9
votes
Is logistic regression suitable for high frequency values?
Ordinal regression might be a good choice here. Chapter 13 of Frank Harrell's Regression Modeling Strategies explains this approach. It makes no assumptions about the distribution of outcomes, working ...
- 86.7k
8
votes
Accepted
Is logistic regression suitable for high frequency values?
Logistic regression is for categorical outcomes; the "usual" form of logistic reg. is for dichotomous outcomes. So, you could divide your outcome into 3.9199 and everything else and then do ...
- 110k
6
votes
Accepted
Variable selection in logistic regression
The problem with your proposed approach is that every predictor in itself may not correlate with the outcome, but interactions between them might. Or you might have a curvilinear relationship between ...
- 117k
5
votes
Accepted
How to estimate this specific logistic regression model which is not linear in its parameters?
Using a local optimizer is not sufficient for global optimization.
Naïvely using a local optimizer (such as Levenberg–Marquardt) to find the parameters of a non-identified model is not a good idea ...
- 89.1k
4
votes
Can I perform logistic regression or any other type of regression on this dataset?
You are right that without data on those who didn't crash, you can't sensibly predict how likely a crash is. For example, maybe your data show there were twice as many crashes among young male drivers ...
- 3,442
3
votes
Can I perform logistic regression or any other type of regression on this dataset?
The answer is pretty simple here. If your outcome variable has both outcomes (the event happens or doesn't happen), then you can easily fit this data to a logistic regression with the event as a ...
- 8,645
3
votes
Variable selection in logistic regression
Adding to Stephen's excellent answer, there are lots of reasons not to do automatic variable selection. He covers some of them. But there are others:
A variable could be a mediator without being ...
- 110k
2
votes
SPSS and PSPP yield very different logistic reggression results with same dataset
You have specified different variables in the two models.
Diff_CC_quadro_normalizadois in the PSPP model but not the SPSS model and ...
- 34.7k
1
vote
Doing a logistic regression on a $2 \times 2$ table, why does R yield a different odds ratio than by hand-calculation?
The glm in R is an iterative procedure that uses Newton-Raphson to converge on the estimate and produce covariance estimates. These estimates are divergent, meaning ...
- 60.6k
1
vote
Doing a logistic regression on a $2 \times 2$ table, why does R yield a different odds ratio than by hand-calculation?
Count data for discrete independent variables and a discrete dependent variable, this seems like a good application of Fisher's exact test:
...
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