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So that is a difference of 5% for boys and 18% for girls between the two time points If you do that and fit a binomial (or equivalently logistic) regression model to the boy girl counts you will, if you choose the usual link function for such models, implicitly already be fitting a (covariate smoothed logged) ratio of boys to girls I was hoping someone could point me in the right direction as to what test i should use to compare this change (5% versus 18%) between two timepoints.
I want to see how muscle strength, affects bone mass and i want to take into account gender to see if it affects differently in girls and boys Would that be a good way to go about it? The idea is that the higher the muscle strength the higher the bone mass
What is an appropriate graph to illustrate the relationship between two ordinal variables
A few options i can think of Scatter plot with added random jitter to stop points hiding each other. I'm comparing the same proportion across samples of two different populations taken over time So, for example, the proportion of 3rd graders with blonde hair, comparing boys and girls, if every ye.
I have to analyze the data from a study in which two groups of subjects (total sample $n = 30$) were tested on 4 cognitive tasks, all related to executive functions. 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.
Statistically, if i choose to use a level with the highest predicted value as the reference, it will mean that all other levels are compared to that reference
In that case, the results interpretation will be based on how other levels are compared or significantly differ (or not) from the reference level
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