# How to find coefficients for Logistic Regression [duplicate]

How do you find the coefficients for logistic regression?

Is it a case of just transforming the samples from the dependent variable and preceding to fit it like a linear regression?

No, it is not just transforming the outcome variable and performing linear regression. There is a difference between using a link function, and transforming the outcome. For example, using a log link, you are estimating $$\log( E(Y|X) )$$, but if you log transform the outcome variable and use linear regression, you are estimating $$E( \log(Y) | X )$$, which is totally different (see Jensen's inequality, for example).
To fit a logistic regression model you perform maximum likelihood. The data is assumed to follow a Bernoulli distribution, conditional on the covariates. The success probability is equal to the inverse logit of $$X \beta$$, i.e., $$1/(1+\exp(-X \beta))$$, and you can form the likelihood out of that. Optimize that as a function of $$\beta$$ and you have the MLE, which is what you get when you get when you fit a logistic regression model.