Yet correlation analysis is argueably the single most important thing that one does with a data set. Such an analysis can help define trends, make predictions and uncover root causes for certain phenomena.

While there are standard tools for performing correlation analyses (this will be provided to you later) it is often done poorly. As a result, lots of erroneous analysis gets published, in virtually all fields.

Often times, there is simply not enough data to adequately define a correlation. This allows one to make ridiculous predictions which, although they can be supported by the data, make no sense.

A favorite example:

Here is a prediction that I made in the year 1839 (that was in the pre-internet era):

All presidents that are elected in a year that ends with a zero will die in office:

- 1840: William Henry Harrison dies in April of 1841
- 1860: Abraham Lincoln Assassinated
- 1880: Garfield Assassinated
- 1900: McKinley Assassinated
- 1920: Harding Died of illness
- 1940: Roosevelt Died of illness
- 1960: Kennedy Assassinated
Gee 7 events in a row, pretty good prediction, huh!

- 1980: Reagan Almost Assassinated
Conclusion: Its probably safe to run for office in the year 2000

Basics of Correlation:

- You have two sampling variables (X and Y). Does the
value of one variable depend on the other or are the variables
random.
- Correlation then determines the probability that the two
variables are randomly correlated.
- Correlations can be strong or weak.
- Strong correlations are extremely useful in identifying root
causes and/or what the most important variables are
- Weak correlations open the way for tremendous ambiguity
- If two variables are correlated, it means that one variable
can be written as a
*function*of the other. - Math Anxiety Alert While functions
can be complicated, here we will only be concerned with
*linear*correlation:Y = AX + B - A = the slope of the correlation
- B = a calibration constant (often called a zeropoint)
- if A is negative, the variables are anti-correlated

Correlation can be used to summarise the amount of association between 2 continuous variables. Plotting a "scatter" yields a "cloud"of points :

In general, we measure correlation by a parameter known as the correlation
coefficient, * r *.

* r * is between - 1 and 1

Mathematically, r is defined as

But we don't really care about this - we only care about using the value
of *r* as a rough guide to how well two variables are correlated.
Usually your eye is a good estimator of *r*.

Regression is now built into the tool

Let's look at the some examples using correlation and regression analysis.

An example data set:

The Goal here is to find the best relation between, Y the dependent variable, and X- the independent variable.

X is the variable that would measure because Y is more difficult, and in some cases might be impossble to Measure.

Since we are measuring X - the role of measurement error will be come important. More on that later.

X Y Other 10.0 12.5 22.0 8.5 11.1 18.0 16.8 22.3 19.5 11.2 15.4 15.5 17.8 25.3 12.2 5.4 8.4 11.6 21.6 32.6 7.4 9.6 18.5 0.8 14.0 15.3 30.5 13.5 16.8 22.7

The correlation between X and Y is shown here:

Y_{pred} = 1.39X + 0.03 ; dispersion = 2.53 ; r = 0.94

Let's calculate the residuals for each data point now.

X Y Y-pred Residual Significance 10.0 12.5 13.93 1.43 0.56 8.5 11.1 11.85 0.75 0.29 16.8 22.3 23.38 1.08 0.43 11.2 15.4 15.60 0.20 0.08 17.8 25.3 24.77 -0.52 -0.21 5.4 8.4 7.53 -0.86 -0.34 21.6 32.6 30.05 -2.54 -1.00 9.6 18.5 13.37 -5.12 -2.02 14.0 15.3 19.49 4.19 1.65 13.5 16.8 18.80 2.00 0.79

Try rejection analysis to improve the fit (mainly lower the scatter). Reject the most deviant point in the above.

That new relation is plotted here:

Y_{pred} = 1.48X -1.76 ; dispersion = 1.96

This representation of the data is a more reliable and robust. Can anything be done to further reduce the scatter?

In most cases usually not. In this case, however, we have a third variable labelled other. What happens if we plot the residuals against other?

residual = 0.30*Other -4.75

since residual = Y_{actual} - Y_{pred} = Y_{actual}
- 1.39X + 0.03

so we have Y_{actual} = Y_{pred} + residual

or
Y_{actual} = 1.39X +0.03 + 0.30*Other - 4.75

The good correlation between the residuals and another variable in this case allows us to make a linear combination to further reduce the scatter. To wit

X Y Other Y-pred Residual 10.0 12.5 22.0 12.08 -0.42 8.5 11.1 18.0 11.20 0.10 16.8 22.3 19.5 22.28 -0.02 11.2 15.4 15.5 15.70 0.30 17.8 25.3 12.2 25.86 0.56 5.4 8.4 11.6 8.80 0.40 21.6 32.6 7.4 32.58 -0.02 9.6 18.5 0.8 17.88 -0.61 14.0 15.3 30.5 15.09 -0.21 13.5 16.8 22.7 16.73 -0.07

The dispersion has lowered from 2.53 to 0.3 with the addition of this second term. Hence, Y can be predicted from X but can be predicted very accurately from X and Other.

One final point about measurement errors in X.

Suppose I have two relations involving different quantities but both use the same independent variable X.

Relation 1:

Y_{1} = 1.5X + 1.5 ; with a dispersion of 0.5 units

Relation 2:

Y_{2} = 6.0X + 2.5 ; with a dispersion of 0.3 units

Suppose that I can only make measurements of X which are accurate
to 10%. This means that, despite a lower disperions,
Y_{2} is less well determined than Y_{1}!

Example: x = 10 +/- 1

Y_{1} = 1.5*10 +1.5 = 16.5

Y_{1} = 1.5*11 +1.5 = 18.0

So 10% uncertainty in X translates into +/- 1.5 unit uncertainty
in Y.

For Y_{2} = 6.0*10 +2.5 = 62.5

For Y_{2} = 6.0*11 +2.5 = 68.5

So 10% uncertainty in X translates into +/- 6.0 unit uncertainty

So relations which have steep slopes require that X be measured very accurately.

Final points about regression:

- Sometimes correlations can be defined by a single point which significantly extends the range of the data.
- Sometimes a few deviant points (devaint due to measurement error or poor sampling) can degrade the correlation. One cycle of rejection should occur for most data sets.