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.
 
I have been insisting to my statistics students that for probabilities, the rule is the number of significant figures is the number of digits past the leading zeros or leading nines. For example to give 4 significant figures for a probability: 0.000001234 and 0.99999991234 are the correct number of decimal places. That way the complementary probability can also be given to the same significant figures ( 0.999998766 and 0.00000008766 respectively). More generally if you have a value that...

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