MHB Jordan's Question from Facebook (About Regression)

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To analyze the model y = A + B e^x, it is suggested to transform the data by setting X = e^x, which simplifies the equation to a linear form y = A + B X. This transformation allows for the application of linear least squares regression on the new dataset X against y. Evaluating e^x at each point x generates the necessary data for this regression analysis. The discussion emphasizes the effectiveness of this method for fitting the model. Overall, this approach provides a straightforward way to handle non-linear relationships in regression analysis.
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Jordan from Facebook writes:

Help please,

2yod9uh.jpg
 
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Sudharaka said:
Jordan from Facebook writes:

Help please,

2yod9uh.jpg

If we assume that a model of the form [math]\displaystyle \begin{align*} y = A + B\,e^{x} \end{align*}[/math] is appropriate, then we note that if we have [math]\displaystyle \begin{align*} X = e^{x} \end{align*}[/math], then we have a nice linear equation [math]\displaystyle \begin{align*} y = A + B\,X \end{align*}[/math].

So it would help to start by evaluating [math]\displaystyle e^x [/math] at each point x, giving a new set of data which we can call X. Then perform a linear least squares regression for data set y against data set X.
 
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