Math 476: Statistics and Math 563: Mathematical Statistics
Syllabus
Schedule & Contact  
The class meets 3:15pm 4:30pm TR at Siegel Hall 203.


Instructor:  Sonja Petrović Office: E1, 111a Office hours: 12noon  1pm Tuesdays and Thursdays, walkins, appointment. econtact: Sonja.Petrovic@iit.edu 

Graduate assistant: 
The graduate teaching assistant for this course is Su Li Office hours: 3:10 pm. to 6:10 pm. on Fridays. (Mathematics Graduate Teaching Assistants are avaialble in the shared Office: E1 129.) econtact: sli69_AT_hawk.iit_DOT_edu 

Course material  
Primary textbook: Wackerly, Mendhall, Scheaffer. Mathematical Statistics with Applications . Cengage, 7th edition. 9780495110811
Complementary text for math 563: Shao (2003). Mathematical Statistics. Springer. Software: You can use the JMP software which is available to students in IIT labs. From time to time, I will use R to show examples; R is free to download at http://cran.rproject.org/, and it is a good idea for graduate students to be familiar with R as well. 

Required coursework  
Attendance and participation  Regular class attendance and class participation is important and expected. You are expected to come to lectures, participate in discussions, read the textbook (including examples not covered in class), and ask questions. Students are responsible for all announcements and supplements given within any lecture. All cell phones must be turned off before entering the classroom. Technology use policy will be discussed on the first day of class. 

Midterm 
There will be one or two inclass exams during the semester. Exam dates and topics covered will be announced on the course homepage and in class. Makeup exams will be given only in case of a documented emergency. 

Final exam  A comprehensive ﬁnal exam will be given during the IIT ﬁnal exam week.  
Homework  Homework problems will be posted on the course website, typically on a Tuesday, usually at least one week before the due date. Due dates are announced in class and/or course homepage. In addition, there might be reading homework assigned during lectures.
Important note: Solutions to homework problems and exams must be written clearly, legibly, and concisely and will be graded on mathematical correctness and presentation. Points will be deducted for sloppiness, incoherent or insufﬁcient explanation, or for lack of supporting rationale. Include enough detail in your solutions so that your explanation is convincing to someone who hasn’t thought about the problem before. The proofs/arguments should be presented so that your classmates could read them and follow the logic (stepbystep). 

**Tentative** grading policy  
Grade components: Homework, quizzes and participation (and, tentatively, a project): 35 % Two inclass exams: 40% Final exam: 25 % 

Tentative grading distribution: A: 90100; B: 8089; C: 7079; D: 6069; E: 059. 
Course overview
Course decsription  
Estimation theory; hypothesis tests; confidence intervals; goodnessoffit tests; correlation and linear regression; analysis of variance; nonparametric methods. Theory of sampling distributions; interval and point estimation, sufficient statistics, order statistics. For graduate students, this course has a twofold aim: (1) develop proficiency in the concepts listed above, and (2) develop good habits of understanding, communicating, and writing statistical analyses. 

Department course syllabus  
For 476, click here. For 563, click here.  
Prerequisites  
MATH 475 Probability or MATH 540 Probability. 