I am unable to interpret this graph. My dependent variable is total number of movie tickets that will be sold for a show. The independent variables are the number of days left before the show, seasonality dummy variables (day of week, month of year, holiday), price, tickets sold till date, movie rating, movie type (thriller, comedy, etc., as dummies). Also, please note that movie hall's capacity is fixed. That is, it can host maximum of x number of people only. I am creating a linear regression solution and it's not fitting my test data. So I thought of starting with regression diagnostics. The data are from a single movie hall for which I want to predict demand.
The is a multivariate dataset. For every date, there are 90 duplicate rows, representing days before the show. So, for 1 Jan 2016 there are 90 records. There is a 'lead_time' variable which gives me number of days before the show. So for 1 Jan 2016, if lead_time has value 5, it means it will have tickets sold until 5 days before the show date. In the dependent variable, total tickets sold, I will have the same value 90 times.
Also, as a side remark, is there any book that explains how to interpret residual plot and improve model afterwards?