Logit function in Logistic Regression I have studied the Perceptron algorithm and now I'm trying to understand logistic regression. I know what odd ratio and Logit functions are but I don't understand why we create the following equation:

$logit(\frac{p}{1-p}) = \bar{\omega}^{T}\bar{x}$

where w is the weight vector. What does it represent, and why? 

Can anyone show me, how does that work?
W is just the coefficients that we estimate to determine the contribution of each variable to the model. We attempt to estimate the log odds ratio by multiplying coefficients by the input variables that we have. 

If we didn't have W, by default it would just be the 1 matrix multiplied by X, or each variable output would be assumed to affect the response equally (ignoring the scale differences between predictors).