Linear regression model with (categorical) predictor variables I used LM model with (categorical) predictor variables on my data in r like this (I have count variable as dependent/target variable):

# Fit linear model to total viewing:
fit &lt;- lm(
  total_viewing_minutes ~ panelist + sex + internet + education,
  data = cln
)


If I understood correctly in r the first category is always the reference one so has by definition a coefficient of 0.

So by looking at the model result, is 68.3 minutes (intercept)  average time viewing of 

Panelist0 + sex1 + internet1 + education1  


and by taking into account the term Panelist1
the 68.4 + 17.6 is avg time viewing of 

 Panelist1 + sex1 + internet1 + education1  


is that correct interpretation?
Your interpretation is correct. I would also add that if Panelist1 increases by 1 unit and the other predictors are held constant, then the average increase in the response variable is 17.557 minutes.