Nonlinear Regression Analysis for Biological Experiment

AI Thread Summary
The discussion centers on analyzing the reliability of results from a biological experiment investigating the effect of trypsin concentration on casein hydrolysis using nonlinear regression. The user utilized Graphpad Prism for data analysis and is seeking guidance on interpreting parameters like sy.x and degrees of freedom. Suggestions include using simpler statistical tools for analysis and considering the Michaelis-Menten equation for better fitting of enzyme kinetics data. The user provided data showing mean times and rates for varying enzyme concentrations, indicating a potential increase in reaction rates with higher concentrations. Overall, the focus is on improving the analysis and understanding of the experimental results.
garytse86
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Hello there. I have just finished a biological experiment on "effect of trypsin concentration on rate of casein hydrolysis"
I have already obtained a graph, and I used a program called "Graphpad Prism" to analyse the data usin nonlinear regression (3rd degree polynomial). I have got all the parameters like sy.x, degrees of freedom... But how do I use these parameters to analyse whether my results are reliable, and by varying enzyme concentration results in an increase in rate?

I have also considered Spearman's rank coefficient but this is only for linear relationships. What else can I do to determine whether my results show regression?

Thank you very much.
 
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can someone help?
 
Show us the data

You sound like your in AP Stats still... but really the data should be able to be analyzied with something as simple as a TI-83 + with little problem... If perhaps you showed the data I could further explain how well your data fits your regression line predicted.
 
hello here is the data:

Concentration (%) Mean time (s) Mean rate (s-1) Percentage mean rate (%)
0 0 0 0
0.1 328 0.00304878 27.43902439
0.2 214 0.004672897 42.05607477
0.3 128 0.0078125 70.3125
0.4 130 0.007692308 69.23076923
0.5 98 0.010204082 91.83673469
0.6 98 0.010204082 91.83673469
0.7 100 0.01 90
0.8 96 0.010416667 93.75
0.9 92 0.010869565 97.82608696
1.0 90 0.011111111 100
 
why you fit the data with a 3 degree

why not u fit your data with Michaelis-menten equation?
 
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