Steve M., Darby D.M., Geostatistics Explained - An Introductory Guide for Earth Scientists
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depth and distance as factors. Is there a significant effect of distance? Is
there a significant effect of depth in the sediment? (b) The marine
geoscientist who collected these data mistakenly analyzed them using
a single-factor ANOVA comparing the three different cores but ignor-
ing depth (i.e. the table below was simply taken as three columns giving
independent data). Repeat this incorrect analysis. Is the result signifi-
cant? What might be the implications, in terms of the conclusion drawn
about the concentrations of PAHs and distance from the refinery, if this
were done?
Distance
Depth (m) 3 km 2 km 1 km
1 1.11 1.25 1.28
2 0.84 0.94 0.95
3 2.64 2.72 2.84
4 0.34 0.38 0.39
5 4.21 4.20 4.23
(2) A glaciologist who wanted to compare the weight of sediment deposited
per square meter in two glacial lakes chose three locations at random
within each lake and deployed four sediment traps at each, using a total
of 24 traps. This design is summarized below.
Location Number of traps
First location in lake 1 4 traps
Second location in lake 1 4 traps
Third location in lake 1 4 traps
First location in lake 2 4 traps
Second location in lake 2 4 traps
Third location in lake 2 4 traps
The glaciologist said “I have a two-factor design, where the lakes are one
factor and the trap grouping is the second, so I will use a two-factor
ANOVA with replication. ” (a) Is this appropriate? What analysis would
you use for this design?
14.8 Questions 193
15 Relationships between variables:
linear correlation and linear
regression
15.1 Introduction
Often earth scientists obtain data for a sample where two or more variables
have been measured on each sampling or experimental unit, because they
are interested in whether these variables are related and, if so, the type of
functional relationship between them.
If two variables are related they vary together – as the value of one
variable increases or decreases, the other also changes in a consistent way.
If two variables are functionally related, they vary together and the value
of one variable can be predicted from the value of the other.
To detect a relationship between two variables, both are measured on
each of several subjects or experimental units and these bivariate data
examined to see if there is any pattern. One way to do this, by drawing a
scatter plot with one variable on the X axis and the other on the Y axis, was
described in Chapter 3. Although this can reveal patterns, it does not show
whether two variables are significantly related, or have a significant func-
tional relationship. This is another case where you have to use a statistical
test, because an apparent relationship between two variables may only have
occurred by chance in a sample from a population where there is no
relationship. A statistic will indicate the strength of the relationship,
together with the probability of getting that particular result, or an outcome
even more extreme, in a sample from a population where there is no
relationship between the two variables.
Two parametric methods for statistically analyzing relationships between
variables are linear correlation and linear regression, both of which can be
used on data measured on a ratio, interval or ordinal scale. Correlation and
regression have very different uses, and there have been many cases where
correlation has been inappropriately used instead of regression and vice
194
versa. After contrasting correlation and regression, this chapter explains
correlation analysis. Regression analysis is explained in Chapter 16.
15.2 Correlation contrasted with regression
Correlation is an exploratory technique used to examine whether the values
of two variables are significantly related, meaning whether the values of
both variables change together in a consistent way. (For example, an
increase in one may be accompanied by a decrease in the other.) There is
no expectation that the value of one variable can be predicted from the
other, or that there is any causal relationship between them.
In contrast, regression analysis is used to describe the functional rela-
tionship between two variables so that the value of one can be predicted
from the other. A functional relationship means that the value of one
variable (called the dependent variable, Y) has some relationship to the
other (called the independent variable, X) in that it is reasonable to
hypothesize the value of Y might be affected by an increase or decrease in
X, but the reverse is not true. For example, the amount of pitting on
limestone buildings is caused by dissolution resulting from acid rain and
is likely to be affected by the age of the building because older stones have
been exposed to the elements for longer. The opposite is not true – the age of
the building is not affected by weathering! Nevertheless, although the
amount of weathering is dependent on the age of the building it is not
caused by age – it is actually caused by acid rain. This is an important point.
Regression analysis can be used provided there is a good reason to hypothe-
size that the value of one variable (the dependent one) is likely to be affected
by another (the independent one), but it does not necessarily have to be
caused by it.
Regression analysis provides an equation that describes the functional
relationship between two variables and which can be used to predict values
of the dependent variable from the independent one. The very different uses
of correlation and regression are summarized in Table 15.1.
15.3 Linear correlation
The Pearson correlation coefficient, symbolized by ρ (the Greek letter rho) for
a population and by r forasample,isastatisticthatindicatestheextentto
15.3 Linear correlation 195
which two variables are linearly related, and can be any value from –1to+1.
Usually the population statistic ρ is not known, so it is estimated by the sample
statistic r.
