This is the course Web site for
Agricultural
Statistics, AAEC 3401, an introductory (first)
course in applied statistics (prerequisite is college algebra or higher,
and basic understanding of Excel spreadsheets).
Statistics is the science of collecting,
organizing, summarizing, and analyzing information in order to draw
conclusions (from
Fundamentals of
Statistics by Michael Sullivan). Statistics is a
discipline that plays a major role in many different areas. For
example, it is used in sports to help a sports team make informed
decisions about their competition. It is used to predict the outcome of
elections and to help determine government policies. Statistics assists
in determining the effectiveness of new medications. It is used by
agronomists to find higher yielding varieties of crops. Animal
scientists use statistics to find new feeding regimes for animals.
Statistics plays a role in economics in testing hypotheses about
economic relations. Statistical models are used by economists to
predict economic output, interest rates, stock and commodity prices, and
many other economic variables.
Used appropriately, statistics can help us
understand the world we live in. Used inappropriately, it can lend
support to inaccurate beliefs. Understanding the methods and
procedures of statistics will equip you with knowledge to understand and
critique studies and experiments. With this ability, you will be an
informed consumer of information, which will enable you to distinguish
solid statistical analyses from the sterile presentation of numerical
facts.
By the end of this course, you should be
able to:

Organize data using graphs and tables
and understand important features of a dataset.

Calculate measures of central tendency
and dispersion (e.g., mean and standard deviation) and use these
measures to understand important features of a dataset.

Compute and interpret probabilities and
find probabilities for discrete and continuous random variables.

Understand the concept of a sampling
distribution and calculate the mean and standard deviation of the
sampling distribution of the mean.

Test hypotheses about means and
proportions for one or more populations.

Develop and interpret confidence
intervals for means and proportions for one or more populations.

Estimate a linear relation between two
variables and use it to predict.

Measure quantitatively the relation
between two variables and test a hypothesis about the relation.