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  • What transformation should I use for a bimodal distribution?
    I have some bimodal data like the one generated down (R language), and I don't know how to transform it to have a normal distribution or homoscedasticity I'm running a linear discriminant analysis and I need homoscedasticity, but I'm not able to get it with this kind of distribution
  • r - Test for bimodal distribution - Cross Validated
    As mentioned in comments, the Wikipedia page on 'Bimodal distribution' lists eight tests for multimodality against unimodality and supplies references for seven of them There are at least some in R For example: The package diptest implements Hartigan's dip test
  • regression - How to analyse a continuous response having a bimodal . . .
    In general you can do a regression with finite mixture models or you could choose quantile regression and model upper lower quantiles apart from using ordinary least squares Since you're dealing with a bimodal distribution you should probably try to bootstrap your model to avoid issues with normality $\endgroup$ –
  • Applying Bayes: Estimating a bimodal distribution
    I'm trying to estimate a bunch of bimodal distributions, i e two means and two standard deviations, based on a variable number of inputs If no input is present, a constant value should be returned From my anecdotal knowledge of what Bayes is about, this should be exactly it, no? Adapt a prior based on incoming evidence
  • How to identify a bimodal distribution? - Cross Validated
    A simple way to program a bimodal distrubiton is with two seperate normal distributions centered differently This creates two peaks or what wiki calls modes You can actually use almost any two distributions, but one of the harder statistical opportunities is to find how the data set was formed after combining the two random data distributions
  • regression - Dealing with bimodal residuals - Cross Validated
    I have data from males females and also data from two countries However, I also have state and city-level data (which aren't evenly balanced) Could you explain to me what exactly it means to have bimodal residuals, and whether this needs to be transformed prior to running the regression? $\endgroup$ –
  • hypothesis testing - How to apply a statistical test on either a bi . . .
    With large samples the sample means will tend to be nearly normally distributed The Central Limit Theorem works for bimodal distributions To verify that averages of samples as large as ours tend to be normal, we can re-sample from x1 Ten thousand averages, re-sampled (with replacement) of size 3000, are nearly normally distributed as shown
  • Separating the populations in a bimodal distribution
    I have a data set which displays a bimodal distribution This was determined by plotting a histogram of the frequency vs number I now need to separate the two original populations and therefore find an intersection point of sorts From the plot it looks like the point might be approx -1 0 to -0 8
  • How do I normalize a bimodal distribution? - Cross Validated
    I'm working with the Iris dataset One of the variables, PetalWidth, has a clear bimodal distribution My understanding is that multivariate regression sssumes normality for each of the input variables Can I continue with the variable left alone? Is it necessary to normalize it? Here's the R code to access the data:
  • Splitting of bimodal distribution, use in regression models
    I have a bimodal length-frequency distribution for the females of a species with a one-year life span This pattern is not observed in the males I suspect that the bimodality is due to different hatching times and the associated environmental conditions





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