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Use standard type for greek letters, subscripts and superscripts that function as identifiers (i.e., are not variables, as in the subscript “girls” in the example that follows), and abbreviations that are not variables (e.g., log, glm, wls) Girls who will attend a party, and features of the party. Use bold type for symbols for vectors and matrices
Use italic type for all other statistical symbols. For example, let's say i want to predict the number of boys vs Considering the population of girls with tastes disorders, i do a binomial test with number of success k = 7, number of trials n = 8, and probability of success p = 0.5, to test my null hypothesis h0 = my cake tastes good for no more than 50% of the population of girls with taste disorders
In python i can run binomtest(7, 8, 0.5, alternative=greater) which gives the following result.
Probability of having 2 girls and probability of having at least one girl ask question asked 8 years, 3 months ago modified 8 years, 3 months ago Thanks to the answers i now understand why the ratio would be 1:1, which originally sounds counter intuitive to me One of the reason for my disbelief and confusion is that, i know villages in china have the opposite problems of too high of boys:girls ratio I can see that realistically, couples won't be able to continue to procreate indefinitely until they get the gender of child they want.
1st 2nd boy girl boy seen boy boy boy seen girl boy the net effect is that even if i don't know which one is definitely a boy, the other child can only be a girl or a boy and that is always and only a 1/2 probability (ignoring any biological weighting that girls may represent 51% of births or whatever the reality is). A couple decides to keep having children until they have the same number of boys and girls, and then stop Assume they never have twins, that the trials are independent with probability 1/2 of a boy, and that they are fertile enough to keep producing children indefinitely. A couple decides to keep having children until they have at least one boy and at least one girl, and then stop
Assume they never have twi.
I would like to run wilcoxon rank sum test to see if there are differences between boys and girls in each age group in regards to antibody levels Should i continue to use log10 or raw values? Suppose i want to build a model to predict some kind of ratio or percentage
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