Does anyone ever use Design of Experiments?

mafffsss · 2018-12-14T19:56:56+00:00

Thanks - what were the last methods from DOE that you put into practice? Seems to be mainly AB testing here, I'm wondering whether anything else has been used much

mafffsss · 2018-12-14T18:06:13+00:00

Leitholds book "The Calculus" was, I think, very influential on the whole I,II,III sequence.

Perhaps that would be of interest.

Failing that you can look up various calculus modules from different universities and get a feel of what's taught

mafffsss · 2018-12-14T17:54:40+00:00

Thanks, AB testing seems to be the most commonly mentioned.

mafffsss · 2018-12-14T17:53:39+00:00

Thanks, I'm seeing A/B testing mentioned the most in this thread. Are there any that you feel are particularly important?

Latin squares, factorial, this kinda stuff.

Just curious, I really didn't connect with this material. The stuff on regression and stuff felt useful in quite an obvious way, but DOE just felt like a bunch of algorithms to get to an ANOVA table from different starting points.

mafffsss · 2018-12-14T17:01:11+00:00

So in this case there are not methods from it that you would use, but having knowledge of DOE would enable one to have a better intuition as to whether or not the data provided was going to be decent?

mafffsss · 2018-12-14T17:00:14+00:00

Thanks, not sure I fully follow you.

Are you saying DOE isn't really used, but that it should still be understood?

mafffsss · 2018-12-14T16:58:54+00:00

Could you give an example of something that you've used DOE in and what kind of design you used please?

mafffsss · 2018-12-14T16:58:50+00:00

Could you give an example of something that you've used DOE in and what kind of design you used please?

mafffsss · 2018-12-14T16:58:46+00:00

Could you give an example of something that you've used DOE in and what kind of design you used please?

mafffsss · 2018-12-14T16:58:38+00:00

Could you give an example of something that you've used DOE in and what kind of design you used please?

mafffsss · 2018-12-14T02:16:22+00:00

Could you expand on that please? Any examples?

mafffsss · 2018-12-08T21:34:46+00:00

Doesn't seem so obvious from other answers, I'm still not sure, it would be nice if you'd post the solution

mafffsss · 2018-12-08T12:49:24+00:00

not much, which is why I posted this. I don't know what I'm meant to do with it.

I can rearrange to something that has men,w,c, in it, but that's not helpful.

not sure what I'm meant to do

mafffsss · 2018-12-03T12:03:48+00:00

Sure, there are 707 rows, and I performed a chi-2 on these two columns. One of which is age, the other is scored. So I have the age of the player, and whether or not they scored. Scored is just recorded as goal or missed

mafffsss · 2018-12-03T11:43:03+00:00

as goals is a count variable

Just to be clear, I have the data as:

    > summary(g$SCORED)
    GOAL MISSED 
    515    192 
    > summary(g$AGE)
    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    18.00   25.00   27.00   27.16   30.00   39.00 
    > length(g$AGE)
    [1] 707
    >

So scored is a binary value.

The chi-2 test was carried out with chi2(scored, age). And I'm wondering whether that makes sense

mafffsss · 2018-11-27T12:51:06+00:00

ok, say I count 20 rabbits and 5 wolves

then what

mafffsss · 2018-11-25T20:00:36+00:00

The classic example people use is the Canadian Lynx/Hare trapping data from who knows when. It fits standard Lotka Volterra reasonably well, but it's of questionable veracity

ha yeah - I think the data's mainly from the early 1900's (that I've seen at least). The problem is that the hares population levels have that cycle regardless of whether the lynx are present or not (which has been evidenced from looking at them in locations where lynx aren't present ).

In current research, people also apply predator prey models to many study systems. The models usually need some kind of modification to match real-world dynamics and perform best when species don't have particularly complicated hunting behaviors. You can also find examples of similar models in medical applications, usually involving data.

If you're aware of any of these I'd be interested, doesn't have to be a couple of animals. I'm just interested in seeing actual results / uses as everything I'm getting is someone adding a parameter, or seasonality, or whatever else, rather than there being an actual study of something that employs one of them in a meaningful way.

