Course Website Locator: bio231-01

Harvard School of Public Health

The following course websites match your request:

Spring 2010

Cross-listed as FAS as BIST231
Dr.Y. Li
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One 1.5-hour lab each week.

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.
Course Note: BIO 230 or signature of instructor required; lab or section time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2009

Cross-listed as FAS as BIST231
Dr.Y. Li
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One 1.5-hour lab each week.

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.
Course Note: BIO 230 or signature of instructor required; lab or section time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2008

Cross-listed as FAS as BIST231
Dr.Y. Li
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One 1.5-hour lab each week.

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.
Course Note: BIO 230 or signature of instructor required; lab or section time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course.

Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

Spring 2007

Cross-listed as FAS as BIST231
Dr. V. DeGruttola
5 credits
Lectures, laboratories. Two 2-hour sessions each week. One 1.5-hour lab each week.

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.
Course Note: BIO 230 or signature of instructor required; lab or section time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course.
Course evaluations are an important method for feedback on the quality of course offerings. The submission of a course evaluation is a requirement for this course. Your grade for the course will be made available only after you have submitted responses to at least the first three questions of the on-line evaluation for this course.

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