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

• Lecture 1: Introduction to Statistics (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)
• Lecture 8: Parametric Hypothesis Testing (cont.) (M-I-T)
• Lecture 9: Parametric Hypothesis Testing (cont.) (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 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)