Course Website Locator: bio503-01

Harvard School of Public Health

The following course websites match your request:

Winter 2010

Dr. A. Culhane, Dr. S. Bentink, Dr. J. Quankenbush
1.25 credits
Seminars. Five 3-hour sessions during WinterSession

This course is an introduction to R, a powerful and flexible statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course will introduce students to the basics of using R for statistical programming, computation, graphics, and modeling. We will start with a basic introduction to the R language, reading and writing data, and graphics. We then discuss writing functions in R and tips on programming in R. Finally, the latter part of the course will focus on using R to fit some important types of statistical models, including linear regression, generalized linear models, generalized additive models, and mixed effects models.

Our goal is to get students up and running with R such that they can use R in their research and are in a good position to expand their knowledge of R on their own. Basic knowledge of statistics at the level of a basic understanding of linear regression is required.
Course note: Pass/Fail or audit grading option only.

Winter 2009

J. Marr, Dr. C. Paciorek (S)
1.25 credits
Seminars. Five 3-hour sessions during WinterSession

This course is an introduction to R, a powerful and flexible statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course will introduce students to the basics of using R for statistical programming, computation, graphics, and modeling. We will start with a basic introduction to the R language, reading and writing data, and graphics. We then discuss writing functions in R and tips on programming in R. Finally, the latter part of the course will focus on using R to fit some important types of statistical models, including linear regression, generalized linear models, generalized additive models, and mixed effects models.

Our goal is to get students up and running with R such that they can use R in their research and are in a good position to expand their knowledge of R on their own. Basic knowledge of statistics at the level of a basic understanding of linear regression is required.
Course note: Pass/Fail or audit grading option only.

Winter 2008

Dr. A. Culhane
1.25 credits
Seminars. Five 3-hour sessions during WinterSession

This course is an introduction to R, a powerful and flexible statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course will introduce students to the basics of using R for statistical programming, computation, graphics, and modeling. We will start with a basic introduction to the R language, reading and writing data, and graphics. We then discuss writing functions in R and tips on programming in R. Finally, the latter part of the course will focus on using R to fit some important types of statistical models, including linear regression, generalized linear models, generalized additive models, and mixed effects models.

Our goal is to get students up and running with R such that they can use R in their research and are in a good position to expand their knowledge of R on their own. Basic knowledge of statistics at the level of a basic understanding of linear regression is required.
Course note: Pass/Fail or audit grading option only. (10.06)

Winter 2007

Dr. S. Guha (P), Dr. C. Paciorek (S)
1.25 credits
Seminars. Five 3-hour sessions during WinterSession

This course is an introduction to R, a powerful and flexible statistical language and environment that also provides more flexible graphics capabilities than other popular statistical packages. The course will introduce students to the basics of using R for statistical programming, computation, graphics, and modeling. We will start with a basic introduction to the R language, reading and writing data, and graphics. We then discuss writing functions in R and tips on programming in R. Finally, the latter part of the course will focus on using R to fit some important types of statistical models, including linear regression, generalized linear models, generalized additive models, and mixed effects models.

Our goal is to get students up and running with R such that they can use R in their research and are in a good position to expand their knowledge of R on their own. Basic knowledge of statistics at the level of a basic understanding of linear regression is required.
Course note: Pass/Fail or audit grading option only. (10.06)

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