# Search results

No trend!
2. ### Trend analysis

Can you plot the curve? delay vs time point.
3. ### Which statistical test to use

What is the model?
4. ### testing the association between points and any of the points belonging to another set of point: am I approaching it correctly?

Yes, I think you are right. If you have an area where there is uniform density for an event (like a point on the x,y coordinate) and you have a small part of that area, irregular polygon or not, then the probability for say k events in the polygon will be binomial distributed out of n events...
5. ### Standardizing Performance (sports-related)

You could search for "Elo rating" that has been used in chess but also in football. You can see how to convert an Elo rating difference to probability to win for teams with rating like 1500 or 2000. You will need to go back a few seasons to give every team an Elo rating. The model is essentially...

Yes.
7. ### making R computing large factorials

Remember that as "n" gets large so that n*p is large, the binomial is approximated by the normal distribution. The rule of thumb used to be n*p>5. So if p is "in the middle" (0.2<p<0.8) and n is larger than 10, it is well approximated by the normal. (Of course mu=n*p and variance =n*p*(1-p).)...
8. ### making R computing large factorials

(Sorry, I had not been reading so carefully.) You have n points, x of them falls into your polygon so that you have 0, 1, 2, 3 to x within polygon. x successes out of n. I should say that I have not read carefully and I have not understood what you are really aiming to. (Just trying to...
9. ### making R computing large factorials

I believe that you need dbinom(). ("d" for density.) pbinom gives you the cumulative distribution function. # dbinom(x, size, prob, log = FALSE) dbinom(2, size=4, prob= 0.5) #[1] 0.375 dbinom(0:4, size=4, prob= 0.5) # [1] 0.0625 0.2500 0.3750 0.2500 0.0625 x is the number "heads" and size...
10. ### Estimate sample size needed for valid study of medical test

Rune! What do you mean by this? Do you have a single variable e.g. a blood sample and for high/low values you would get high risk or low risk? Then I come to think of roc-curves, receiver operating characteristic. If you want to combine many variables to classify to high/low risk other...
11. ### Linear Probability Model

The thing with generalized linear models is that you can choose your self what kind of link function you want. Just like you can choose to include or not to include an x-variable. So the link can be the logit link function or the identity link function (like in LPM) or the probit link of the...
12. ### Help with ANOVA

That was absolutely not my intention. I apologize. But you are not the first one using the three-number. I just wonder where people get things from. So, OK there you go. You have a pilot plant estimate. And it is not to little data. (And I do not intend to be "condescending" now either.) I...
13. ### Help with ANOVA

Can you tell us where the number of three comes from? It it not a holy number is it? (Joseph, Maria and Jesus). I believe that they don't want to do just one. They want to replicate. But with two there could be one outlier. So they recommend Three, because then they can compare the two with the...
14. ### Need help to decide which statistical test to use

I would guess that this is not a balanced design (They have hardly randomized people to different treatments) so I guess that the full factorial can not be estimated. But I guess that the main effects can be estimated.
15. ### Need help to decide which statistical test to use

There seems to be four factors (knee replacement and Hip, race groups, insurance types, different age groups) where age could be a covariate = a regression variable.
16. ### Including polynomials if adj R2 does not change

You could do a Box-Tidwell plot (similar to the more well known Box-Cox transformation) and/or look at the Tukey and Mosteller’s Bulging Rule. You can look at a GAM - generalized additive model with the package "mgcv" in R. (Here is an introduction.) Or fractional polynomials or lowess plot.
17. ### How's the new look?

@bugman! Can you still not see the chat box?
18. ### track data across quartile groups (if that's even a thing)

Well, I believe that if you round a variable (like -36 to 36 to 1,2,34) you will lose a lot of information. First you collect information. Then you throw away a large part of it. (It would be nice if someone did a simulation study about this <--- suggestion!)
19. ### Little help on this given data (categorical)

Is that so much? I don't think so. It is good to work isn't it? (But someone will talk about family wise error rates, or about jelly beans.)
20. ### Scorecard logic for measuring performance of managers

It is nice to see Berley here again. And now you have exactly 300 posts. That is a special number....