Syllabus
The Basis for this course is to develop an understanding of statistical inference. This class will focus on a wide variety of topics; however the general principle is that of making informed decisions from data. This course will focus on various methods of parameter estimation, hypothesis testing, and interval estimation. Topics in decision theory will also be discussed. Both theoretical and computational methods will be examined in this class.
It is the mark of a truly intelligent person to be moved by statistics. -George Bernard Shaw
Grading policies, office hours, and general information
Course Objectives
- To introduce a theoretical foundation to statistical inference.
- To provide a thorough treatment of statistical inference, including foundational asymptotic results, classical treatments, as well as modern computational approaches and Bayesian methods.
- To compare the various approaches of inference through mathematical and simulated approaches.
Logistics
- Lecture Times and Location: Mon., Wed., Fri. 9:05 - 9:55 PM, in Hutcheson 209
- Evening Reviews (~bi-weekly): Thursday 6:00pm (Hutcheson 204)
- Midterm 1 Date: March 1 (Friday)
- Midterm 2 Date: April 12 (Friday)
- Instructor: Professor Scotland Leman, 401A Hutcheson Building, leman(AT)vt(DOT)edu
- Final Exam date and time: May 03, 10:05-12:05 (Friday)
- Instructor's Office Hours: Bi-weekly Review Sessions, after class, and by request
- Teaching Assistants: Mohamed Salem: msalem(AT)vt(DOT)edu
- TAs' Office Hours: By Request/Slack Questions and Answers
Prerequisites
Readings
The primary text is:
Casella and Berger . Statistical Inference Cengage Learning.
This is a very nice graduate level book on probability theory and statistical inference. This text should not limit your reading from other relevant texts.
Computing
For computing, you may use any upper level language of your choosing. For instance, C/C++, Java, Matlab, R, all make for reasonable choices. This course will not levy a large computational burden, however, be prepared for some computational exercises.
Graded work
Graded work for the course will consist of problem sets, computational problems, one or two midterms, and a final exam. Your final grade will be determined as follows:
| Final exam | 25 % |
| Midterm exam 1 | 25 % |
| Midterm exam 2 | 25 % | Homework problems/Pop quizes |
25 % |
There are no make-ups for exams, in-class or homework problems except for a medical or familial emergency or previous
approval of the instructor. See the instructor in advance of
relevant due dates to discuss possible alternatives.
Cumulative numerical averages of 90 - 100 are guaranteed at least an
A-. Cumulative numerical averages of 80 - 89 are guaranteed at
least a B-. Cumulative numerical averages of 70 - 79 are
guaranteed at least a C-. Cumulative numerical averages of 60 -
69 are guaranteed at least a D-. These ranges may be lowered,
but
they will not be raised (e.g., if everyone has averages in the 90s,
everyone gets at least an A-).
Academic honesty
You are expected to abide by Virginia Tech's Community Standard for all work
for this course. Violations of the Standard will result in a
failing final grade for this course and will be reported to the Dean of
Students for
adjudication. Ignorance of what constitutes academic dishonesty
is
not a justifiable excuse for violations.
For the homework problems, you may work with a study group with
others
but must submit your own answers, unless otherwise indicated. For exams, you are required to work alone and for only the
specified time period.
Procedures if you suspect your work has been graded incorrectly
Every effort will be made to mark your work accurately. You should be credited with all the points you've worked hard to earn! However, sometimes grading mistakes happen. If you believe that an error has been made on an in-class problem or exam, return the paper to the instructor immediately, stating your claim in writing.
The following claims will be considered for re-grading:
(i) points are not totaled correctly;
(ii) the grader did not see a correct answer that is on
your paper;
(iii) your answer is the same as the correct answer, but in a
different form (e.g., you wrote a correct answer as 1/3 and the grader
was looking for .333);
(iv) your answer to a free response question is essentially
correct but stated slightly differently than the grader's
interpretation.
The following claims will not be considered for re-grading:
(v) arguments about the number of points lost;
(vi) arguments about question wording.
Considering re-grades takes up valuable time and resources that TAs
and the instructor would rather spend helping you understand
material. Please be considerate and only bring claims of type
(i),
(ii), (iii), or (iv) to our attention.