# Statistics for Brain and Cognitive Science

A Gaussian (normal) probability distribution is often used for statistical analysis in brain and cognitive sciences. (Figure by Professor Emery Brown)

### Instructor(s)

Emery N. Brown

### MIT Course Number

9.07

### As Taught In

Fall 2016

### Level

Undergraduate

###

Some Description
Instructor(s) | Prof.
As Taught In | Spring 2002
Course Number | 2.24
Level | Undergraduate/Graduate
Features | Lecture Notes, Student Work

## Course Description

## Course Features

- Lecture notes
- Assignments: problem sets (no solutions)

## Course Description

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.

## Other Versions

## Other OCW Versions

Archived versions:

- 9.07 Statistical Methods in Brain and Cognitive Science (Spring 2004)

## Related Content

## Course Collections

See related courses in the following collections:

### Find Courses by Topic

- Probability and Statistics
- Cognitive Science

Emery Brown. 9.07 Statistics for Brain and Cognitive Science. Fall 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.

For more information about using these materials and the Creative Commons license, see our Terms of Use.