I am trying to determine the correlation between two sets of data points which span the same time period (20 minutes) but have different resolutions. The first set was recorded at 1-minute intervals, while the second was recorded at 2-second intervals. Both sets of data contain 10 minutes before an event, and 10 minutes after the event. The recordings are of completely different effects resulting from the event, so the 1-minute and 2-second data are not related.
When I plot the two sets against their common time scale, I get a visual match in the pattern of the graphs which indicates that there is a definite correlation between the two effects - the same reaction profiles are present in the data. The events were repeated 19 times with multiple recording devices for each effect, 94% of which indicate this visual correlation.
However I need to do the proper statistics to back up my claim that there is a strong correlation, but it isn't my field of expertise. I was advised to use Pearson, (an online tool into which you paste the two data sets and it does all the work) but this requires two data sets of equal length.
I have looked into downsampling the 2-second data to 1-minute data through aggregation but this will take extremely long. I've also considered downsampling by only keeping every 30th data point (decimating the other 29 points) and re-terming it a 1-minute measurement. Decimation will also take long, but less time than aggregation.
Q1. Is there a different statistical method that I am not aware of that will let me compare the two sets as they are?
Q2. If I downsample, which method is better? Aggregation or Decimation?
I am currently working in LibreOffice Calc, which is the Linux version of Excel.