Hey all,

Currently an undergrad in stats and data science and I am aiming to do a masters in stats and phd in stats in Europe. Since I want to do a phd I am planning of doing a research masters/thesis-based masters.

However I haven't taken any proof based classes, only applied linear algebra and Calculus 1-3. And I don't think I have space in my schedule to put real analysis. I might be able to add a proof based combinatorics class but it would be very hard to add real analysis class in my schedule before I graduate.

Is real analysis required for thesis-based master programs and phds? Would I be able to learn the necessary proofs during my masters program if I didn't take real analysis?

I was wondering would my lack of real analysis in my undergraduate matter for PhD applications if I do well in my research masters? Wouldn't a PhD focus mostly on my masters courses than my undergrad courses? Would I be at a severe disadvantage not taking real analysis for a research masters in stats and also a PhD in stats?

Any advice would be super helpful!

I'm running my statistics for a behavioral experiment I did and my results are confusing my advisor and myself and I'm not sure how to explain it.

I'm doing a generalized linear mixed model with treatment (control and treatment), sex (M and F), and sex*treatment. (I also have litter as a random effect) My sex effect is not significant but my treatment is (there's a significant difference between control and treatment).

The part that's confusing me is that there's no significant differences for sex*treatment and for the pairwise between groups. (Ie there's no significance between control M and treatment M or between control F and treatment F).

Can anyone help me figure out why this is happening? Or if I'm doing something wrong?

Hello!

My plan is to work through the following books, in the order they are listed:

Mathematical Statistics with Applications, Mendenhall, Wackerly, Scheaffer (currently reading)

Applied Linear Regression Models, Kutner, Nachtsheim, Neter

The Elements of Statistical Learning, Hattie, Tibshirani, Friedman.

I've done an intro Stats and Stats Methods course a few years ago during my math degree, and I'm interested in pursuing a masters in applied statistics or biostatistics.

Is ESL overkill? What other books would complement this set and prepare me for grad school/industry? Is there anything you would swap?

Hi, I have a hypothetical question to ensure I really understand:
A researcher conducts a t-test for independent samples, assuming equal variances, and does not reject the null hypothesis. Then he conducts the test again, this time without assuming equal variances. Is there a situation in which, in the second test (without the assumption of equal variances), he would actually reject the null hypothesis?

If I understand correctly, the degrees of freedom when assuming equal variances is necessarily not smaller than when not assuming equal variances. But what about the estimator of the standard error? Is it possible that without the assumption of equal variances, the standard error is smaller, thus making the t statistic larger, which in turn leads to the rejection of the null hypothesis?

I'm imagining a jobs board with posts advertising academic projects that need stats help. Does anything like this exist and where could I find it?

I'm asking as a new MD trying to get some simple reviews published. Contributing to medical research is ideally something I want to include in my career going forward, but I'm looking at working in community environments without academic associations. I'm good enough at basic stats on my own, but for nuanced or messy data sets it'd be nice to know there is somewere to look to get extra eyen on, in exhange for an authorship credit.

I am a Data enthusiast, my manager from my previous (as a Data Analyst intern) told me one thing on my last day review that "You need to master statistics and math to excel in the world of Data". Since then, I tried few courses but they weren't that helpful. All my colleagues had a degree or a Phd in Math so they were absolutely tremendous in finding out trends. For eg:- The thing which took me hours to solve, they would solve it in 30 mins with the help of their excellent math and excel skills. I don't know where to start. All I know is that Mathematical mind is very much needed in nowadays. I have a background where I left Maths long back. And now I want to learn but don't know from where to start. Any tips, advice or Suggestions would be more than helpful...... Thanks!

Hello everyone,

I have a catagorical dummy, and in the model I wish to add a beta infront of it ( + b3 * catagorical dummy). Ofcourse in truth this is not 1 beta but multiple.

How to make that clear from the model. Is there another greek letter I should use?

Thankyou!

Hi there,

I've created a video here where I talk about the kernel trick, a technique that enables machine learning algorithms to operate in high-dimensional spaces without explicitly computing transformed feature vectors.

I hope it may be of use to some of you out there. Feedback is more than welcomed! :)

As the title suggests, I am almost brand new to stats. I strongly disliked math in high school and college, but now it has come up in my philosophical ventures of epistemology.

