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

(22 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
2
  • Lecture 12: Testing Goodness of Fit (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
3
  • Lecture 13: Regression (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
4
  • Lecture 14: Regression (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
5
  • Lecture 15: Regression (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
6
  • Lecture 17: Bayesian Statistics (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
7
  • Lecture 18: Bayesian Statistics (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
8
  • Lecture 19: Principal Component Analysis (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
9
  • Lecture 1: Introduction to Statistics (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
10
  • Lecture 20: Principal Component Analysis (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
11
  • Lecture 21: Generalized Linear Models (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
12
  • Lecture 22: Generalized Linear Models (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
13
  • Lecture 23: Generalized Linear Models (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
14
  • Lecture 24: Generalized Linear Models (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
15
  • Lecture 2: Introduction to Statistics (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
16
  • Lecture 3: Parametric Inference (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
17
  • Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
18
  • Lecture 5: Maximum Likelihood Estimation (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
19
  • Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
20
  • Lecture 7: Parametric Hypothesis Testing (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
21
  • Lecture 8: Parametric Hypothesis Testing (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences
22
  • Lecture 9: Parametric Hypothesis Testing (cont.) (M-I-T)
Statistics for Applications (Fall 2016) (M-I-T) MIT Prof. Philippe Rigollet Basic and Health Sciences

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