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18.650 Statistics for Applications

18.650 Statistics for Applications (Fall 2016, MIT OCW). Instructor: Professor Philippe Rigollet. This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods. (from ocw.mit.edu)

Lecture 20 - Principal Component Analysis (cont.)

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Lecture 01 - Introduction to Statistics

Lecture 02 - Introduction to Statistics (cont.)

Lecture 03 - Parametric Inference

Lecture 04 - Parametric Inference (cont.) and Maximum Likelihood Estimation

Lecture 05 - Maximum Likelihood Estimation (cont.)

Lecture 06 - Maximum Likelihood Estimation (cont.) and the Method of Moments

Lecture 07 - Parametric Hypothesis Testing

Lecture 08 - Parametric Hypothesis Testing (cont.)

Lecture 09 - Parametric Hypothesis Testing (cont.)

Lecture 10

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 16

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.)