That said, every explanation of Bayes Theorem vs the Frequentist Theorem seems vague and dubious. So far, I think the easiest way I could sum up the two theories are the following. Bayes theorem takes an approach where the model of analyzing data (and calculating a probability) changes based on the data coming into the analysis, whereas frequentists input the data coming into the analysis on a fixed theorem that never changes. For Bayes theorem, the way the model ‘ends up’ is how Bayes theorem achieves its endeavor, and for the Frequentist, it’s simply how the data respond to the static model that determines the truth.

Okay, I have several questions. Bayes theorem approaches the probability of A given B, but this seems dubious when juxtaposed to Frequentist approach to me. Why? Because it isn’t like the Frequentist isn’t calculating A given B, they are, it is more about this conclusion in conjunction with the axiomatic law of large numbers. In other words, it seems like the probability of A given B is what both theories are trying to figure out, it’s just about the way the data is approached in relation to the model. For this reason, 1) It seems like Frequentist theorem is just bayes theorem, but it takes the event as if it would happen an infinite number of times. Is this true? Many say, well in Bayes theorem, we consider what we’re trying to find as probable with prior background probabilities. Why would frequentists not take that into consideration? 2) Given question 1, it seems weird that people frame these theories as either/or. Really, it just seems like you couldn’t ever apply Frequentist theory to a singular event, like an election. So in the case of singular or unique events, we use Bayes. How would one even do otherwise? 3) Finally, can someone discover degrees of confidence which someone can then apply to beliefs using the Frequentist approach?

Sorry if these are confusing, I’m a neophyte.

I decided to stop putting off properly learning R so I can have more tools in my toolbox, enjoy the streamlined R Markdown process instead of always having to export a bunch of plots and insert them elsewhere, all that good stuff. Before I unknowingly come up with horribly inefficient ways of accomplishing some frequent tasks in R, I'd like to explain how I handle these tasks in Stata now and hear from some veteran R users how they'd approach them.

A lot of data I work with comes from survey platforms like SurveyMonkey, Google Forms, and so on. This means potentially dozens of columns, each "named" the entire text of a questionnaire item. When I import one of these data sets into Stata, it collapses that text into a shorter variable name, but preserves all or most of the text with spaces as a variable label (e.g., there may be a collapsed name like whatisyourage with the label "What is your age?"). Before doing any actual analysis, I systematically rename all the variables and possibly tweak their labels (e.g., to age and "Respondent age" in the previous example) to make sense of them all. Groups of related variables will likely get some kind of unifying prefix. If I need to preserve the full text of an item somewhere, I can also attach a note to a variable, which isn't subject to the same length restrictions as names and labels.

Meanwhile, all the R examples I see start with these comparatively tiny, intuitive data sets with self-explanatory variables. Like, forget making a scatterplot of the cars' engine sizes and fuel efficiency—how am I supposed to make sense of my messy, real-world data so I actually know what it is I'm graphing? Being able to run ?mpg is great, but my data doesn't come with a help file to tell me what's inside. If I need to store notes on my variables, am I supposed to make my own help file? How?

Next, there will be a slew of categorical or ordinal variables that have strings in them (e.g., "Strongly Disagree", "Disagree", …) instead of integers, and I need to turn those into integers with associated value labels. Stata has encode for this purpose. encode assigns integers to strings in alphabetical order, so I may need to first create a value label with the desired encoding, then tell Stata to apply it to the string variable:

label define agreement 1 "Strongly Disagree" 2 "Disagree" […]
encode str_agreement, gen(agreement) label(agreement)

The result is a variable called agreement with a 1 in rows where the string variable has "Strongly Disagree", and so on. (Some platforms also offer an SPSS export function which does this labeling automatically, and Stata can read those files. Others offer only CSV or Excel exports, which means I have to do all the labeling myself.)

I understand that base R has as.factor() and the Tidyverse's forcats package adds as_factor(), but I don't entirely understand how best to apply them after importing this kind of data. Am I supposed to add their output to a data frame as another column, store it in some variable that exists outside the frame, or what?

I guess a lot of this boils down to having an intuitive understanding of how Stata stores my data, and not having anything of the sort for R. I didn't install R to play with example data sets for the rest of my life, but it feels like that's all I can do with it because I have no concept of how to wrangle real-world stuff in it the way I do in other software.

Hi! First time in this sub and i need a bit if help for determining the sample size of a population i don't know for my descriptive cross-sectional survey research. For context, my target population is young adults (aged 18-25 - unknown population) in a certain city that has a population of 19,189. I would appreciate help on how i can determine the sample size of an unknown population if i were to use purposive sampling or maybe recommendations of better sampling methods i can use for this.

