price-elasticity-derivation

Hi Suchada,
thanks for reaching out! Beta are the weight coefficient for the independent variables, in our case they are price, promotion, etc. Beta_0 is the offset or intercept. In practice we obtain the optimal betas from fitting the logistic regression on our data set.
For a more detailed explanation on what betas are and how they tie in with machine learning, you can check out the Machine Learning in Python course. Here's a link to the linear regression model lecture:
https://learn.365datascience.com/courses/machine-learning-in-python/the-linear-regression-model/
Here the concept is explained for a case of one dependent variable(x_1) only, so we only have the corresponding beta_1. But the same concept applies in the general case when we have more independent variables.
Hope this helps!

Best,
365 Eli

What about  ?

What is it? and I would like to know how to transform to be ?