MIT OpenCourseWare

Browse materialsMIT 18.650 · Fall 2016

Statistics for Applications

Subjects: Mathematics · Statistics

Art: Johannes Kepler, Konjukce Saturnu a Jupiteru Originál, 1596 · Public domain

Instructor: Prof. Philippe Rigollet
Term: Fall 2016
Level: Undergraduate
What's inside: 22 videos · 11 problem sets · 34 other files · 3 web pages
Materials open: Videos here · files on MIT OCW

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About this course

Source: MIT OpenCourseWare 18.650 Statistics for Applications, Fall 2016, Prof. Philippe Rigollet — 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.

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.

Materials by section

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

Lecture Slides

10 files

Assignments

11 problem sets

Other materials from this course

2 files

22 video transcripts

MIT OpenCourseWare, Prof. Philippe Rigollet, 18.650 Statistics for Applications, Fall 2016.

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

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Statistics for Applications

Before you start

What's here, and what isn't

Materials by section

Lecture Slides

Assignments

Other materials from this course

Lecture 1: Introduction to Statistics

Lecture 2: Introduction to Statistics (cont.)

Lecture 3: Parametric Inference

Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation

Lecture 5: Maximum Likelihood Estimation (cont.)

Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments

Lecture 7: Parametric Hypothesis Testing

Lecture 8: Parametric Hypothesis Testing (cont.)

Lecture 9: Parametric Hypothesis Testing (cont.)

Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit

Lecture 12: Testing Goodness of Fit (cont.)

Lecture 13: Regression

Lecture 14: Regression (cont.)

Lecture 15: Regression (cont.)

Lecture 17: Bayesian Statistics

Lecture 18: Bayesian Statistics (cont.)

Lecture 19: Principal Component Analysis

Lecture 20: Principal Component Analysis (cont.)

Lecture 21: Generalized Linear Models

Lecture 22: Generalized Linear Models (cont.)

Lecture 23: Generalized Linear Models (cont.)

Lecture 24: Generalized Linear Models (cont.)