I don't know much about statistics and am just trying to pass so i thank you in advance for any type of help!

Hey guys! A senior here undergoing my final-paper capstone project.

My project is all about testing whether our team's animation project can increase the level of knowledge of students about the university's cultural artifacts (since we have already done a previous basis-survey that clarified and supported this concern)

Our research paper's plans are to test via a pre-test and post-test Likert Scale questionnaire of the same questions before and after exposure to the animation, over the same samples/participants.

Let's assume that we will be having n=30 samples, with a 15-item Likert Scale questionnaire with a 1-5 scale (Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree)

After tons of research, I got to the assumption that I would rather safely use Wincoxon than Paired T-test for the fact that Likert Scale is ordinal (assuming it's also not normally distributed)

Would it be wise to evaluate the Wincoxon rank values for EACH question? Or am I right to assume that I can total all the Likert Scale data of a single sample of all 10 questions and use that as an overall sample for all 30 participants?

I'm quire confused on how I should proceed in analyzing this type of data set (since I am normally used to standard t-test evaluations), if I should do an itemized analysis or an overall analysis (if that's even possible).

Any suggestions or advice is very appreciated, thanks!

EDIT: 15-item survey instead of 10

So I want to do a moderation analysis for a three-category categorical moderator in a Poisson regression. Usually I simply do moderation analysis with Hayes' Process Makro but that doesn't let me do a Poisson regression. So I guess I have to do it manually.

I know how to do a Poisson regression analysis via Generalized Linear Models. I choose Poisson loglinear, select my dependent variable, pull my predictor into covariates, the covariates as main effect into model and select Include exponential parameter estimates in the statistics menu.

I have also attempted a moderation analysis within this before by mean-centering the variables and manually creating the interaction term. However, those were all metric variables back then, so I guess I cant do the same with my categorical moderator.

So how do I do it? And is there anything I have to keep in mind?

Do I have to mean-center my non-dummy independent variable? And how do I construct the interaction term? Do I need two interaction terms (one for each dummy)?

I am a CPA by trade with ~4.5 yoe in auditing. I have about 1 year left before I finishing my MS in statistics. Ideally, I would like to end up in a data scientist role, but I know the job market for those positions can be tough, especially in current times.

Are their any jobs I should aim for that would utilize my accounting experience and statistics? I have heard a few suggestions from other subs, but would appreciate input from others here.

I frequently hear about having a portfolio, but I was wondering if that’s a country specific thing.

I need to take an exam at the end of the month and stats will be on it. Thing is, I’ve never taken stats before. I need to know stats and biostats at the level of someone with a bachelor’s (not a math degree, I’m going into biology). Now I don’t expect to learn in a month that high of a level of statistical knowledge, but if I could get at least some knowledge that would be very helpful. Preferably in video format, but anything will do honestly.

Hi,

I've searched around for the answer to this and have had no luck so please point me in the correct direction if you can.

I am measuring the effect of a drug. That measurement can be quantified in several different ways. I'd like to know which of the 4 quantification method is the most sensitive to the drug (e.g. measures the largest effect). Is there a way to compare effect sizes (e.g. cohen's) between the 4 quantification methods?

I hesitated to say sensitivity because that naturally leads to a thinking of an ROC curve but I don't believe that's the correct route here.

Thanks, GBL

I am currently working with a data set consisting of 300 questionnaires. For an analysis I use a Kruskal-Wallis test. There are 9 metric variables that can be considered as dependent variables and 14 nominal variables as fixed factors. In total, I can therefore carry out 126 tests. After 28 tests, I noticed that every test is significant and the Eta-square is always very high. What could be the reason for this? It doesn't make much sense to me. What am I doing wrong? Could it be due to the different sized n's? For example, the size of n in one question is between 17 and 90 in the different versions. I work with Jasp. Should I use other tests to determine significant differences?

Hey guys I wanted some advice: I am studying public health but am going to take a lot of stats courses next fall to prepare me for going into biostats/epidemiology for graduate school, but the only related courses I've taken are intro stats and calc 1. I'm planning on taking nonparametric stats, programming for data analytics, and intro to statistical modeling. Have you folks found these courses to be pretty challenging compared to others? Are they perfectly manageable to take all in one semester? I don't want to bite more than I can chew since they are higher level stats courses at my institution and I haven't taken many similar classes. Thanks for any advice!

