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# How to use the Spearman's Rank Correlation Coefficient in my experiment? Tweet

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1. How to use the Spearman's Rank Correlation Coefficient in my experiment?
Hi
I recently did a enzyme-inhibition experiment for my unit 6 coursework and i am now on the part where i have to carry out a statistical test on the data i collected from my experiment. This is what my experiment was bascally about :

It was just a simple enzyme-inhibition experiment. SO in a simplifed version :

1. I first poured 5cm3 of milk in to 4different test tubes
2. To another set of 4test tube, I then added 2cm3 of trypsin to each of them and then adden 1cm3 of the inhibitor at a concentration 10-4 mol dm^3.
3. I then poured the solution containing the enzyme trypsin and inhibitors into the milk
4. I then took reading for the Absorbance value from the colorimeter, at a regular interval for 15second for the next 4minutes.
5. I then repeated the experiment but with different concentration of the inhibitor, i.e 10^-3 mol dm^3 & 10^-2 mol dm^3

Now i was thinking of using the spearman'r rank correlation Coefficient to determine a relationship between the absorbance value for different inhibitor concentration but i am unsure as to how to go about doing it.

Am i right in understanding that i should i just use Absorbance values I collected for the lowest and highest concentration of inhibitor in order to determine there is a relationship between the different concentration of inhibition?

Or am i completely wrong in using this statistical test? PLEASE ADVICE!
Last edited by arnab; 03-05-2012 at 20:36.
2. Re: How to use the Spearman's Rank Correlation Coefficient in my experiment?
anyone?
3. Re: How to use the Spearman's Rank Correlation Coefficient in my experiment?
I kinda skimmed what you wrote, but a spearman's rank correlation cofficient will only show how strong or weak your correlation is - ranging from a valve of -1 (perfectly negative) though 0 (no correlation at all), to +1 (perfectly positive). It won't show anything else.

If you're trying to compare the your two variables (the x and y values on a scatter graph) you can use a linear regression model to work out of there if there is any relationship between x and y - i.e. does x actually affect y, or are does it not make a difference at all. You can also, from this, work out a confidence interval to show how reliable your hypothesis is.

If that makes sense though. What is it exactly you want to show with your statistical analysis? From that I could give a good test to use.
4. Re: How to use the Spearman's Rank Correlation Coefficient in my experiment?
Actually for the linear regression model you gotta assume a normal distribution for your data, so dunno if that'll work out.

For a correlation coefficient, do you have all your x and y values? Cause that's all you need.
Last edited by Waterstorm; 03-05-2012 at 23:54.
5. Re: How to use the Spearman's Rank Correlation Coefficient in my experiment?
(Original post by Waterstorm)
Actually for the linear regression model you gotta assume a normal distribution for your data, so dunno if that'll work out.

For a correlation coefficient, do you have all your x and y values? Cause that's all you need.
Yep, i got my values of x and y. I like prove the relationship that as x and y increases, the rates of reaction also increases.

E.g say X = 1% concentration of a solution
Y = 2% concentration of another solution

So like to prove that as X and Y increases, the rates of reaction also increases.
6. Re: How to use the Spearman's Rank Correlation Coefficient in my experiment?
Do you know how to work out the Pearson's correlation coefficient? The only difference is, is that Spearman's isn't affected by extreme values.

And as long as you have the x and y values, it's just a case of ranking x from lowest to highest with their corresponding y values, then working out the difference between the two. And then using the equation. Tbh, I never really liked this, and didn't understand it fully. I can message you our uni notes on it though, it's explained really well on there, if you want.
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