Steph Locke explains what beta values on parameters in a regression actually signify:

When we read the list of coefficients, here is how we interpret them:

The intercept is the starting point – so if you knew no other information it would be the best guess.

Each coefficient multiplies the corresponding column to refine the prediction from the estimate. It tells us how much one unit in each column shifts the prediction.

When you use a categorical variable, in R the intercept represents the default position for a given value in the categorical column. Every other value then gets a modifier to the base prediction.

Linear regression is easy, but the real value here is Steph’s explanation of logistic regression coefficients.