I am not absolutely sure but R would be the product moment correlation. The product moment correlation is a measure of the correlation of two variables X and Y measured.
in simpler terms, its a way of knowing how "accurate" your data points are. If r^2 is closer to 1, that means your points are falling on that linear line (Cal Curve). 1=100% of your points are falling on your calibration curve line. So an r^2 value of say 0.9998 is very accurate. The closer your r^2 value is to 0, the less accurate your data points are. So and r^2 value of 0.9281 is less accurate to you cal curve. I've attached a video that helps explain the theory a little better and its easy to understand (probably better than I did) ruclips.net/video/Guo7ntYAyIY/видео.html
My god, this video is better than a whole virtual semester on analytical chemistry, please, please, keep this up
I laughed and learned. Best teacher I ever met! Thank you for making science easy but that you don't show it as being perfect.. because it is not :)
So funny and great! Thanks for the awesome work!!
Our pleasure!
I don’t know what you have done but I enjoyed it☺️🙃😂♥️
This is excellent ty for saving my bacon
need some quantitative transfer technique in there to make sure that anything in the weigh boat makes it into the stock flask.
thank you for this
You're very welcome!
You’re so funny 🤣🤣🤣
Glad you enjoyed it!
I love it
Tnx this video was very useful
What was the solute?
love
Can you please do a video about starting up and running HPLC samples? I'm scared of this instrument.
That would be a great video. What all would you want to see in a video like that?
@@DavidYazdi1 How do I stabilize the instrument? One time some of my peaks were on the next sample's chromatogram, WTH? Best practices and hacks?
What is R square???
I am not absolutely sure but R would be the product moment correlation. The product moment correlation is a measure of the correlation of two variables X and Y measured.
in simpler terms, its a way of knowing how "accurate" your data points are. If r^2 is closer to 1, that means your points are falling on that linear line (Cal Curve). 1=100% of your points are falling on your calibration curve line. So an r^2 value of say 0.9998 is very accurate. The closer your r^2 value is to 0, the less accurate your data points are. So and r^2 value of 0.9281 is less accurate to you cal curve.
I've attached a video that helps explain the theory a little better and its easy to understand (probably better than I did)
ruclips.net/video/Guo7ntYAyIY/видео.html