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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 variable

Residual: 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.

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Source:  OpenStax, Elementary statistics. OpenStax CNX. Dec 30, 2013 Download for free at http://cnx.org/content/col10966/1.4
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