An r of +1, which shows a perfect positive linear correlation, will only be
obtained when the values of both variables increase together and lie along a
straight line (Figure 15.1(a)). Similarly, an r of –1, which shows a perfect
negative linear correlation, will only be obtained when the value of one
variable decreases as the other increases and the points also lie along a
straight line (Figure 15.1(b)). In contrast, an r of zero shows the lack of a
relationship between two variables and Figure 15.1(c) gives one example
where the points lie along a straight line parallel to the X axis. When the
points are more scattered but both variables tend to increase together, the
values of r will be between zero and +1 (Figure 15.1(d)), while if one variable
tends to decrease as the other increases, the value of r will be between zero
and −1(Figure 15.1(e)). If there is no relationship and considerable scatter
(Figure 15.1(f)) the value of r will be close to zero. Finally, it is important to
remember that linear correlation will only detect a linear relationship
between variables – even though the two variables shown in Figure 15.1(g)
are obviously related the value of r will be close to zero.
15.4 Calculation of the Pearson r statistic
A statistic for correlation needs to reliably describe the strength of a linear
relationship for any bivariate data set, even when the two variables have
Table 15.1 A contrast between the uses of correlation and regression.
Correlation Regression
Exploratory – are two variables
significantly related?
Definitive – what is the functional relationship
between variable Y and variable X and is it
significant?
Predictive – what is the value of Y given a
particular value of X?
Neither Y nor X has to be dependent
upon the other variable. Neither
variable has to be determined by
the other.
Variable Y is dependent upon X. It must be
plausible that Y is determined by X, but Y does
not necessarily have to be caused by X.
196 Relationships between variables
been measured on very different scales. For example, the values of one
variable might range from zero to 10, while the other might range from
zero to 1000. To obtain a statistic that always has a value between 1 and −1,
with these maximum and minimum values indicating a perfect positive and
negative linear relationship respectively, you need a way of standardizing
the data. This is straightforward and is done by transforming the values of
both variables to their Z scores, as described in Chapter 7.
(a)
Y
Y
Y
XXX
X
X
XX
(b) (c)
(d)
(g)
(e) (f)
Figure 15.1 Some examples of the value of the correlation coefficient r.
(a) A perfect linear relationship where r = 1, (b) a perfect linear relationship
where r = −1, (c) no relationship (r = 0), (d) a positive linear relationship with
0<r < 1, (e) a negative linear relationship where –1<r < 0, (f) no linear
relationship (r is close to zero) and (g) an obvious relationship but one that will
not be detected by linear correlation (r will be close to zero).
15.4 Calculation of the Pearson r statistic 197
To transform a set of data to Z scores, the mean is subtracted from each
value and the result divided by the standard deviation. This will give a
distribution that always has a mean of zero and a standard deviation (and
variance) of 1. For a population the equation for Z is:
Z ¼
X
i
(15:1 copied from 7:3)
and for a sample it is:
Z ¼
X
i
X
s
(15:2)
Figure 15.2 shows the effect of transforming bivariate data measured on
different scales to their Z scores.
(a)
Y
Y
X
X
Z
y
0
Z
x
(c)
(b)
0
Figure 15.2 For any set of data, dividing the distance between each value and
the mean by the standard deviation will give a mean of zero and a standard
deviation (and variance) of 1.0. The scales on which X and Y have been
measured are very different for cases (a) and (b) above, but transformation of
both variables gives the distribution shown in (c) where both Z
x
and Z
y
have a
mean of zero and a standard deviation of 1.0.
198 Relationships between variables
Once the data for both variables have been converted to their Z scores, it
is easy to calculate a statistic that indicates the strength of the relationship
between them.
If the two increase together, large positive values of Z
x
will always be
associated with large positive values of Z
y
and large negative values of Z
x
will
also be associated with large negative values of Z
y
(Figure 15.3(a)).
If there is no relationship between the variables all of the values of Z
y
will
be zero (Figure 15.3(b)).
Finally, if one variable decreases as the other increases, large positive
values of Z
x
will be consistently associated with large negative values of Z
y
and vice versa (Figure 15.3(c)).
This gives a way of calculating a comparative statistic that indicates the
extent to which the two variables are related. If the Z
x
and Z
y
scores for
each of the units are multiplied together and s umm ed (Equation (15.3)),
data with a positive correlation will give a total with a positive value, while
data with a negative correlation will giveatotalwithanegativeone.In
contrast, data for two variables that are not related will g ive a total close to
zero:
X
n
i¼1
ðZ
xi
Z
yi
Þ (15:3)
Importantly, the largest possible positive value of
P
n
i¼1
ðZ
xi
Z
yi
Þ will
be obtained when each pair of data has exactly the same Z scores for
both variables (Figure 15.3(a)) and the largest possible negative value
will be obtained when the Z scores for each pair of data are the same
number but opposite i n sign (Figu re 15.3(c)). If the pairs of scores do
not v ary together completely in eithe r a positive or ne gative way the
total will be a smaller positive (Figure 15.3(d) or negative number
(Figure 15.3(f)).
This total will increase as the size of the sample increases, so
dividing by the degrees of freedom (N for a population and n – 1
for a sample) will g ive a statistic that has been “averaged,” just as the
equations for the standard deviation and variance of a sample are
averaged and corrected for sample size by dividing by n – 1. The
statistic given by Equation (15. 4) is the Pearson correlation coeffi-
cient r.
