I am new to regression and am working through some exercises.
I am trying to predict the dependent variable based off a time series of independent variables, which I believe are correlated.
date dependent independent january 0.655 15.0470029 february 0.911 15.27434021 march 1.15 15.46698656 april 1.38 15.62245827 may 1.62 15.86285276 june 1.95 16.12415496 july 2.09 16.29778048 august 2.35 16.47115632 sept 2.65 16.66489092 oct predict? 16.94209902
Basic regression in Excel gives me an R^2 of 0.996487955
Linear formula from excel:
16.94209902*1.208521405+-17.54118113 = 2.933708174
I know the ACTUAL for oct is 2.88
How do I ensure that my independent variables actually have predictive signal? How can I apply a naive model to see if I am just capturing the linearity in the dependent data?
I am new to all of this, so please let me know if you need further clarity.