Statistics for Laboratory Scientists II – JHSPH 140.616

This second of two 2006 JHSPH OpenCourseWare graduate courses:

  • 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:

  • utilize tests for goodness of fit.
  • understand contingency tables.
  • analyze for variance.
  • understand advanced concepts of multiple comparisons.
  • understand linear regression.
  • utilize advanced methods of experimental design.

Course Organization

This course constitutes 21 lectures. PDFs of lecture notes are available for many but not all of the lectures. Additionally, a few homework assignments were included for sessions 1–4 and session 6. However, no video or audio files are associated. A final project was originally assigned, assumably due after session 21.

Further Reading

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



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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