15.4 Calculation of the Pearson r statistic 199
Y
Y
YY
X
XX X
X
8
7
5
4
8
7
5
4
8
7
5
4
8
7
5
4
80
0
0
0
+0.87
+0.87
–0.87
–0.87
7
5
4
8
7
5
4
800
800
+1.10 +1.10
+1.10
+0.55 +0.55
+0.55
–0.55 –0.55
–0.55
–1.10
+1.10
+0.55
–0.55
–1.10
+1.10
+0.55
–0.55
–1.10
+1.10
+0.55
–0.55
–1.10
+1.10
+1.10
+0.55
+0.55
–0.55
–0.55
–1.10
–1.10
–1.10
–1.10
–0.55
–0.55
+0.55
+0.55
+1.10
+1.10
–1.10
–1.10
700
700
700
700
500
500
400
400
500
500
500
500
400
400
500
500
700
700
800
800
800
800
X
Y
XZ
x
(Z
xi
×
Z
yi
)
(Z
xi
×
Z
yi
)
3.00
2.40 -2.400.00
0.00 -3.00
Z
x
Z
y
Y
XZ
x
Z
y
Z
y
Y
X
Z
x
Z
y
Z
x
Z
y
Y
X
Z
x
Z
y
XX
(a)
Raw score Raw score Raw score
Z
score
Raw score
Z
score
Z score Z score
Raw score
Z
score
Raw score
Z
score
(b) (c)
(d)
(e)
(f)
i = 1
n
i = 1
n
Figure 15.3 Examples of raw scores and Z scores for data with (a) a perfect
positive linear relationship (all points lie along a straight line), (b) no
relationship, (c) a perfect negative linear relationship (all points lie along a
straight line), (d) a positive relationship, (e) no relationship, and (f) a negative
relationship. Note that the largest positive and negative values for the sum of
the products of the two Z scores for each point occur when there is a perfect
positive or negative relationship, and that these values (+3 and – 3) are
equivalent to n – 1 and – (n – 1) respectively.
200 Relationships between variables
r ¼
P
n
i¼1
ðZ
xi
Z
yi
Þ
n 1
(15:4)
More importantly, Equation (15.4) gives a statistic that will only ever be
between –1 and +1. This is easy to show. In Chapter 7 it was described how
the Z distribution always has a mean of zero and a standard deviation (and
variance) of 1.0. If you were to calculate the variance of the Z scores for only
one variable you would use the equation:
s
2
¼
P
n
i¼1
ðZ
i
ZÞ
2
n 1
(15:5)
but because
Z is zero, this equation becomes:
s
2
¼
P
n
i¼1
Z
2
i
n 1
(15:6)
and because s
2
is always 1 for the Z distribution, the numerator of
Equation (15.6) is always equal to n − 1.
Therefore, for a set of bivariate data where the two Z scores within each
experimental unit are exactly the same in magnitude and sign, the equa-
tion for the correlation between the two variables:
r ¼
P
n
i¼1
ðZ
xi
Z
yi
Þ
n 1
(15:7)
will be equivalent to:
r ¼
P
n
i¼1
Z
2
xi
n 1
or
n 1
n 1
¼ 1:0 (15:8)
Consequently, when there is perfect agreement between Z
x
and Z
y
for each
point, the value of r will be 1.0. If the Z scores generally increase together but
not all the points lie along a straight line, the value of r will between zero and
1 because the numerator of Equation (15.8) will be less than n − 1.
Similarly, if every Z score for the first variable is the exact negative
equivalent of the other, the numerator of Equation (15.8) will be the
15.4 Calculation of the Pearson r statistic 201
negative equivalent of n − 1 so the value of r will be –1.0. If one variable
decreases while the other increases but not all the points lie along a straight
line, the value of r will be between –1.0 and zero.
Finally, for a set of points along any line parallel to the X axis, all of the Z
scores for the Y variable will be zero, so the value of the numerator of
Equation (15.6) and r will also be zero.
15.5 Is the value of r statistically significant?
Once you have calculated the value of r, you need to establish whether it is
significantly different from zero. Statisticians have calculated the distribution
of r for random samples of different sizes taken from a population where there
is no correlation between two variables. When ρ = 0, the distribution of values
of r for many samples taken from that population will be normally distributed
with a mean of zero. Both positive and negative values of r will be generated
by chance and 5% of these will be greater than a positive critical value or less
than its negative equivalent. The critical value will depend on the size of the
sample, and as sample size increases the value of r is likely to become closer to
the value of ρ. Statistical packages will calculate r and give the probability the
sample has been taken from a population where ρ =0.
15.6 Assumptions of linear correlation
Linear correlation analysis assumes that the data are random representa-
tives taken from the larger population of values for each variable, which are
normally distributed and have been measured on ratio, interval or ordinal
scales. A scatter plot of these variables will have what is called a bivariate
normal distribution. If the data are not normally distributed, have been
measured on a nominal scale only or the relationship does not appear to be
linear, they may be able to be analyzed by a non-parametric test for
correlation, which is described in Chapter 19.
15.7 Conclusion
Correlation is an exploratory technique used to test whether two variables
are related. It is often useful to draw a scatter plot of the data to see if there is
any pattern before calculating the correlation coefficient, since the variables
202 Relationships between variables