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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 <- 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?

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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.

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