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