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Home » Basic and Health Sciences » Mathematics » Statistics for Applications (Fall 2016) (M-I-T) » Lecture 21: Generalized Linear Models (M-I-T)

Lecture 21: Generalized Linear Models (M-I-T)

Course:Statistics for Applications (Fall 2016) (M-I-T)
Discipline:Basic and Health Sciences
Institute:MIT
Instructor(s): Prof. Philippe Rigollet
Level:Graduate

Statistics for Applications (Fall 2016) (M-I-T)

Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit (M-I-T)
Lecture 12: Testing Goodness of Fit (cont.) (M-I-T)
Lecture 13: Regression (M-I-T)
Lecture 14: Regression (cont.) (M-I-T)
Lecture 15: Regression (cont.) (M-I-T)
Lecture 17: Bayesian Statistics (M-I-T)
Lecture 18: Bayesian Statistics (cont.) (M-I-T)
Lecture 19: Principal Component Analysis (M-I-T)
Lecture 1: Introduction to Statistics (M-I-T)
Lecture 20: Principal Component Analysis (cont.) (M-I-T)
Lecture 21: Generalized Linear Models (M-I-T)
Lecture 22: Generalized Linear Models (cont.) (M-I-T)
Lecture 23: Generalized Linear Models (cont.) (M-I-T)
Lecture 24: Generalized Linear Models (cont.) (M-I-T)
Lecture 2: Introduction to Statistics (cont.) (M-I-T)
Lecture 3: Parametric Inference (M-I-T)
Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation (M-I-T)
Lecture 5: Maximum Likelihood Estimation (cont.) (M-I-T)
Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments (M-I-T)
Lecture 7: Parametric Hypothesis Testing (M-I-T)

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