Prerequisites

Statistics.com is a professional development learning program, and prerequisites are kept to a minimum. We instead provide information that can help you decide whether a course is right for you. Many courses list as prerequisites familiarity with the material covered in Statistics.com's sequence of courses in beginning and introductory statistics. For many people, particularly those who have covered this material before, are comfortable with it, and need a refresher, this will be sufficient.

In any case, the decision about whether you meet the prerequisites for a particular course is up to you -- Statistics.com does not review your course record or background to make decisions about course admittance.

Your preparedness for a course that requires knowledge of basic statistics is a function of these factors:

  1. Whether you have taken a course in introductory statistics,
  2. How long ago you took it,
  3. Your comfort with the course content,
  4. Whether you have made use of the concepts since then.
Statistics.com has four courses at the introductory level that are often cited in the prerequisites for other courses. Three of them form a sequence, and the fourth is a survey. You should consider all of the above factors when deciding how many of these courses are needed for background.

There are two additional courses we can recommend to reinforce your general background:

  1. Introduction to Resampling. Resampling methods lie at the heart of inference (confidence intervals and hypothesis testing) and are an ideal route to gaining a deep (as opposed to mechanistic) understanding of these important topics.
  2. Introduction to Biostatistics. This course is broad in its nature, not focusing on any particular method. It is a good vehicle for extending your fundamental statistics knowledge, prior to taking courses that specialize in particular methods.

If you plan on taking more advanced courses at statistics.com, these courses will help you with the background needed for a mastery of the theory and concepts involved:

  1. Matrix Algebra (for multivariate statistics)
  2. Maximum Likelihood Estimation (for modeling courses)

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