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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, - PowerPoint PPT Presentation


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

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Presentation Transcript
slide1

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

slide2

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.

properties of correlation r makes no distinction between explanatory and response variables

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.

r 0 indicates a positive association

r > 0 indicates a positive association;

r < 0 indicates a negative association.

1 r 1

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.

correlation measures only the strength of a linear relationship between two quantitative variables

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!!

slide9

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.

slide10

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! 

slide11

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.