maybe principal component analysis (PCA)?
I want to test a series of 36 time periods and 10 data points for each one of those periods. The goal is to see which data points have the biggest effect on change from one period to the next.
I thought Chi-Square was the way to go, but I do not have any expected frequencies here. Regression analysis is more of a prediction/forecasting model? What statistical model should I use here?
Okay, I have:
m sets of data that have n data points
I want to find what data points have the highest effect on each set of data. Basically, I want to find which data points have the most correlation with the end result. I know I have done this before, but I forget what type of analysis I used.
I may have found the solution to my problem. Even though I have more than two data points, I can isolate each data point with the end result (x's and y's) and do n tests of Pearson product-moment correlation coefficient - Wikipedia, the free encyclopedia