36-746 Statistical Methods for Neuroscience and Psychology
12 units, Carnegie Mellon University
This course provides a survey of basic statistical methods, emphasizing motivation from underlying principles and interpretation in the context of neuroscience and psychology. Though 36746 assumes only passing familiarity with schoollevel statistics, it moves faster than typical universitylevel first courses. Vectors and matrices will be used frequently, as will basic calculus. Topics include Probability, Random Variables, and Important Distributions binomial, Poisson, and normal distributions the Law of Large Numbers and the Central Limit Theorem Estimation and Uncertainty standard errors and confidence intervals the bootstrap Principles of Estimation mean squared error maximum likelihood Models, Hypotheses, and Statistical Significance goodnessoffit, pvalues power General methods for testing hypotheses permutation, bootstrap, and likelihood ratio tests Linear Regression simple linear regression and multiple linear regression Analysis of Variance oneway and twoway designs multiple comparisons Generalized Linear and Nonlinear Regression logistic and Poisson regression generalized linear models and Nonparametric regression smoothing scatterplots smoothing histograms.
Prerequisites: none listed
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