Maybe an option is to define my linear model as
theta = (Intercept + b1 * P1(mx_ex[‘dias_normalized’]) + b2 * P2(mx_ex[‘log_cantidad’])) ?
being P1, P2 probability distributions with a shape = 83 (total number of observed values)
Maybe an option is to define my linear model as
theta = (Intercept + b1 * P1(mx_ex[‘dias_normalized’]) + b2 * P2(mx_ex[‘log_cantidad’])) ?
being P1, P2 probability distributions with a shape = 83 (total number of observed values)