good fits - bad models ?

Enter six data points - e.g car speed at one second intervals from beginning of braking until car has stopped, or average temperature at two week intervals from September to December. Fit a linear model to your data. CHALLENGE - Deviation is defined as the square root of the sum of the squares of the vertical distances to to linear model function. Why not use some measure that depends on the perpendicular distances to the linear model function? CHALLENGE - If we can (almost) always fit a finite set of data with a polynomial, why do we bother with statistical techniques like regression? Why does it say almost?