How Can Scattered Data Impact Tool Performance Analysis?

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Scattered data from the tillage machine tests complicates the analysis of force differences between two soil engaging tools, A and B. Initial observations suggest that tool A may exert higher force than tool B, but the variability in the data makes it difficult to draw confident conclusions. Suggestions include using time-based filtering to eliminate noise and conducting multiple runs at each speed to average out variability. The discussion highlights the importance of considering additional factors, such as tool depth and soil uniformity, which may impact the results. A 3D plot incorporating both speed and depth is recommended to better visualize the relationship between these variables and force.
  • #31
I'm sorry, without thinking about it, I was using the time-series terminology "cross correlation". That was probably misguiding. It's just the correlation between the two variables that I was thinking of.
 
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  • #32
I have also computed the Pearson's correlation coefficient of the average values of draft with respect 'speed and depth, respectively. The correlation is higher with speed than with depth.

Instead, by computing the Pearson's correlation coefficient of the time based draft with respect 'time based speed and depth, respectively, sometimes, the reselts are pretty variable. Sometimes, the correlation is strong, other times the correlation is weak. Sometime, the correlation is stronger with speed, other times is stronger with depth.
 

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