Statistics for Brain and Cognitive Science
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.
Fall 2016
Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.
Readings
- Dekking, F. M., C. Kraaikamp, et al. A Modern Introduction to Probability and Statistics: Understanding Why and How (Springer Texts in Statistics) . Springer, 2007. ISBN: 9781852338961.
- DeGroot, Morris H., Mark J. Schervish. Probability and Statistics . 3rd ed. Addison Wesley, 2002. ISBN: 9780201524888
- Rice, John A. Mathematical Statistics and Data Analysis . 3rd ed. Duxbury Press, 2006. ISBN: 9780534399429.
- Rosner, Bernard. Fundamentals of Biostatistics . 6th ed. Duxbury Press, 2005. ISBN: 9780534418205.
- Wallisch, Pascal, Michael E. Lusignan, et al. MATLAB ® for Neuroscientists: An Introduction to Scientific Computing in MATLAB . Academic Press, 2008. ISBN: 9780123745514. (eBook ISBN: 9780080923284)
Prerequisites & course information
Prereq: 6.100B U (Fall) Not offered regularly; consult department 4-0-8 units
Sources
- Course catalogue ↗2026–2027
- MIT OpenCourseWare ↗Fall 2016
- MIT OCW readings ↗
- MIT OCW syllabus ↗
- Historical MIT OpenCourseWare offering. Named reading entries and schedule topics are automatically extracted from the linked source pages.