I have a dataset of monthly data. One column is my target variable and all the other are my feature. I have computed correlation between my target and all the other feature and then I made linear regression and got my betas and R2.
Now my question is more theoretical. if I oversample to daily data (I used a linear interpolation) and compute again correlation, betas and R2, they have changed a lot. Can anybody explain me why that happens? is correlation affected by oversampling? I might expect my betas to change because I have much more data after oversampling and so the R2, but not really the correlation if the size of my monthly data was already quite large. Thanks