How to Determine the Equation of a Curved Line of Best Fit?

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SUMMARY

The discussion focuses on determining the equation of a curved line of best fit for a dataset representing age and mass. The user suggests that an exponential fit is appropriate, based on the plotted natural logarithm of mass against age, which reveals a linear relationship. The dataset includes ages ranging from 5 to 14 and corresponding mass values. The conclusion emphasizes the utility of logarithmic transformation in identifying the best fit for nonlinear data.

PREREQUISITES
  • Understanding of exponential functions and their properties
  • Familiarity with logarithmic transformations
  • Basic knowledge of linear regression analysis
  • Experience with data visualization tools, such as Excel or Python's Matplotlib
NEXT STEPS
  • Research how to perform exponential regression using Python's NumPy library
  • Learn about the method of least squares for curve fitting
  • Explore data visualization techniques to plot logarithmic transformations
  • Investigate the use of R-squared values to assess the goodness of fit for nonlinear models
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Data analysts, statisticians, and researchers looking to model nonlinear relationships in datasets, particularly those involving age and mass correlations.

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I really need help. I will try my best to post this chart.

Age/Mass
5/ 24
6 / 25
7 / 27
8 / 28
9 / 31
10 /34
11 /38
12 /41
13 /47
14 /55
The slashes are there to separate the numbers and columns

How would you determine the equation of the line of best fit through these points. Its a curve by the way.
 
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An exponential fit seems rather close to me. Plot the natural log of the mass versus the age. It more or less conforms to a linear relationship. One would expect that a young boy may sometimes indulges!
 

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