Statistics and Visualization for Data Analysis and Inference

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Analyzing global data. (Figure by MIT OpenCourseWare.)


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A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis distribution through permutation, useful parametric distributions, the generalized linear model, and model-based analyses more generally. Familiarity with MATLAB®, Octave, or R will be useful, prior experience with statistics will be helpful but is not essential. This course is intended to be a ground-up sketch of a coherent, alternative perspective to the "null-hypothesis significance testing" method for behavioral research (but don't worry if you don't know what this means).

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Ed Vul, and Mike Frank. RES.9-0002 Statistics and Visualization for Data Analysis and Inference, January IAP 2009. (Massachusetts Institute of Technology: MIT OpenCourseWare), (Accessed). License: Creative Commons BY-NC-SA

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