AAEC 3401 - Agricultural Statistics



 Class Notes
 Lab Assignments:
 - Section1
 - Section2
 Course Syllabus
 Lab Syllabus


 Texas Tech



Dr. Emmett Elam

Room 301-B Ag. Science Bldg.
Dept. of Agr. and Applied Economics
Texas Tech University
806+742-2023, ext. 243






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.

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Department of Agricultural and Applied Economics, Texas Tech University 2004-05

Developed by:Chandan Kambli | Page updated on 01/12/2010