Data Interpretation - part I

Instructor: Rebecka Jornsten , Email: rebecka@stat.rutgers.EDU

Lecture: Wednesday 6.40-9.30pm, Hill 552

Office Hours: Wed 4.30-5.30 451 Hill Center

About the course:

Texts

Required: T. Hastie, R. Tibshirani & J. Friedman (2001) The Elements of Statistical Learning. Springer.

Suggested: J. Maindonald and J. Braun Data analysis and graphics using R, Cambridge series in statistical and probabilistic mathematics (2003)

Suggested: Rice, J.A. (1995) Mathematical Statistics and Data Analysis. Second Edition. Belmont, Duxbury.

Suggested: McCullagh, P. and Nelder, J.A. (1989) Generalized Linear Models. Second Edition. London, Chapman & Hall.

Suggested: Seber, G.A.F. and Wild, C.J. (1989) Nonlinear Regression. New York, Wiley.

Labs

Assigned in lecture, due 1-2 weeks after. Only typed lab reports will be accepted.

Project

The project proposal is due Wednesday April 2 . Write a short (1-3 pages) description of the data/the source/the paper, and the analysis/discussion you anticipate completing.

Computing

The labs are an integral part of the course. They will require you to analyze data using the statistical software R, and to write up your findings in short reports. In addition, a final project counts for 25 % of your course grade. You can use other programming languages if you prefer, but the labs will be written for R and I will not assign alternative labs.

Exams

There will be a final, and a project presentation.

Grading

Your letter grade for the course will be based on the overall score you get from labs, the exam and the project.

Labs	20%
Final	40%
Project	40%

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Links:

Course outline

Project

Lab Assignments

Notes

How to write a report

Back to : Rebecka Jornsten

01/17/08