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.
I wonder if there is any statistical test to "test" the significance of a bimodal distribution. I mean, How much my data meets the bimodal distribution or not? If so, is there any test in the R pro...
I am attempting to model bimodal continuous coral survival data that includes values of 0 and 1 (0-100% survival). I have attempted to use linear mixed effects models (lmer and glmmTMB) with a few
R how best to model continuous bimodal survival data using lmer and ...
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...
Here are two simulated bimodal samples to use for illustration. ... It is probably OK to to a two-sample Welch t test to see if we can a find significant differences in sample means between two such large bimodal samples. The difference is sample means for my simulated data is highly significant with a P-value far below 5%.
A mixture of two normal distributions with equal standard deviations is bimodal only if their means differ by at least twice the common standard deviation." I am looking for a derivation or intuitive explanation as to why this is true.