So, I've been building data collectors for the stock market. Basicallythis is just to give myself a resource that I can query as much as Iwant and not get dinged by over usage. (the learning experience isalso a plus)I'm curious though if the change in the frequency of data will skewthe results of a regression model (to keep things simple y = priceand x = time_stamp).The problem comes in when the amount of data is considered. I havethe collectors recording the change in the stocks once a minute. Fordata in the past that happened before I was collecting, I load the dailyopen and close prices and whatever other information companies arewilling to share for free.My question is, will the much more detailed data that I'm collectingeach minute  throw off or enhance the regression models that can becreated by just looking at the daily open and close prices?I would assume that it would enhance it but, I would like to know if that's not the case and that in order to get an accurate measurement I would need to just use the interval that applies universally.Here's an example chart to better explain what I mean.
Does variation in data density over time affect regression models?
machine learning;statistics;regression;logistic regression;linear regression