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Introduction
scatter plots
Correlation Analysis
Pearson Correlation

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Introduction
The basic purpose of correlation is to find out if two
variables are related to one another. If the variables are
related, regression then allows the use of the relationship
in the prediction of one variable given a score on the
other variable.
In this chapter we learn,
(1) how to calculate a correlation coefficient,
(2) how to evaluate both it`s significance and strength,
(3) how to test it for statistical significance, and finally
(4) how to construct and use a regression equation.

Scatter plots
Scatter plots graphically show these relationships. When you
use a good graphics package to draw scatter grams, the x
and y-axes should be approximately the same length. If not,
the picture starts to tell lies about the data. The figures below
show various scatter grams.
High positive
correlation
High negative
correlation
Low
correlation
Perfect positive
correlation

Correlation Analysis
Correlation coefficients indicate both the direction of the
relationship and its magnitude. If a correlation is negative, it
indicates that the high values on the first variable are related
to low values on the second variable, and low values on the
first variable go with high values on the second.
If the correlation is positive, then low values on the first variable
go with low values on the second variable, and high values on
the first variable, in general, go with high values on the second
variable. Of course, this direction is given by the sign (either +
or -) of the calculated correlation.

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