Geoscience Reference
In-Depth Information
Most of these dif erent types of data require specialized methods of analysis,
some of which are outlined in the next section.
1.4 Methods of Data Analysis
Data analysis uses precise characteristics of small samples to hypothesize
about the general phenomenon of interest. Which particular method is used
to analyze the data depends on the data type and the project requirements.
h e various methods available include:
•
Univariate methods
- Each variable is assumed to be independent of
the others and is explored individually. h e data are presented as a list
of numbers representing a series of points on a scaled line. Univariate
statistical methods include the collection of information about the
variable, such as the minimum and maximum values, the average, and the
dispersion about the average. h is information is then used to attempt to
infer the underlying processes responsible for the variations in the data.
Examples are the ef ects of chemical weathering on the sodium content of
volcanic glass shards, or the inl uence of specii c environmental factors on
the sizes of snail shells within a sediment layer.
•
Bivariate methods
- Two variables are investigated together to detect
relationships between these two parameters. For example, the correlation
coei cient may be calculated to investigate whether there is a linear
relationship between two variables. Alternatively, the bivariate regression
analysis may be used to i nd an equation that describes the relationship
between the two variables. An example of a bivariate plot is the
Harker
Diagram
, which is one of the oldest methods of visualizing geochemical
data from igneous rocks and simply plots oxides of elements against SiO
2
(Harker 1909).
•
Time-series analysis
- h ese methods investigate data sequences as a
function of time. h e time series is decomposed into a long-term trend,
a systematic (periodic, cyclic, rhythmic) component and an irregular
(random, stochastic) component. A widely used technique to describe
cyclic components of a time series is that of spectral analysis. Examples
of the application of these techniques include the investigation of cyclic
climatic variations in sedimentary rocks, or the analysis of seismic data.
•
Signal processing
- h is includes all techniques for manipulating a signal
