Mike Frank and Ed Vul
\nStatistics and Visualization for Data Analysis and Inference<\/p>\n
Mon Jan 26 thru Fri Jan 30, 1:00 - 3:00PM Description:<\/p>\n A whirl-wind tour of the statistics used in behavioral science Course Outline:<\/p>\n Visualization. Creating a visualization to understand experimental Permutation. Understanding what would have happened \u0093by chance\u0094 Distribution. Understanding the spread of data. Inferring parametric Models of Data 1: The Linear Model. What is a \u0093model of data.\u0094 Basic Models of Data 2: Bayesian Models. Constructing and testing more Mike Frank and Ed Vul Statistics and Visualization for Data Analysis and Inference Mon Jan 26 thru Fri Jan 30, 1:00 - 3:00PM Room 46-3189 M-R 1\/26 \u0096 1\/29 and 46-3310 for F 1\/30 Description: A whirl-wind tour of the statistics used in behavioral science research, covering topics including: data visualization, building your own null-hypothesis […]<\/p>\n","protected":false},"author":38,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5],"tags":[],"class_list":["post-434","post","type-post","status-publish","format-standard","hentry","category-classes"],"_links":{"self":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/434","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/users\/38"}],"replies":[{"embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/comments?post=434"}],"version-history":[{"count":1,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/434\/revisions"}],"predecessor-version":[{"id":435,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/posts\/434\/revisions\/435"}],"wp:attachment":[{"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/media?parent=434"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/categories?post=434"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ling-phil.mit.edu\/whamit\/wp-json\/wp\/v2\/tags?post=434"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nRoom 46-3189 M-R 1\/26 \u0096 1\/29 and 46-3310 for F 1\/30
\n
\nresearch, covering topics including: data visualization, building your
\nown null-hypothesis distribution through permutation, useful
\nparametric distributions, the generalized linear model, and model-
\nbased analyses more generally. Familiarity with Matlab, Octave, or R
\nwill be useful, prior experience with statistics will be helpful but
\nis not essential. This course is intended to be a ground-up sketch of
\na coherent, alternative perspective to the “null-hypothesis
\nsignificance testing” method for behavioral research (but don’t worry
\nif you don’t know what this means).<\/p>\n
\nresults. Simple univariate displays. Conventional multivariate
\ndisplays. The repertoire of visual variables. Introduction of examples
\nto be used throughout the course: simple behavioral experiments,
\ncomplex behavioral experiments, and eye-tracking.<\/p>\n
\nthrough non-parametric tests, confidence bounds, and measures of
\neffect size. Discussion of null-hypothesis significance testing and
\nits limitations.<\/p>\n
\nforms (Binomial, Gaussian, Poisson, etc.) as a convenient way of
\ndescribing the structure of data. Effect size and Bayesian derivation
\nof tests for parametric distributions, inc. binomial test, t-test,
\nCohen\u0092s d, etc.<\/p>\n
\nassumptions of the linear model. The standard and generalized linear
\nmodel and relationship to ANOVA. Bayesian derivation of the LM. Link
\nfunctions and logistic regression. Effect size in a linear model.
\nIntroduction to multilevel models.<\/p>\n
\ncomplex models of data. Bayesian models as a tool for creating models
\nwith complex task assumptions. Brief introduction to basic techniques
\nfor Bayesian inference.<\/p>\n","protected":false},"excerpt":{"rendered":"