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Note that r is calculated by multiplying the z-scores for each individual’s x - and y -values, adding the products, PowerPoint Presentation
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Note that r is calculated by multiplying the z-scores for each individual’s x - and y -values, adding the products,

Note that r is calculated by multiplying the z-scores for each individual’s x - and y -values, adding the products,

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Note that r is calculated by multiplying the z-scores for each individual’s x - and y -values, adding the products,

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  1. Correlation, r, measures the direction and strength of the linear relationship between two quantitative variables. We have (x, y) data on n individuals.r =

  2. Note that r is calculated by multiplying the z-scores for each individual’s x- and y-values, adding the products, and dividing by n-1.

  3. Properties of Correlation:r makes no distinction between explanatory and response variables. Correlation requires that both variables be quantitative. r does not change if we change the units of measurement. r has no unit of measurement – it is just a number.

  4. r > 0 indicates a positive association; r < 0 indicates a negative association.

  5. Values near 0 indicate a very weak linear relationship. The strength of the linear relationship increases as r moves away from zero and toward – 1 or + 1. – 1 ≤ r ≤ + 1 r = – 1 or + 1 only if all the data points are collinear.

  6. Correlation measures only the strength of a linear relationship between two quantitative variables. Correlation does not describe curved relationships, no matter how strong they may be. ALWAYS visually examine your data!!

  7. Use r with caution when the scatter-plot shows outliers. r is not a resistant measure.

  8. Correlation is not a complete description of two-variable data.When describing 2-variable data, give the values of , sx, and sy in addition to r.

  9. Note that the correlation coefficient only measures the strength of a linear relationship between your variables.  It cannot determine causation.A strong linear relationship between two quantities (life expectancy and # people per TV for example) does not guarantee a cause/effect relationship! 

  10. Generally, you can NOT conclude that there is a cause/effect relationship until you have run a carefully designed experiment (discussed in chapter 3). This idea is usually written  ASSOCIATION ≠ CAUSATION.