Once you have an interpolated age distribution you may compute moments (and some other properties) based on the standard definitions. As an illustration, the necessarily mean is approximated by numerically integrating the age with respect on the distribution.
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For dilemma two, When you've got a sample that is certainly limited to The 2 genders (e.g. all respondents are male or woman) you may shouldn't be in a position to acquire a coefficient for male and female from the equation.
$begingroup$ You must be a little bit careful with design objects in R. Such as, whilst the fitted values as well as the predictions with the training info really should be a similar within the glm() design scenario, they're not a similar when you use the correct extractor functions:
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in concrete conditions, Exactly what does the necessarily mean of a sample really let you know with regards to the inhabitants? Sizzling Community Inquiries
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