# How to understand output from linear regression ( independent variable with three levels)?

I have run a linear regression model, where one of my independent variables has three levels. In my dataframe these are under "sex" and are femalepre, femalepost and male. Pre and post for pre-puberty and post-puberty respectively.

In the summary(model)\$coef

I can see that the effect sizes, std.errors and p values are listed as follows;

    Femalepre    0.245  0.012  0.45
Male         -1.24  0.034  0.63


From my understanding, linear regression (lm in r) takes whatever comes first in the alphabet (femalepost in this case), these effect sizes show that when going from female post-puberty to female pre-puberty, there is an increase in the relative abundance of the metabolite (dependent variable) by 0.245. Whilst, going from female post puberty to male, there is a decrease by 1.24 times.

If female post puberty is regarded as the baseline, how do I explore the change from female pre-puberty to male?

Apologies for my lack of understanding, I'm doing this project as part of my BSc research.

• Search for "pairwise comparisons" and "multiple comparisons" and "adjustment of p-values". Jun 23 at 7:08