Nonlinear Regression Analysis for Biological Experiment

In summary, the conversation discusses a biological experiment on the effect of trypsin concentration on the rate of casein hydrolysis. The data was analyzed using nonlinear regression with a 3rd degree polynomial and parameters such as sy.x and degrees of freedom were obtained. The individual is seeking help in determining the reliability of the results and if there is a relationship between enzyme concentration and rate. The data is provided and the suggestion to use Michaelis-menten equation is made.
  • #1
garytse86
311
0
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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  • #2
can someone help?
 
  • #3
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.
 
  • #4
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
 
  • #5
why you fit the data with a 3 degree

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

Related to Nonlinear Regression Analysis for Biological Experiment

What is nonlinear regression analysis for biological experiment?

Nonlinear regression analysis is a statistical method used to model and analyze complex relationships between variables in biological experiments. It allows researchers to identify and quantify nonlinear patterns and trends in their data, and make predictions about the behavior of the variables being studied.

What types of biological experiments can benefit from nonlinear regression analysis?

Nonlinear regression analysis can be applied to a wide range of biological experiments, including studies on growth and development, enzyme kinetics, drug dose-response relationships, and ecological dynamics. Any experiment that involves complex relationships between variables can benefit from this type of analysis.

How is nonlinear regression analysis different from linear regression analysis?

The main difference between nonlinear and linear regression analysis is that nonlinear regression models allow for more complex relationships between the variables being studied. In linear regression, the relationship between the independent and dependent variables is assumed to be linear, while in nonlinear regression, this relationship can take on a variety of shapes, such as exponential, logarithmic, or polynomial.

What are the benefits of using nonlinear regression analysis in biological experiments?

Nonlinear regression analysis allows researchers to uncover hidden patterns and trends in their data that may not be evident with traditional linear models. It also provides more accurate and precise estimates of the relationships between variables, which can lead to more informed and reliable conclusions.

What are the limitations of nonlinear regression analysis for biological experiments?

Nonlinear regression analysis can be more complex and computationally demanding than linear regression, which can make it difficult to implement for large datasets. It also requires a good understanding of statistical concepts and methods, and the choice of an appropriate model can be subjective and require some trial and error.

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