Course summary: Introduces the basic concepts and methods of statistics with applications in the experimental biological sciences. Demonstrates methods of exploring, organizing, and presenting data, and 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. 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. Introduces and employs the freely-available statistical software, R, to explore and analyze data.
First term objectives: Graphical displays of data, basic experimental design, probabilities and distributions, confidence intervals and tests of hypotheses, tests for goodness of fit, contingency tables [ tentative syllabus ]. Second term objectives: Analysis of variance, multiple comparisons, linear and non-linear regression, experimental design, special topics. If you plan on analyzing data for your own research, I highly recommend that you sign up for both terms. Several of the most common statistical methods (such as linear regression and analysis of variance) will be covered in the second term [ tentative syllabus ].
Books: The text for this course is Sokal and Rohlf. Any other statistical text that covers the following topics will also be useful: Random variables and distributions, confidence intervals, hypothesis testing, sample size and power calculations, maximum likelihood estimation, goodness of fit, contingency tables, analysis of variance, linear regression. To learn the statistical environemnt R used in this course, I recommend the book by Peter Dalgaard, and will also list the relevant chapters for recommended reading. However, there is plenty of free material available on the web, some of it listed in the R resources (including most of the material from John Verzani's book). The Cartoon Guide to Statistics by Larry Gonick is cheap, and a really fun read.
Useful links: [ General course info | R resources | Practice problems | JHSPH Biostatistics Center | Academic calendar ]
Exams 140.615 Wednesday March 14 (final). 140.616 Friday April 13 (midterm); Monday May 14 (final).
Notes / Reading / Code / Homework / Lab / Practice
| Date | N | R | C | H | L | P | Topic | |
| February | 13 | |
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Confidence intervals | |||
| February | 10 | Sampling distributions | ||||||
| February | 8 | |
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Sampling distributions | |
| February | 6 | |
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Multiple random variables (handout) | |||
| February | 3 | Random variables and distributions | ||||||
| February | 1 | |
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Random variables and distributions (handout and code) | |
| January | 30 | |
Experimental design | |||||
| January | 27 | |
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Statistics and probability (handout) | |
| January | 25 | Summarizing and presenting data | ||||||
| January | 23 | |
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Introduction; Summarizing and presenting data | ||||