Course or series
Statistics for Applications
MIT OpenCourseWare
Lecture 1: Introduction to Statistics
“Lecture 1: Introduction to Statistics” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 2: Introduction to Statistics (cont.)
“Lecture 2: Introduction to Statistics (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 3: Parametric Inference
“Lecture 3: Parametric Inference” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation
“Lecture 4: Parametric Inference (cont.) and Maximum Likelihood Estimation” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 5: Maximum Likelihood Estimation (cont.)
“Lecture 5: Maximum Likelihood Estimation (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments
“Lecture 6: Maximum Likelihood Estimation (cont.) and the Method of Moments” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
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Lecture 7: Parametric Hypothesis Testing
Introduces parametric hypothesis testing through a running-time example. The inspected opening distinguishes estimation from testing and shows how choices about a distribution, variance and alternative hypothesis constrain the question being answered.
MIT OpenCourseWare
Lecture 8: Parametric Hypothesis Testing (cont.)
“Lecture 8: Parametric Hypothesis Testing (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 9: Parametric Hypothesis Testing (cont.)
“Lecture 9: Parametric Hypothesis Testing (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
“Lecture 11: Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 12: Testing Goodness of Fit (cont.)
“Lecture 12: Testing Goodness of Fit (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 13: Regression
“Lecture 13: Regression” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 14: Regression (cont.)
“Lecture 14: Regression (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 15: Regression (cont.)
“Lecture 15: Regression (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 17: Bayesian Statistics
“Lecture 17: Bayesian Statistics” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 18: Bayesian Statistics (cont.)
“Lecture 18: Bayesian Statistics (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 19: Principal Component Analysis
“Lecture 19: Principal Component Analysis” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 20: Principal Component Analysis (cont.)
“Lecture 20: Principal Component Analysis (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 21: Generalized Linear Models
“Lecture 21: Generalized Linear Models” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 22: Generalized Linear Models (cont.)
“Lecture 22: Generalized Linear Models (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 23: Generalized Linear Models (cont.)
“Lecture 23: Generalized Linear Models (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.
MIT OpenCourseWare
Lecture 24: Generalized Linear Models (cont.)
“Lecture 24: Generalized Linear Models (cont.)” is listed in Statistics for Applications. Its place in the course provides the learning context; an individual account of the lecture's content has not yet been checked.