STAT 101 Lecture Notes - Lecture 23: Mean Squared Error
Document Summary
Stat 101 - introduction to business statistics - lecture 23: line of fit. Mathematically leverage the equation: derivatives and optimizations. The best line minimizes the sum of the squares of the vertical distances from the point to the line, and is called the least squares line. Sometimes, we may fit a line on a transformed scale, then back-transform, which gives best fitting curves. The difference between y and yhat (y - yhat) is called the residual (e) B1 = r (sy / sx ) , where r is the correlation between y & x. B0 = ybar - (b1 * xbar) Slope interpretation: the change in y for every one unit change in x. Ie. 3. 7 additional rooms per additional crew. Intercept interpretation: more problematic. sometimes the intercept does not have a clear interpretation. sometimes it is an extrapolation outside the range of data. Residual = the vertical distance from the point to the least squares line.
