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18.650 Statistics for Applications (Fall 2016, MIT OCW).
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English
English [CC]
FREE
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Description
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)
Course content
- Lecture 01 – Introduction to Statistics Unlimited
- Lecture 02 – Introduction to Statistics (cont.) Unlimited
- Lecture 03 – Parametric Inference Unlimited
- Lecture 04 – Parametric Inference (cont.) and Maximum Likelihood Estimation Unlimited
- Lecture 05 – Maximum Likelihood Estimation (cont.) Unlimited
- Lecture 06 – Maximum Likelihood Estimation (cont.) and the Method of Moments Unlimited
- Lecture 07 – Parametric Hypothesis Testing Unlimited
- Lecture 08 – Parametric Hypothesis Testing (cont.) Unlimited
- Lecture 09 – Parametric Hypothesis Testing (cont.) Unlimited
- Lecture 11 – Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit Unlimited
- Lecture 12 – Testing Goodness of Fit (cont.) Unlimited
- Lecture 13 – Regression Unlimited
- Lecture 14 – Regression (cont.) Unlimited
- Lecture 15 – Regression (cont.) Unlimited
- Lecture 17 – Bayesian Statistics Unlimited
- Lecture 18 – Bayesian Statistics (cont.) Unlimited
- Lecture 19 – Principal Component Analysis Unlimited
- Lecture 20 – Principal Component Analysis (cont.) Unlimited
- Lecture 21 – Generalized Linear Models Unlimited
- Lecture 22 – Generalized Linear Models (cont.) Unlimited
- Lecture 23 – Generalized Linear Models (cont.) Unlimited
- Lecture 24 – Generalized Linear Models (cont.) Unlimited
Instructor
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