Відео

mf explains this in 6minutes while my computational statistics professor cant explain this in 2 lectures

Hi, I have recently stumbled upon your great channel and have been binging your videos. To preface this, I am not anyone special. I am currently doing my Masters in Psych/Neuroscience and stats and stats communication is my side job. With that being said, I vehemently disagree with the perspective that everything should be taught as a linear model. Much of what we do in Psychology is linear modelling, but I found that it is simpler to teach things like the t-Test, the F-Test or the ANOVA independetly and then bring them all together into the little facets of the GLM. I usually frustrates me to no avail when people come to me for help and their professors have decided to just teach everything under the guise of a linear model. For them it just seems completly overwhelming. In principle, I get your argument. It would be better if people had a full understanding of the GLM or even the GLIM but purely didactically speaking this is just too much. Let me make an example. I can teach someone the basic of ANOVA in around 15 minutes. They do not need any special knowledge for my explanation. If they understand, I can teach them about the F-Test, which gives me a vehicle to repeat some things about the chi(squared) and the F distribution. If I want to later integrate it into the GLM, they will have heard the logic of ANOVA thrice (for one factor, for multiple factors and for ANCOVA) and the transition into the GLM is easier. If I teach the ANOVA purely through the GLM lense I need to do so much more preperation. My pupils would have to have a somewhat firm grasp about dummy coding, the basics of regression anslysis and the different kinds of linear model parameters. Then on top of this I have to explain the logic of ANOVA, but not the native logic of ANOVA. Instead I have to explain ANOVA in GLM which on it's own is (imo) a bit more difficult to explain than pure ANOVA. I could, of course, just explain ANOVA and then explain an ANOVA in GLM, but this is just the same thing that I initially outlines, just crammed into one session with more effort and less time spent on what ANOVA really does. Maybe as an aside, I think ANOVA is a brilliant procedure and gives a good introduction into quantifying questions that are not straight forward. However, in my experience, if ANOVA is being taught from the GLM perspective students get the impression that ANOVA is part of the GLM and falls to the same assumptions, which is just not true. ANOVA os exceedingly robust to almost all violations, which cannot be said of the GLM. I

Great video, subscribed!

Thank you very much for such informative videos. I spent several years in class and didn't understand all these concepts, but watching this video has made things easier for my comprehension. I have a few questions I would like to ask: When performing a statistical test, we use a parametric test if the data or variable in question is normally distributed, and a non-parametric alternative if the data or variable is not normally distributed. My question is: when does the central limit theorem come into play here? Also, a colleague of mine told me to always use parametric tests even if the data is not normally distributed. His explanation was that parametric tests are more powerful than non-parametric tests. So, should I straightforwardly use the non-parametric alternative when I observe that my data is not normally distributed, or should I take the CLT into consideration and use the parametric test?

What advances have been made since Erich Fromm??

Hello Mr. Fife 😀 Does, for example, running general linear model as t-test versus mann-whitney u test and comparing theirs results count as sensitivity analysis? Or only transformations, bootstraping and trimming would count as sensitivity analysis?

You're amazing and you explain very clearly. Please keep making videos.😊

This guy is so f*cking awesome! Very informative as well as great energy!

Diagnostic criteria require an optimal cutoff. Those cutoffs are not arbitrary or determined by one dataset (the focus of researchers). Clinicians often conceptualize the data continuously (e.g., pre-diabetic, higher risk for cardiovascular disease, pre-clinical risk for stress-mediated chronic disease development), but patients want to know if they have a condition or not (category). Clinical scientists don't categorize everything because we only know how to use ANOVAs , but what a condescending standpoint. Eliminating categorical cutoffs eliminates diagnoses. I'm good with that, but really, as a patient, are you?

If all you know is ANOVA, what would you do instead?

as an ecologist in progrees I can say, in ecology EVERYONE is using GLM all the time even when they could be using other simpler methods so here I am trying to actually understand them ahjhahaha

Or you could just drop it all and go fishing!

Rowan University! I was in the first year of freshman to go all 4 years majoring in bioinformatics!! Edit: negative binomial mentioned 15:15

Please also discuss the problems when categorical data are analysed as continuous data. Thank you for your videos.❤

Myanmese (or Burman, depending on who you ask).

1:04..or maybe we measure people who share the same gender. Why can’t I see a clear reason that “gender” is not a common candidate for nesting variable (ie people usually just control for it), but classroom always is?

This is a Nobel Price in Languages right here.

i am not in anyway involved with doing statistics. i just love hearing a man be real about things.

Thank you for this video!! Can you go into the differences between linear regression vs. the “decision tree” of tests in more detail? Is it a matter of pros and cons of methods or just old vs new techniques? One obvious thing that comes to my mind is one can’t account for repeated sampling with a t-test or ANOVA, right?

10:00 consider the bumblebee

This was great

Why does he shout the whole time? 😮

Totally agree in theory, but docs love ORs and the Titanic turns slowly. How can I better communicate interpretability of betas if I keep the outcome continuous? “For each year older the kiddo is, we see delay to initial imaging increase by 1.6 days.” The blank stares haunt my dreams.

