Questions tagged [binomial-distribution]
The binomial distribution gives the frequencies of "successes" in a fixed number of independent "trials". Use this tag for questions about data that might be binomially distributed or for questions about the theory of this distribution.
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Which test do I use to estimate the preference of species?
I have a question about how to analyse my dataset and would really appreciate your advice.
My data consist of observations of a set of target plant species collected during field surveys. The surveys ...
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Unitless Poisson distribution: an example
I asked here how the mean and variance of a Poisson distribution can be equal if they have different units of measurement (e.g., if we are modeling the length of a table, the mean is measured in $\...
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Stretching binomial distribution
Let's consider a class with 20 students for which almost always (say $90\%$ of the time) 1 or 2 students are absent.
I try to model the "absence" event as a success of a binomial ...
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Binomial mixed model with aggregated counts
I am analyzing a dataset with ~400k farrowing events in dairy cows. Each record includes farm ID, cow ID, farrowing date, date of successful artificial insemination, number of inseminations required, ...
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How to use the binomial test for obtaining an accuracy threshold for a true/false classifier?
Suppose that for each subject in an experiment, a classifier makes $n$ binary (correct/incorrect) predictions about that subject’s responses on a task. For a given subject, let $k$ be the number of ...
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Does it make sense to represent monetary value with a discrete distribution?
I am implementing a Bayesian non-parametric clustering model based on the paper "Bayesian clustering of multiple zero-inflated outcomes" by Franzolini et al. (2023) to my household spending ...
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How to justify that testing 20 sprinklers (out of ≤5000) is a statistically meaningful sample?
In fire-protection maintenance, we often need to test a sample of sprinklers that have been in service for about 25 years.
Each test is pass/fail, and the parameter of interest is the failure ...
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Fitting of data when datapoints come from binomial distribution
I have the following question. I have some data $p(x)$ where each datapoint comes from averaging 100 independent binary experiments. Although it is irrelevant for the question, I am measuring quantum ...
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Residual issues in binomial GLMM when including random effect
I'm working on a dataset of ~2900 fish, where the visually estimated sex was compared to the true sex. In about 10% of the cases (≈260 fish), the estimation was wrong (deviation = TRUE).
I'd like to ...
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Two ideas to include measurment errors in a Binomial Test with different results
I have a measurement with two possible outcomes, let's say 0 and 1. If the outcome of the measurement is 1, the true value is always 1. If the outcome of the measurement is 0, there is a chance of 1% ...
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Estimating and interpreting causal effect of a continuous exposure variable on binary outcome using double machine learning
I'm using double machine learning in the structural causal modeling (SCM) framework to evaluate the effect of diet on dispersal in birds. I'm adjusting for confounding variables using the backdoor ...
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Sample size calculation for a test on proportions
I'm not sure whether I'm assessing this problem correctly.
Suppose I want do demonstrate that an adverse event occurs in less than 20% of the participants (but I hope/assume that it actually NEVER ...
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glmmTMB warns of false convergence with betabinomial models but no with bin or negbin models
I've been looking for the best distribution to fit a glmm model to my data. The best seems to be a betabinomial model, but it gives a warning about false convergence, which is caused by large z values:...
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Why does glmer give a "nearly unidentifiable, rescale variables" message and glmmTMB does not, when fitting a binomial model?
I was fitting the following binary glmm with glmer:
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Predicting global outcomes with logistic model
I have a database of many employees, and i want to estimate how many are going to retire next year, based on many retired last year. So i thought about a logistic model like
glm(retire ~ age2025 + ...
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