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The process for finding a curve that fits multiple curves represented as individual data points in a dataset is called curve fitting. The steps involved in this process are as follows:

1. Collect the data: Collect the data representing the individual curves that need to be fitted with a single curve.

2. Choose the type of curve: Choose the type of curve that best fits the data. This could be a polynomial, exponential, logarithmic or any other type of curve.

3. Determine the parameters: Determine the parameters of the chosen curve. This involves adjusting the coefficients or constants in the equation for the curve to best fit the data.

4. Perform curve fitting: Perform curve fitting on the dataset using a software package like MATLAB, Python or R. This involves using the least squares method to minimize the difference between the actual data points and the fitted curve.

5. Analyze the fit: Analyze the fit of the curve using statistical measures like the R-squared value, root mean square error (RMSE), mean absolute error (MAE), etc. A good fit would have a high R-squared value and low errors.

6. Refine the fit: If the fit is not good enough, refine the fit by adjusting the parameters or using a different type of curve.

7. Evaluate the results: Evaluate the results and draw conclusions from the fitted curve.