Hi all,
I have a data set, including soil respiration and carbon amount. After plotting a scatter plot respiration vs carbon amount, I can see there is kind of positive relationship between them. I want to check if this relationship statically valid. But my problem is, soil respiration have much more data than the amount of carbon. Precisely, respiration was collected at 5 treatments, each treatment have 4 replicates, and sampled at 5 days, so in total there are 100 samples recorded. Carbon amount: 5 treatments, and each treatment has 3 replicates, so in total there are 15 samples recorded. It looks like impossible to do normal linear regression. My question is "is there any statistical way to measure their relationship in such a limited data set"?
Many thanks,
Xin
Hi Xin,
There are many tests to analyze relationship between two variables. Have you tried ANOVA? Alternatively, there are Pearson correlation, Chi-Square, Kruskall Wallis test. It will all depend on the format of your data.
Have a look at this:
https://stats.idre.ucla.edu/spss/whatstat/what-statistical-analysis-should-i-usestatistical-analyses-using-spss/
Hope it helps.
I am going to be a wet blanket and say no, assuming I understand your description. The only way a linear model (regression, anova, etc) will work is if there is a y for each set of x's. Suggest you see this book and think about redesigning your experiment.
https://www.amazon.com/Design-Analysis-Experiments-Douglas-Montgomery/dp/1118146921/ref=sr_1_1?keywords=montgomery+design+of+experiments&qid=1561252854&s=books&sr=1-1
HTH, David Booth
It's hard to understand your design based on your description (how do you make a scatterplot of respiration and carbon amount of these observations are not paired; what are the units of observation)? It would help if you could show your plot and/or a snapshot of how your data are organized.
Thank you all for the kind answers. As I understand, to quantify relationship, x and y should always be paired? So if I have more x than y, then it is impossible to know there relationship?
I made the scatter plot based on their means, not all the measured samples. Is this wrong?
Means across what? It is not clear what the units of your data are--I don't understand what each dot on your scatterplot represents or where it comes from. Keep in mind that people responding here may come from different research backgrounds and not be familiar with the typical experiment designs used in your area.
It would help if you could show your plot and/or a snapshot of how your data are organized.
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