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During my career as a statistical consultant in academia and industry, I’ve run across numerous examples of people misusing data analysis tools.  This can result in suboptimal decisions and/or inappropriate conclusions.

Like many other data analysts, I’ve wrestled with how to improve the decision-making ability of intelligent individuals who have not been adequately trained in how to properly analyze data. Many times, statistics, data analytics, or data science courses are taught in the first few years of university education. By the time people need the tools and techniques taught in these courses, the knowledge gained has become a distant memory. Whether your project consists of learning a new technique to address a specific problem, or jogging the memory of several topics learned in the past, I hope this website will enable each person to make more informed decisions regarding their data analysis problems.

The content on each webpage is split into six sub-categories:

  1. Introduction
  2. Assumptions
  3. An example with code or menu guidance
  4. Annotated Output
  5. Analysis Results
  6. What to do when things go wrong

The purpose of formatting each webpage this way is so that people can step through each portion of each statistical analysis at their own pace, hopefully without getting lost. This should allow for individuals to learn from an example, and then apply each technique to their own data.

Although all portions of each page are important, the ‘Annotated Output’, ‘Assumptions’, and ‘What to do when things go wrong’ sections should be particularly useful. The information contained in these areas should provide individuals with guidance on how to interpret each analysis, a level of confidence in the results by validating statistical assumptions, and what options might be available when things don’t go as planned.


This site is strictly for educational purposes only and comes with no warranty of any kind.  The decisions that you make regarding any particular data analysis problems are your own. Terms such as SAS and SPSS are registered trademarks of their respective organizations.

Although I make every effort to provide a clear and concise solution to many popular data analysis problems, this website should not be considered a stand-alone reference for all things related to data analysis. Each topic contains a list of resources which should provide a more comprehensive explanation of the topic covered. I encourage all users of this website to investigate these resources and defer to them if needed.

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