# How can I calculate relative difference among 3 columns?

So I have 4 columns of data, shown below. The first column is the independent variable and the next 3 columns are the values of dependent variables.

1    223064 222724  222672
2    220066 219400  219298
3    217105 216128  215978
4    214105 212906  212709
5    211295 209732  209492


I would like to calculate the relative difference among the 3 dependent variables with respect to the variation in the independent variable, and the output should be a single value for each row. To clarify, the 3 (dependent variable) columns are values for the same event but for different times (independent variable) and they are diverging as we go downwards. Basically, I am trying to show the divergence of the data.

One way I can think of it is to calculate the difference among the columns themselves, ABS(1-2), ABS(2-3), ABS(3-1). Then Calculate the Mean and Standard Deviation of the 3 differences.

Is this a good way to do it? or, is there any other method to show the relative differences among the columns beside the above one suggested?

A common one (described in the link) is $\eta^2$. Mentally, you can consider this as finding out what fraction of variation of all of your data is due to group differences.
• Sure. The F value of a one way ANOVA could itself be useful. If you are familiar with the ANOVA process, $\eta^2$ is just the SSR (or SSB) divided by the SST. I should add that $F<F_{crit}$ gets into hypothesis testing, which moves a bit away from your original question. Measures of effect size, in general, will be more true to what you have asked. Commented Jun 14, 2017 at 13:38