitative and mixed methods research as a Program Manager at my university that I got my master's from until 2023. I currently work in the field of special education and it kind of fell into my lap mostly because of the terrible MPH employment market and also because I do like what I do. I just don't like it enough to make it my lifelong career.
I want to do translational research in mental health and health behavior. I want to study how to apply statistical principles to those areas.
My question is, is it feasible for me to pivot to a PhD in Biostatistics? Is there something else I need to do to make myself a better candidate for admission? I was considering another master's in Data Analysis or Statistics of other related fields. Is that necessary or are there simpler ways for me to get there?
ETA: I did get into a PhD in Epidemiology but did not enjoy the theoretical epi as much as the biostats. I got A's in all my biostats courses but didn't do well in some of my epi courses. So I left the program.
Half of my courses (literally split down the middle) in my MPH were biostats because I had the flexibility to choose them as electives. I learned SAS and R.
I’m a third year college student and the highest level of math I’ve taken is algebra 2 with trig. I’m currently a finance major with a concentration in corporate law so business stats is a required course. The problem is, my class is fully online (videos, homework, and exams), and my professor is not the best at explaining concepts. It’s also too late for me to drop this class as I cannot afford the sunk cost. That being said, I have a few questions:
1: How valuable are the concepts in regard to my career goals?
2: If they are valuable, what is the fastest way I can understand the concepts (eg; why standard deviation matters)
3: If there is a possibility to utilize my online resources that will ensure that I pass, should I just use them instead of wasting my time trying to relearn everything?
Say you are developing some sort of statistical test and you have run simulations and applications, and now you wanna work on its theoretical guarantees (asymptotics, efficiency, consistency, etc.)
Can you not just feed in the test statistics, assumptions, etc. to your favorite intelligent AI and get it to derive everything?
In this instance, is the focus of statistical theory going to be more on the algorithms/computation side and dealing with issues like scalability going forward, while leaving the math to AI?
Didn't AI solve that big unsolved problem in pure mathematics the other day (Navier-stokes or something, I think)
we're undergrads and are currently conducting a study on a company with employees as the respondents. The company have multiple offices that are located across different locations. We were given multiple survey points so that employees can be sampled.
The problem is that one of the survey point which consist of 2 offices declined to participate in the study.
what is the implication of this?
can this be solved?
is this a reason for us to fail?
additional info: the needed respondent for that particular survey point is only 10 people
[research]
I have an M.Pharm in Pharmaceutics, and recently I’ve started developing a strong interest in statistics and quantitative methods.
My academic background is primarily in pharmaceutics, so I’m still relatively new to statistics. I started learning statistical concepts through YouTube, Reddit, and online resources, and I’ve become increasingly interested in understanding not just how to use statistical tests, but how statistics can actually be used to solve real problems in pharmaceutical research.
During my M.Pharm work, I have already had some exposure to statistical methods. For example, I have used Design of Experiments (DoE) for formulation optimization and statistical approaches for evaluating/validating pharmaceutical formulations and processes.
However, I don't want my statistical knowledge to remain limited to things like “which statistical test should I use for this dataset?” I’m interested in going deeper and eventually using statistics/computational methods as a tool for pharmaceutics research, formulation development, drug delivery, and potentially drug discovery.
I’m currently thinking about pursuing a PhD in Pharmaceutics/Pharmaceutical Research, and I would ideally like my PhD to remain strongly rooted in experimental pharmaceutics while incorporating meaningful statistical or computational components.
For example, I’m curious about areas such as:
DoE and QbD for formulation and process optimization
Statistical modelling of drug release and dissolution
Pharmacokinetic/pharmacodynamic modelling
Predictive modelling for formulation development
Machine learning/AI in drug delivery or formulation development
Biostatistics applied to pharmaceutical experiments
Multivariate analysis and process monitoring
Process analytical technology (PAT) and data-driven manufacturing
Stability modelling and shelf-life prediction
Statistical approaches to drug discovery and development
Population-based modelling or PBPK
Using experimental data + computational models to guide formulation development
I’m particularly interested in the idea of having a 75%+ pharmaceutics/experimental component, while using statistics/computational methods as a powerful supporting component rather than completely moving into a pure computer science or mathematics PhD..
Give me your insights please