Correct variable/data use for day of week predictors Currently trying to model count data using ticket counts for each day of week as the dependent variable (y) and the corresponding day of the week integrated using OHE for 78 days. Assuming Poisson distribution.
I was just wondering if the independent variables are correctly formatted to be able to predict a relationship between day of the week and the ticket count. My goal is to forecast ticket counts for any particular day of week (negBinom regression)
The first image shows the dependent variable y (ticket count) and the second shows what is being modeled as the predictor variables in the regression (days of week). I just want to know if this is the correct thought process.
Is it appropriate to use individual categorical features as predictor variables? Even if the days are the same order in the observations? If not, what is a more appropriate way to incorporate days of the week.



Yes, days of the week can be modelled either as continuous 'seasonal' features in time series models (sometimes with an additional binary variable indicating holidays), or a categorical variable. Which approach is better really depends on the specific problem.
