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Lecture

GLM and Statistical Testing Illustrated

In course

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system ...

Description

This lecture covers the General Linear Model (GLM) and statistical testing in the context of neural signals and signal processing. It explains the theoretical model, contrasts, Gauss-Markov assumptions, fitted models, multiple comparisons using Bonferroni, Gaussian random field, False Discovery Rate (FDR), and cluster size analysis. The instructor also discusses the importance of F-tests, region-of-interest analysis, statistical inference, and group-level analysis. Additionally, it delves into functional connectivity, resting-state fMRI, multivariate methods like Principal Component Analysis (PCA) and Partial Least Squares Correlation, and the analysis of variance (ANOVA) models.

Instructors (2)

Silvestro Micera

Dimitri Nestor Alice Van De Ville

Official source

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing

Related concepts (32)

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Digital signal processing

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F-test

An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled. Exact "F-tests" mainly arise when the models have been fitted to the data using least squares. The name was coined by George W. Snedecor, in honour of Ronald Fisher. Fisher initially developed the statistic as the variance ratio in the 1920s.

Test statistic

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