Hi, dont know much about statistics, but really interested in it. I asked myself whether an independent variable can be moderating variable at the same time. To make it clear:

x: independent variable

x is positively related to y1.

x is negatively related to y2.

The lower x the more there is a positive relation between y1 and y2, but this relation fades when x increases.

Is that realistic? How would i test for something like that?

Hi all, I have some questions about model/test choices stemming from a biological experiment.

Data/simplified experiment overview: We infected a host organism with a parasite and measured both host death (counts) and parasite abundance (counts) across different temperature treatments (factor). We've already done some straightforward GLMMs for death ~ treatment and abundance ~ treatment.

Questions: I'd like to unpack possible death and abundance relationships more. (1) At a broad level, higher abundance samples might also be higher death samples (i.e. temperature --> abundance --> death hypothesis). I think some straightforward correlation test is fine here. Even just plotting data and talking trends. Or simply discussing when the above models (death ~ treatment or abundance ~ treatment highlight the same treatment).

(2) Or, more nuanced, the per unit increase of abundance might drive more death at different temperatures. That is, at temperature A, each unit increase of abundance doesn't change much. But, at temperature B, every extra parasite drives a lot more death - even if overall abundance might be lower than generally observed during temp A. In a model, this might looks like: death ~ abundance*temperature.

Issues: In (2) I'm trying to use abundance as a fixed effect, when in reality it was a measured dependent variable. For biological interpretation, I'm comfortable navigating the caveats of we don't truly know if abundance drives death, or, if sickly hosts that are dying are more prone to carrying higher abundance. That part is okay.

But statistically, I wonder if there are structural problems in building a GLMM this way (e.g. collinearity with the temperature variable or other issues).

I've read that SEMs (structural equation models) might be a way forward, but this analysis would be a smallish add on for a project I'd like to keep moving along with my current skill set of classic bio/eco-stats and GLMs (freq or bayesian) if possible.

(and unfortunately, in this system we can't run experiments to control abundance directly)

Thank you!!!

Hi all, I'm graduating from a T10 stats undergrad program this semester. I have several internships in software engineering (specifically in big data/ETL/etc), including two at Tesla. I've been applying to new grad roles in NYC for data engineering, software engineering, data science and any other titles under the relevant umbrella since August. My callback rate is significantly low.

I've applied to a breadth of roles and companies, provided they paid more than peanuts for NYC. I've gotten referrals where possible (cold messages/emails), including referrals to Amazon which practically hands out OAs. I made over 100 different resumes over this time period. I posted a pitch to Linkedin. I applied within hours of roles being posted.

I was rejected or ghosted for most applications/referrals. Of around 600 applications I sent out, I've had a total of three interview processes (not counting OAs, received around 10 of those and scored perfect or almost perfect), all of which were at fairly competitive companies (think Apple, DE Shaw, mid-size techs, etc.). Never received an OA from Amazon.

I don't understand what's happening. I barely hear back, but when I do, I'm facing an extremely competitive talent pool. Have any of you had a similar experience? I'm starting to wonder if my "Statistics" degree is getting me auto filtered by recruiters. People with similar internship experience with a CS degree are having no issues.

TLDR: T10 stats senior with Tesla internships, applied to ~600 NYC data/SWE roles since August. 3 interviews total. Suspecting low response rate is due to stats degree vs. CS. Anyone else having similar experience?

How do you calculate minimum sample sizes for permutation tests?

Hello, I've recently studied about permutation testing through online resources and I really love the approach. It's so intuitive! I'm wondering if there's any guidance on minimum sample size requirements? I couldn't find anything on this topic to answer this question confidently. If I'm doing an experiment and want to use permutation testing to draw conclusions what sample sizes should I be targeting for?

I intuitively feel bigger sample sizes will help because smaller sample sizes will lead to more variance in terms of A vs B and thus a significant result is less likely to be obtained.

Not sure if this is the right place to ask, but l am a second year Psychology student taking multiple statistics classes. I find it easy to memorise formulas and steps for data analyses but I have always struggled with understanding the content. Even with simple things like SD, where I think I understand but then the meaning changes depending on context. I am now doing ANOVA, Post-hoc, planned-constraint tests etc. Despite doing countless practise data sets and understanding how to conduct these tests in the SPSS software, I cannot seem to wrap my head around the content. I am so desperate at this point and just need some advice on what you would do in my position. I have an exam tomorrow and can run these tests with ease, but reporting and interpreting the data seems impossible at this point.