Build a multiple linear regression, explain how several explanatory variables jointly relate to the variable being explained, and say what each fitted coefficient means

A multiple regression explains Y with k independent variables and is fitted by least squares: Y = b0 + b1X1 + b2X2 + … + bkXk + ε. The intercept is the expected value of Y when every independent variable is zero, which may have no economic meaning. Each slope is a partial effect: the expected change in Y for a one-unit change in that variable, holding all the others constant. That is why a slope in a multiple regression can differ, even in sign, from the slope of a simple regression on the same variable. Common slips are adding the intercept to a slope, ignoring units, and reading a partial slope as the total effect when variables move together.