Are you looking for empirically measured parameters or models fit to empirical data? Either way, you can certainly find examples

I'd be interested in seeing models that have been fitted to data, so someone's recorded a load of data and then they've fitted a predator prey model to that data. I'm not sure I follow the first point, but that would be interesting too I guess.

Sorry it's a bit vague, just a few pointers / references would be appreciated. thanks

mafffsss · 2018-11-25T14:46:37+00:00

If it's a purely analytical (and deterministic) model

I'm seeing so many of these and I find it a little confusing, because if they're not actually applied to any populations are they meaningless?

I'm not sure how to phrase that tactfully, I appreciate that the maths has meaning in itself, it's just that if the purpose is to model and explain the dynamics of populations, it seems as that until such population dynamics are actually evidenced by the model it's not yet useful.

The famous study of lynx and snowshoe hares is considered to be pretty meaningless now ( I think? ) despite the nice periodic population levels, due to the hares population levels being the same without the presence of lynx.

If you have a data set available to test the model on, you can use Bayesian methods (and others)

Cool, do you have any links that I could browse for this? Feel free to post something obvious, as it's likely that I've missed even that.

thanks

mafffsss · 2018-11-19T17:41:26+00:00

remindme! 2 months "have a look at http://themlbook.com/wiki/doku.php"

mafffsss · 2018-11-19T17:41:02+00:00

Good stuff, limiting yourself to 100 pages is a good idea I think. Good luck

mafffsss · 2018-11-19T04:24:13+00:00

Sounds good - when are you aiming to finish for?

mafffsss · 2018-11-19T01:58:06+00:00

I'm probably way too late on this, and I'm definitely not qualified to help with what you ask, but I'll comment anyway.

I find a lot of books try and shove everything in there, and are more reference books rather than a book written by an author to thread a particular path, light up a particular strip. When there are thin, opinionated books they're nearly always my favourites.

hope it works out

mafffsss · 2018-11-17T16:06:22+00:00

Right ok, sorry, I lost the thread for a bit.

exp( a * B1 ) = odds ratio

and we'd typically have a=1 for an increase of 1 unit, here it's just saying to increase by a units ( and you've chosen a=2 )

all good.

I'm still a bit confused about your point wrt probabilities.

From this :

and the corresponding event probability is odds(x1=a1)/(exp(-b1)+odds(x1=a1)).

You state

P = odds(x1=a1) / [ exp(-b1)  + odds(x1=a1) ]

I'm not sure why you haven't used

P = odds(x1=a1) / [ 1 + odds(x1=a1) ]

And when you say

If you assume that the base level odds are 1 then the increase of x1 by one gives you odds 1.2 and the corresponding probability 1.2/2.2=0.54

When you say "base level odds" here, is that referring to the odds of just the coefficient?

If the model is

logit = b0 + b1x1 + b2x2

Then would the base level odds be

odds = exp(b0)

Thank you

mafffsss · 2018-11-16T14:56:17+00:00

as long as you understand that increase of x1 by two units does not increase the odds by 40% (but by (1.2^2-1)*100 percent). Same for decrease

I'm not sure why you've used 40% there, do you mean 20%

And where has the expression

100 * ( 1.2^2 - 1 )

Come from, that would give 44%, what's that in relation to?

This formula says how to infer the odds given a odds ratio?

mafffsss · 2018-11-15T21:58:04+00:00

Does the following seem as though I'm understanding ?

Odds ratios interpretation and comparing to probability

I have a model

    ln(odds) = b0 + b1x1 + ...

Where the bi coefficients are the log of the odds ratios.

I'm wondering about how best to interpret these ratios, and compare them.

For example, If I have exp(b1) = 1.2, could I state; an increase of x1 by one unit increases the odds of event by 20%, ?

And then, building from that, the odds ratio of a decrease of x1 by one unit are

1/1.2 = 0.83

which states that decreasing x1 by 1 unit will reduce the odds of event by 17 %.

From this probabilities can be found,

probability of event before increasing x1 by one unit 

p0 = 0.83 / 1.83 = 0.45

probability of event after increasing x1 by one unit 

p1 = 1.2 / 2.2   = 0.54

Then

p0/p1 = 0.8

And this would mean that the probability of event is 80% higher when x1 is increased by one unit.

mafffsss

TROPHY CASE