Statistics for Laboratory Scientists I – JHSPH 140.615

This 2006 JHSPH OpenCourseWare graduate course:

  • introduces the basic concepts and methods of statistics with applications in the experimental biological sciences.
  • demonstrates methods of exploring, organizing, and presenting data, and it introduces the fundamentals of probability.
  • presents the foundations of statistical inference, including the concepts of parameters and estimates and the use of the likelihood function, confidence intervals, and hypothesis tests.
  • introduces and employs the freely-available statistical software, R, to explore and analyze data.

Topics include experimental design, linear regression, the analysis of two-way tables, sample size and power calculations, and a selection of the following: permutation tests, the bootstrap, survival analysis, longitudinal data analysis, nonlinear regression, and logistic regression.

Learning Objectives

Upon completion of this course, you will be able to:

  • create graphical displays of data.
  • understand basic experimental design.
  • understand basic probability.
  • implement confidence intervals and tests of hypotheses.

Course Organization

This course constitutes 23 lectures and a final exam during the 24th session. PDFs of lecture notes are available for many but not all of the lectures. Additionally, homework assignments were fortunately included with many of the lectures associated with this OpenCourseWare course. However, no video or audio files are associated. A final exam was given for this course, but it is not available through OCW.

Further Reading

The following texts are listed as required or recommended for the course:

Required:

Recommended:

No particular chapters were linked with each session. Use your best judgement in matching chapters with sessions.

    Other Requirements

    Students are also required to have access to a scientific calculator with the ability to handle logarithms, exponents, trigonometric functions, simple memory and recall, and factorials. Additionally, the freely available statistical software, R, will be necessary. Consult the following URLs and files associated with them:

    This leads to the R Project for Statistical Computing, where you can download and learn about R: R for Windows

    This PDF contains class notes on how to use R effectively: PDFFile ICONNotes on Using R

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