• Instructor: Adam J Sullivan, PhD
• TAs:
  • Taylor Fortnam
  • Blain Morin
  • Fuyu Zou
• Masters Tutoring Office Hour Help:
  • Julia Mullokandova

• Main: https://php-1511-2511.github.io
• Canvas:
  • PHP 1511: https://canvas.brown.edu/courses/1077969
  • PHP 2511: https://canvas.brown.edu/courses/1077129
• Github: https://github.com/php-1511-2511
• Slack: https://php-1511-2511-spring-2019.slack.com/

• Recognize when data should be analyzed by regression
• Plan an appropriate analysis
• Bring in, Clean and Analyze data with R.
• Coherently summarize results.

• Attend all lectures and actively participate in discussion.
• Read all assigned material prior to coming to class and actively participate in class discussions. **Students in the past have found that reading the notes works, but make sure you know what you need to learn best, as we will move fast. **
• Complete and turn in all assignments on time. Solutions to homework must be clearly written with appropriate tables and figures included.
• Demonstrate an understanding on material on examinations.
• Respect each other, each others questions and each others discussion.

• Course topics will be drawn (but subject to change) from

• Linear Regression

• Linear Regression Diagnostics

• Generalized Linear Models

• Nonlinear Regression

• Robust Regression

• Survey Analysis

• Applied Regression Analysis and Generalized Linear Models by John Fox Jr.
• Amazon. We follow this book for content and pacing.

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Main Textbook

Regression Methods in Biostatistics by Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch Amazon. We follow this book for content and pacing.

• An Introduction to Statistical Learning: with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
• We will use this as a reference to some methods as well as a resource for data and R code.

• This is available freely available as an eBook Get it
  • If you prefer a paperback version you may buy it at cost from Springer (see links from library site) or purchase a hardback version at the through Amazon.
• For additional information check out Videos for the ISL book
  

• Other resources for reference books, statistical computing using R, etc are provided on the Resource tab

-Students will be evaluated based on:

• This course will move very fast and it is crucial to success in the course that students attend and participate.
• Unexcused absences will result in a loss of percentage points.

• Assignments will be given out to students. Assignments will require data handling, data cleaning and interpretation of the results.
  
• It is expected that all assignments are completed on time. No late assignments will be accepted.
• Students will also be graded on the conciseness and quality of work.
• Turning in many pages of just computer code and output will affect the grade in a negative fashion.
• All work must be done in R. No other Stat software is accepted.

• An in class exam will be given.
• Students will be expected to interpret and analyze regression models.
• Students will also be expected to understand conceptual ideas.

• An in class exam will be given.
• Students will be expected to interpret and analyze regression models.
• Students will also be expected to understand conceptual ideas.

Students will spend the semester working on a Reproducible Research Project. This project will require:

1. Asking a relevant public Health question for those in 2511 and a relevant scientific question for those in 1511.
2. Identify available data to answer this question.
3. Model the question with appropriate statistical models.
4. Write up a report with appropriate tables, graphs and results.

• The project will consist of individual as well as group content.
• For the individual content you will complete the 4 requirements.
• For the group component. You will work in small groups to evaluate each others work.
• This will require:
  1. Constructive Criticism of Group members Projects based on Course Content.
  2. In depth review of group members work.
• You will be graded on both individual and group aspects.
• It is important to learn not only how to ask a public health question and answer that question with a study or data but equally important to review others work and arguments.

• We will use R as a programming language for data analysis and use existing packages written in R to support the course.
• You should have access to a laptop or desktop capable of running R or RStudio.
  
• We will also provide access to a dedicated server running RStudio Pro for all students that will have a unified environment.
  
• See the Resources page for books and other resources for learning R.

• We will be using Slack for class discussion.
• The system is highly catered to getting you help fast and efficiently from classmates, the TAs, and myself.
• Rather than emailing questions to the teaching staff, I encourage you to post your questions on Slack (peer answers earn participation points!).
• Any non-personal questions related to the material covered in class, problem sets, labs, projects, etc. should be posted on slack.
• Before posting a new question please make sure to check if your question has already been answered.
• The TAs and myself will be answering questions on the forum daily and all students are expected to answer questions as well. Please use informative titles for your posts.

• This does not mean that the professor or staff will be available 24/7.
• Do not expect a fast answer from the staff after 5pm on a week night.
• Do not expect a fast answer from the staff on the weekends.

• Brown University is committed to full inclusion of all students.
• Students who, by nature of a documented disability, require academic accommodations should contact the professor during office hours.
• Students may also speak with Student and Employee Accessibility Services at 401-863-9588 to discuss the process for requesting accommodations.

• This course is designed to support an inclusive learning environment where diverse perspectives are recognized, respected and seen as a source of strength.
• It is our intent to provide materials and activities that are respectful of various levels of diversity: mathematical background, previous computing skills, gender, sexuality, disability, age, socioeconomic status, ethnicity, race, and culture.
  
• It is the nature of data analysis that we will touch on sensitive subjects.
• My goal will be to approach these with as much respect and kindness as I can.
• Please come see me if you ever feel hurt or upset about class discussions.

• Brown University welcomes students from around the world, and the unique perspectives international students bring enrich the campus community. * To empower students whose first language is not English, an array of ELL support is available on campus including language and culture workshops and individual appointments.
• For more information about English Language Learning at Brown, contact the ELL Specialists at ellwriting@brown.edu.