# 15 - We can always compute correlation for any bivariate...

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Unformatted text preview: We can always compute correlation for any bivariate quantitative data set. That doesn’t mean we should because the relationship is curved and the value of correlation may be misleading. Influential observations: An observation is influential for a statistical calculation if removing it would markedly change the result of the calculation. The result of a statistical calculation may be of little practical use if it depends strongly on a few influential observations. Points that are outliers in either the x or y direction of a scatterplot are often influential for the correlation. Points that are outliers in the x direction are often influential for the least-squares regression line. The reason that the outlier has little influence on the regression line is that it lies close to the regression line calculated only from the other observations except the outlier itself. If the outlier does not lie close to the line calculated from the other observations, it will be influential. Influential points are those whose x coordinates are outliers. We did not need the distinction between outliers and influential observations in Chapter 2. A single high salary that pulls up the mean salary for a group of workers is an outlier because it...
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## This note was uploaded on 02/27/2012 for the course STAT 121 taught by Professor Patticolling during the Winter '11 term at BYU.

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15 - We can always compute correlation for any bivariate...

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