Agricultural Price Analysis
Instructor: Dr. Hector Zapata
Basic Statistics for Price Analysis
Descriptive statistics are some of the most basic tools used for statistical data
analysis, and their application to price analysis is not an exception. Statistical
measures such as the mean, standard deviation, minimum and maximum values,
coefficient of variation, and coefficient of linear association (correlation
coefficient) are often used to describe price behavior. Prior to conducting any
statistical analysis, it is good practice to construct graphs of data as a means of
learning price patterns that prices follow over time and also for checking price
data quality (making sure that there are no obvious errors in the price data).
Three methods of analysis, tabular, graphical and statistical, are covered in this
lecture. Illustrations are provided using Excel, the spreadsheet program that will
be used to conduct research for the newsletter project. At the end of this lecture,
readers should have some proficiency on the fundamentals of graphical analysis,
calculation of descriptive statistics in Excel, analysis of descriptive statistics, and
writing descriptive analyses for technical reports.
Tables should be descriptive enough for the reader to know what data the table
contains, the time period, and the units of measurement for each variable; this
information is placed on the title.
Data are often collected at local, state, regional,
national or international level. This information should be specified in the title
also. The title is followed by a header; this could be two horizontal lines going
across the page with one or more spaces in between the lines to allow for column
names. Units of measurement are included, either inside the header or in the
body of the table.
The body of the table is the one containing the numbers that
Another horizontal line is added at the bottom of the body of the
table to delimit the end of the data set.
The data source(s) are included in a
smaller font at the bottom of the table; cite sources in such a way that the reader
can go to them and easily locate the information that the table contains. Finally,
footnotes are often used to describe details about the table that help clarify how
variables are measured, whether data transformations have been applied,
specific description of certain variables, and many other important details.
example for U.S. corn exports is provided below. The data was downloaded from
the USDA/ERS website: