ST 380: Probability and Statistics for the Physical Sciences
Fall Session, 2009


ST 380 Probability and Statistics for the Physical Sciences


MWF from 9:10 a.m. to 10:00 a.m.


323 Mann Hall

. .


Sujit Ghosh





5216 Sas Hall

Office hours:

Wednesday, 2:00-3:00 or by appointment

. .


Liwei Wang





Statistics Tutorial Center, 1101 Sas Hall

Office hours:

Mon: 3:00-4:00 and Thu: 1:30-2:30

Class links: Course organization| Ask a question | Private tutors

Course prequisite: MA 241

Required text: Lavine M. (May, 2009). Introduction to Statistical Thought, (available free online)

Statistical resources: Online Statistics: An Interactive Multimedia Course of Study

Homework: Homework will normally be assigned weekly (as indicated on the homework page) at the end of class each Friday. Homework solution will normally be discussed in class on Mondays. The final homework average will be computed after dropping the two lowest grades.

Project: Project abstract is due after the midterm exam (as indicated on the project page). Students will be required to work in groups of size at most four members in a group. As a part of the project students will be required to obtain a set of real data, perform exploratory data analysis and make conclusions using statistical techniques learned in this course (e.g., the use of softwares like R is highly encouraged). Students are required to present the project in a mini seminar style within a time slot of no more than 15 minutes during the final week of classes. Each member of a group will receive equal credit.

Examinations: The midterm examination will be closed book and closed notes held in class. However students will be permitted to bring one 8 1/2 by 11 inch sheet of notes (may use both sides) to the midterm exam. Students may bring calculators to the midterm exam, in addition to pen/pencil and scratch papers. There will be no in-class final exam. The project will be used as the final exam.

Exam schedule:
Midterm exam
Monday, October 12
Time: 4:00-6:00, 450 Riddick Hall
Syllabus:Chap.1 of the text
Final Project
Final week

Asking questions: If you have questions about lectures, homework assignments, exams, procedures or any other aspect of the course please log onto, follow the links to "ST" and "ST380" and click on "Message Board". Then click on "Post New Topic", enter your question in the Message box, and click on "Submit Message". You will receive a response from me or another student. Everyone in the class will be able see your question and the response.

Anonymous mail: If you wish to send me an anonymous suggestion or reminder, send email to or just click here. The system will remove mail headers, but you must remember to remove your signature and other identifying information.

Grading system: Final grade will be based on following rule:

Final Semester Score = (2.5xHW + 3.5xM + 4xF)/10

where HW is the homework average (out of 100) after dropping the two lowest scores and M, and F are the scores (out of 100) on the midterm exam, and final project, respectively. Grades will be assigned on the +/- scale.

Auditing:Auditors are expected to attend class regularly and submit homework on the same schedule as the other students. The final grade for auditors (AU or NR) will be based on their final homework average. A homework score of 75 or better is required for an AU.

Policy on incomplete grades and late assignments: Unexcused late homework will not be accepted. HWs are to submitted by 5:00PM on the due date. If the midterm exam is missed, no substitute exam will be given. However, if there is a legitimate reason (as determined by the instructor) for missing the midterm exam, the final exam/project will count 65% and the remaining 10% will be lost from the final semester score. Final exam/project can not be missed.

Policy on academic integrity: The University policy on academic integrity can be found in Code of Student Conduct Policy (POL11.35.1). For a more though elaboration see the NCSU Office of Student Conduct website. For this course group work on homework and project is encouraged. However copying someone else's work and calling them your own is plagiarism, so the work you turn in should be your own. Students are required to sign the Honor Pledge while submitting the assignments.

Students with disabilities: Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, students must register with Disability Services for Students at 1900 Student Health Center, Campus Box 7509, 515-7653. For more information on NC State's policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation (REG02.20.1)

Class attendance policy: Reasonable efforts must be made to attend all lectures. However, if there is a legitimate reason (as determined by the instructor) for missing a lecture, the student must notify the instructor by a phone/e-mail.
If you are ill with symptoms of H1N1 influenza (i.e. fever over 100, sore throat, cough, stuffy or runny nose, fatigue, headache, body aches, vomiting and diarrhea) please do not come to class. Instead, immediately contact your medical provider or Student Health Services (515-7107) for advice or to arrange an appointment.

Reference material (Have requested these be on reserve at DH Hill Library):

Bolstad, William M. (2004). Introduction to Bayesian Statistics. John Wiley & Sons Inc.

DeGroot, Morris, H. and Schervish, Mark, J. (2001). Probability and Statistics, 3rd Edition. Addison Wesley.

Course objectives:

A prime objective of the ST380 course is to present probability models and statistics with emphasis on Monte Carlo simulation and graphical display of data using statistical software (e.g., R). Statistical methods include point and interval estimation of population parameters and curve and surface fitting (regression analysis).

In ST380 we develop the probabilistic language and computational tools of statistics useful for physical sciences. The course describes probabilistic models for specifying statistical distributions based on exploratory data analysis, summarizing statistical inference based on estimation and hypothesis testing, evaluating predictive distributions, and making scientific conclusions using statistical language.

Students taking the course will have completed one semester of calculus and had some exposure to basic probability and statistics. MA 241 is a prerequisite for this course.

Syllabus: In ST380 we shall cover most, but not all of the materials in chapters 1 through 6 of Lavine's online book. If time permits, parts of chapter 7 will also be covered.
  1. Probability: Basic notions; probability distributions; moments; parametric families; joint, marginal and conditional distributions; association and dependence; simulation using R
  2. Modes of inference: exploratory data analysis; likelihood function; estimation; prediction; hypothesis testing
  3. Regression: linear models; generalized linear models; predictions using regression
  4. Bayesian statistics: prior distributions; posterior distributions; posterior predictive distribution; Monte Carlo methods
(roughly four weeks will be devopted to each of the above topics)

Last updated on: August 17, 2009