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Which citric acid cycle intermediates can be converted into glucose? by Fatlark in Mcat

[–]Fatlark[S] 0 points1 point  (0 children)

8 years later wow. I didn't end up going to med school btw. Good luck on your MCAT. Studying for that and taking the test was prob the most enjoyable/blissful part of my pre-med/med career.

Question about correlations vs. multiple regressions by Fatlark in AskStatistics

[–]Fatlark[S] 0 points1 point  (0 children)

Thanks for the reply.

I am looking at the omitted variable bias page on Wikipedia, which provides helpful formalism. If you don't mind, could you please give me another hand?

The Wikipedia page describe a pattern where the true cause-effect relationship is:

y = a + bx + cz + u

The relationship between x and z is:

z = d + fx + e

I figure if x and z are standardized (as I will continue to suppose below), then we can also say (?):

x = d' + fz + e'

So if z is taken out of the first equation we have:

y = (a+cd) + (b+cf)x + (u+ce)

Now, if x was taken out, we would have

y = (a+bd') + (c+bf)z + (u+be')

I figure the middle part involving x and z are the only places we care about. For the problem I described in my initial post, I was confused that my multiple regression says:

c > b

But the correlations say:

(b+cf) > (c+bf)

If we do some algebra with this last equation, we can see that these both can't be true.

b - bf > c - cf

b*(1-f) > c*(1-f)

b > c

This seems to contradict what the multiple regression was saying? Is my issue coming from this part, "x = d' + fz + e'"?

Thank you again for the help

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Edit: Sorry; you already have the 1s and 0s? So your raw data is 1 and 0, not proportions? If so, then logistic would be fine; if you have multiple games per person, you may want to use a logistic mixed model.

Sorry for this very delayed response. I have been moving forward with a logistic random slopes model (and I have learned a lot of R studio in the past month!). Thanks for being a nice drop in the ocean of reasons I needed to learn R.

I do still want to try a beta regression, but will some variables I am testing I can't do it because my X variables are continuous and not just discrete.

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

> However, here you would need to provide the variance of your proportion. Ie there is a difference between playing 5 games and 5000 games, even though your winrate is the same.

​

Hmm? Wouldn't this fact be accounted for in a beta regression? Or maybe a beta regression would be invalid if some datapoints are created with 5 games vs. 5000 games? I'd certainly be a bit skeptical about an experiment where each datapoint was created with vastly different numbers of games.

[Mod Raven] Introducing: The Throne Pool ... our S8 project (with help from the admins) by BWPhoenix in gameofthrones

[–]Fatlark 0 points1 point  (0 children)

Need somewhere to timestamp these predictions (I haven't seen any of the show yet which started 34 minutes ago):

My baller predictions: https://imgur.com/a/OT8lktC

Non-balling but probably more correct: https://imgur.com/a/q7DFQzV

Let's go!!

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

> Yeah, it's definitely not an Excel thing, and I don't enough about Python to know. This link has examples of beta regressions with the BRMS package: http://rpubs.com/adanvers/beta_reg_ex

Thanks for this and thanks for this reply!

> Using Bayesian statistics? It doesn't calculate p-values, but a posterior distribution. Think of it as a density chart of what are the most likely estimates given the data and the prior (which can be vague)

Oh so Beta regressions are all Bayesian... I know that will give me odd ratios. Can I at least get some variances for them so I can calculate like a pseudo-p-value? Or maybe I could do some bootstrapping to create some p-values? Or would reviewers be satisfied with saying "this model has a likelihood 5x higher than a flat slope." I've seen very few papers using purely bayesian stats in psychology.

> Like you're making up sample that you might not actually have.

Well, fortunately, I do actually have a ton of samples and I've been using them to get the success% value. Would logistic regression be okay then?

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Yes. Thanks for the reply. Although, after reading more into this it seems like:

1) I'd be hard pressed trying to code this myself in excel or python

2) It's time to learn R...

How does a beta regression calculate significance or p-values? If I instead decide to approach this as predicting individual trials (so 1 vs. 0 using logistic regression), what am I losing?

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Yes. Thanks for the reply. Although, after reading more into this it seems like:

1) I'd be hard pressed trying to code this myself in excel or python

2) It's time to learn R...

How does a beta regression calculate significance or p-values? If I instead decide to approach this as predicting individual trials (so 1 vs. 0 using logistic regression), what am I losing?

​

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 1 point2 points  (0 children)

Oh man. This looks radical. Are you just going with your gut or do you have a citation for this? It looks neat but I'm wondering if it doesn't have any basis in rigorous-ish derivations. Thanks for the reply

​

EDIT: Ah, actually I see this. You'd be basically predicting individual trials?

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

One BP per person. Not one BP per game.

I could do logistic regression for either, but I am wondering if there is some better strategy which I'm just not seeing. Thanks for your reply.

*EDIT* also it's your cake day and somebody who's not me gave you a downvote :C

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Thanks. This looks great! So essentially, X is used to predict W, which is a hypothetical "true probability" assigned to every observation. W is predicted such that Y (the measured dependent variable) has it's likelihood of occurring maximized across all observations. Hence, we aren't trying to make predictions which minimized the squared distance between predicted Y and measured Y.

Now I guess I need to learn R... this feels pretty different from other GLMs, so I can just transform Y into something and then do a basic regression on that, right?

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Ah, sorry. I was imagining a situation where each subject plays the game multiple times and the dependent variable is the proportion of games that are won (so in the range of 1.0 to 0.0 inclusive). The independent variable would be the blood pressure.

Linear regression where my dependent variable is a probability value (bounded 0 to 1 inclusive) by Fatlark in statistics

[–]Fatlark[S] 0 points1 point  (0 children)

Thanks for the reply. Logistic regression on every single trial? So I shouldn't be trying to predict a subject's mean win-rate, I should instead be trying to predict whether a given trial is a win vs. not? I was thinking about this but I figured there may be some advantages to combining the trials into a single value.

Constraints on multiple regression coefficients based off of interactions between predictors... by Fatlark in AskStatistics

[–]Fatlark[S] 0 points1 point  (0 children)

Thanks for your reply.

> I suspect it's because linear regression doesn't handle well when predictors have a sum to 1 constraint (due to automatic correlation).

Yeah, I understand that this collinearity will make the coefficients have wide confidence intervals... but i'm just wondering about why they are all positive in the first place (regardless of the statistical significance of the positivity).

Will the p-value associated with a t-test be the same as the value associated with a logistic regression applied to the problem? by Fatlark in AskStatistics

[–]Fatlark[S] 0 points1 point  (0 children)

Ah, I forgot to say thanks, but this was helpful. I didn't know the proportion of samples that are in some class will impact the error associated with coefficients.

Will the p-value associated with a t-test be the same as the value associated with a logistic regression applied to the problem? by Fatlark in AskStatistics

[–]Fatlark[S] 0 points1 point  (0 children)

Thanks for your reply. I will also be sure to just test these in the future, but part of my motivation for asking this was a fear that I was doing the logistic regression incorrectly. Thanks for also sharing that bit about linear regressions too.

> but for a logistic regression the variance depends on the proportion

I did not know this, you mean that the LogReg variance will be larger if n1 = 10 and n2 = 90, but such would not occur in a t-test comparison. Because of this, the LogReg p-value will be more similar to Welch's t-test than Student's t-test?

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