MIT OpenCourseWare

Browse materialsMIT 9.07 · Fall 2016

Statistics for Brain and Cognitive Science

Subjects: Science · Statistics

Art: Santiago Ramón y Cajal (1852—1934); uploaded to…, Fig retine, 1911 · Public domain

Instructor: Emery N Brown
Term: Fall 2016
Level: Undergraduate
What's inside: 1 lecture note · 1 reading · 38 other files · 2 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 9.07 Statistics for Brain and Cognitive Science, Fall 2016, Emery N Brown — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

This course’s cited books, in our reading lists: Probability and Statistics

How this course was organized

What's here, and what isn't

MIT's own materials here did not support a verified, course-wide order, so they are grouped by section instead.

Not included here

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Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Readings

Lecture Notes

22 files

Assignments

8 files

Other materials from this course

5 files

2 images

MIT OpenCourseWare, Emery N Brown, 9.07 Statistics for Brain and Cognitive Science, Fall 2016.

License: CC BY-NC-SA 4.0 · Edition: Fall 2016 · Open the original course on OCW ↗

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Statistics for Brain and Cognitive Science

Before you start

What's here, and what isn't

Not included here

Materials by section

Readings

Lecture Notes

Assignments

Other materials from this course

Lecture 1: Axioms of Probability Theory, Counting Rules Conditional Probability, Bayes’ Rule and Independence

Lecture 2: Discrete Probability Models

Lecture 3: Continuous Probability Models I & II

Lecture 4: Transformations of Random Variables Joint Distributions and Independent Random Variables

Lecture 5: Conditional Distributions and Functions of Jointly Distributed Random Variables I & II

Lecture 6: Expectations, Variances, Covariances and Correlation Moment Generating Functions I & II

Lecture 7: The Law of Large Numbers and the Central Limit Theorem

Lecture 8: Method-of-Moments Estimation Likelihood Theory I

Lecture 9: Likelihood Theory II

Lecture 10: Propagation of Error Bootstrap and Monte Carlo Methods

Lecture 11: Hypothesis Testing I & II

Lecture 12: Simple Regression Model I, II & III

Lecture 13: Analysis of Variance