How to interpret R contrasts when given continuous and categorical explanatory variables? Let's say I have run a linear regression model that models the sugar content in a Jelly Bean as a function of its colour and weight:
lm(sugar ~ color + weight)


The summary of the above model outputs the following:
Coefficients:

           Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.07934    0.28625   0.277   0.7823
coloured    0.41976    0.09952   2.208   0.0296 *
weight      2.54078    0.35643   7.128 1.81e-10 ***

What is the mean sugar content of a coloured Jelly Bean?
Would it be 0.07934 + 0.41976 + 2.54078? Or is it not possible to calculate without knowing the mean weight of a coloured Jelly Bean?
I would be very grateful for any help with this. Please note this is not a homework question.

The complete model is:
$sugar content = 0.07934 + 0.41976*(coloured) + 2.54078*(Weight)$
There is a strong correlation as weight goes up there is more sugar content.   At zero weight there is nearly zero sugar (apparently there was noise in the  readings from original dataset).  I am not sure how what the coloured (yes/no??) variable represents.
To answer your question, the model knows nothing about the weight of a Jelly bean only the relationship between weight and sugar content.  So yes you will need to know the mean weight of a Jelly bean to know the expected sugar content.
