My question originally arises from reading this post Use of circular predictors in linear regression.
Right now, I'm trying construct linear regression using "Bike Sharing dataset" from https://archive.ics.uci.edu/ml/datasets/bike+sharing+dataset which basically tries to regression bike rental count on different variables
One of the predictor that I have question is on using "Hour" of when the rental occurred, which takes value from 0 to 23. The original post suggests transforming the circular data (time of day) using sine function to maintain the circular characteristic.
I was trying to apply to same methodology to my situation to transform the Hour variable. However,transforming 0~23 using sin(π hour/180) lets 00:00 and 12:00 to have 0. But I think people will certainly display different behavior when renting bike at midnight(00:00) and afternoon(12:00)
In this case, is it better to just use 23 dummy variables to account for hour or am I misunderstanding the concept of circular regression?