Why don't you use poly(var_name, n) instead, for orthogonal polynomials?

I'm not a Statistician or a Biostatistician, and I'm not even good at Math, but your explanation was so crystal clear even I can understand it. Sweet! And I've had Senior Level Management folk - VPs, SVPs - from major Big Pharma companies ask to keep hacking away at data that plain as daylight like the Continuous Variable Distribution you showed in this video, and I keep asking myself: "am I so stupid? Am I missing something obvious?" After all, the data is being summarized and showing whatever its showing, but somehow the big folks want it to show something else. And I'm always like "what else do you want it to show? It is what it is!" Of course, I swallow my pride and hide my impatience because maybe, just maybe, I'm really stupid. But after months of slicing and dicing data into invisible chunks, it always comes back to where I started. Scary! Thanks again for making advanced topics palatable for myself and others like me. It gives us hope.

“But previous literature did” hm ok yeah let’s shy away from that excuse

As always, the vlog is excellent. It brings to mind a quote from Frank Harrell on categorisation: ‘Employ it when the intention is to mislead the reader" ;-)

I've encountered that many tend to create categorical variables to use as predictors in logistic regression models, so that the value on the logit scale can be easily interpreted as an odds ratio. But what they don't realize is that the values can be recoded to keep the continuous distribution of the variable, but transformed it so that the value of 0 can indicate the value of say the bottom 25th percentile and the value of 1 can equal the value at the upper 25th percentile. Now in theory you are still interpreting the values as if they were a binary variable, but at least you do not lose statistical power by capping the natural variability of an informative covariate

I used the book by Imbens and Rubin (2016) to measure treatment effects in my MSc thesis. It's a sub-classification, based on the Propensity score (PS), a continuous variable. The sample is split on the median such that the average PS of the treated is equal to the average PS of the controls in each stratum. The results are somewhat sensitive on how the sample is stratified, but the stratification is done using a very specific algorithm. I would be interested to hear your thoughts on that book. Note that the PS in my sample was analytically derived, not estimated.

I categorized my gene expression into low medium and high because we have no idea how to analyze something without resorting to pvalue comparisons (is there a difference of the mean, plug in whatever model you have depending on normality).

Medical doctors are indoctrinated to think of the world in terms of decision points and cutoffs. This is why the doctor demands discretization of a continuous response. He wants to have a decision point, where test results above that point indicate treatment. Continuous distributions are much more challenging to deal with. If you have a guy whose test score is in the middle of the pack, what do you do, give him half the treatment? What if the treatment is a surgical procedure? This is why doctors demand cutoffs. They are not morons, they just have a different set of priorities and constraints.

It is interesting considering how to model a random variable X that quantifies an extent but also have a variable Y is an indicator variable that gives us whether there was any extent to X at all. If we have labels then we can model X * Y. Without labels I think a mixture could be reasonable. If you have labels for some but not all of the data then that sounds like a missing data problem. There you should consider whether the missingness is MCAR, MAR, or MNAR. If the former two, then model-driven imputation is may be possible. All-the-better if you impute a probability distribution over the missing values rather than filling in just one value.

Yeah, an "optimal cutoff" requires a well-defined optimization problem. It requires an objective function to be either minimized or maximized. Vaguely pointing at a continuous empirical distribution does not constitute such clarity.

I could see someone trying to partition the data if they saw a bimodal distribution and no apparent labels to explain that bimodality, but I would still prefer a mixture model. A mixture model allows the assignment of probabilities to the apparent subpopulations.

I think what actually stops me from ever using median splits is that the decisions I help people make with statistics don't involve the median. It just doesn't have any relevance on the problems I work on.

I am really interested in the continuous along with categorical findings when we have to make a decision, as you said. I would really appreciate it if I could find a paper that demonstrate this approach, or utilize it.

I always liked Fuzzy Logic as a framework for interpreting linguistic categorical concepts continuously and vice versa. 'Fuzzification' and 'Defuzzification' are permanent fixtures in my toolbox for when I need to explain the ideas that you talk about here.

can you leave the link in your description I can not find your website.

First of all you’re kind of crazy, in a good way 😂, secondarily, I will use “languagatized”with my students! Boo to discretizing continuous variables!

The inflection idea to make language continuous is something we already do. Daft and Lengel talk about media richness. Papers are less rich than talking because tone and inflection don’t come through in a paper. That’s a huge simplification of their premise, but that’s the gist.

I just don’t get the median split. Why take a continuous variable with all its information and then strip it away and turn it into a discrete one with two values. It’s a loss of data. EDIT: I commented the moment you said median split. Nice to see your elaboration support my dislike of the practice. 😁

I found a new hidden gem channel! Nice video.

PLEASE do videos on analysis in SAS! eg for logitudinal data

you are brilliant!

I love this guy lol

Appreciate it, thanks!

Oh my, this video would save me a lot of work if I checked earlier! Thanks!

What about ridge regression and LASSO for feature reduction?

so, if the loess is tend to be linear we chose linear model else non-linear. is that wright?

Super useful, especially in ecology, because I rarely get normal data from my field experiments. And when I do, is usually because something went wrong 😆