Linear Regression and Correlation: Summary 1.3 2008/06/23 16:50:14 GMT-5 2008/07/15 11:49:04.445 GMT-5 Barbara Illowsky illowskybarbara@deanza.edu Susan Dean deansusan@deanza.edu Connexions cnx@cnx.org elementary statistics This module provides a summary on Linear Regression and Correlation as a part of Collaborative Statistics collection (col10522) by Barbara Illowsky and Susan Dean. Bivariate Data: Each data point has two values. The form is (x,y).Line of Best Fit or Least Squares Line (LSL): y^ = a + bx x = independent variable; y = dependent variableResidual: Actual y value-predicted y value=y-y^Correlation Coefficient r:Used to determine whether a line of best fit is good for prediction. Between -1 and 1 inclusive. The closer r is to 1 or -1, the closer the original points are to a straight line. If r is negative, the slope is negative. If r is positive, the slope is positive. If r=0, then the line is horizontal.Sum of Squared Errors (SSE): The smaller the SSE, the better the original set of points fits the line of best fit.Outlier: A point that does not seem to fit the rest of the data.