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A random sample of size ... from a bivariate normal distribution with mean ... , unit variances, and correlation coefficient ... is generated. The sample correlation ... is shown as well as the Cook's distance corresponding to the locator point. Several methods of fitting the regression line are available. The LS (least-squares) method uses Mathematica's built-in function ... . See the Details section for more information about L1 (least absolute deviation) and RLINE (resistant line). Cook's distances provide an indication of points that have a large influence on the slope of the LS regression. As a rough rule, points that exceed ... , where ... is the sample size, may be influential. The recommended practice is to look at a plot of all Cook's distances. The Cook's distances are determined using ... to fit the LS regression. Two plots are available for the Cook's distances. See Details for more information. The slider zoom can be used to zoom out and move the locator some distance away to explore its influence on the regression, correlation, and Cook's distance. The effect of sample size ... and correlation ... may also be explored. By varying the random seed, you can explore the stochastic variation for a fixed initial data configuration.

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      EUN,LOM,LRE4,work-cmr-id:262349,http://demonstrations.wolfram.com:http://demonstrations.wolfram.com/InfluentialPointsInRegression/,ilox,learning resource exchange,LRE metadata application profile,LRE

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