# Stats 516 Course Info

**COVID-19 UPDATE (3/16/20): UMass has moved to distance online learning starting after spring break. All information for this course will now be available on Moodle, including new HWs, final exam, new virtual office hours, etc. You may use this site for existing resources (such as old posted final exams), but it will be no longer updated. From now, please use the Moodle page for this course.**

**Course Meets:** Tu, Th 10:00-11:15 LGRT 202

**Instructor**

Michael Sullivan, LGRT 1323G, 545-1909

Office Hours: Tues 11:15-11:45, 1:00-2:30, Thur 9:00-10:00, and by appointment.
If you want help by email instead of in person, a yes/no question is much more likely to receive an answer. I usually eat at Hampshire D.C. near the register. Feel free to ask me a question then as well.

**TA/Grader**

Zhou Tang, LGRT 1425, zhoutang at math dot umass dot edu

Office Hours: Wednesday 10:00-11:00.

**Prerequisites: ** Single-variable calculus (Math 131, 132),
Probability with calculus (Stats 515) with a grade C or better, and multi-variable calculus (Math 233).

**Credit:** 3 credit hours

**Required: **
Wackerly, Mendenhall, Schaeffer, Mathematical Statistics with Applications, 7th Ed. (ISBN-13: 978-0495110811). Note that a minority (10%?) of the problems on the international version of the 7th edition are different than our (US) 7th edition problems. You must turn in the US 7th edition problems if you want
them graded. If you are unsure, you can compare your problems to the ones in the DuBois Library Reserve textbook.

** Course Description: **
Continuation of Stat 515. Overall objective of the course is the development of basic theory and methods for statistical inference. Topics include: Sampling distributions; General techniques for statistical inference (point estimation, confidence intervals, tests of hypotheses); Development of methods for inferences on one or more means (one-sample, two-sample, many samples - one-way analysis of variance), inference on proportions (including contingency tables), simple linear regression and non-parametric methods (time permitting).

** Contents: ** This schedule is tentative (for example, this plans for 13.5 weeks in a 13 week semester).

1.5 weeks: Review of Central Limit Theorem from Stats 515 (Chapter 7)

3 weeks: Point estimators and confidence intervals (Chapter 8)

3 weeks: Properties of estimators and methods of estimation (Chapter 9).
Midterm.

4 weeks: Hypothesis testing (Chapter 10)

2 weeks: Linear models (Chapter 11.1-11.8). Review for final.

** Exams: **
The midterm will be closed-book **10AM-1115AM on March 10 in LGRT 1634** (16th floor).
The final will be closed-book **8AM-10AM May 1 room TBD**.
The final is determined by Spire and may change, so check Spire for any updates, including room location.
For both exams you may bring in **one** (8.5 by 11)-inch sheet of paper, double-sided, with whatever **hand-written** formulas and/or notes you want. I will not provide you with a formula sheet; however, I will provide relevant (or irrelevant) distribution tables as they appear in the textbook appendix. You should bring a calculator.
If you have a conflict, you must notify me at least 2 weeks in advance by filling out the following
sheet
signed by the Registrar's office.
Make sure to not book any travel during exams. Travel is not an excuse to miss the exam.
If a last-minute emergency occurs after the two-week deadline, you will need
to present to me a note either from your medical provider for
medical emergencies or from the
Office of the Dean of Students for non-medical emergencies.

**Grading: **
The grade for the course has three components:
Midterm (30%), Final (35%) and timely-completion of HWs (35%).

If attendance starts to drop, there MAY be several short in-class quizzes that will supplement the homework grade component.
These quizzes will be relatively easy. Some may be individual and some group efforts.
If you turn in your HW late, you must give it to the grader directly. The grader is not obligated to grade your late HW if he/she/they already graded the other students' HWs because the grader has his/hers/their own work/study schedule to follow. If the grader does grade your late HW, there is a 25% penalty for up to 24 hours late, and 50% penalty for up to a week late.
UMass does not have the resources to pay for grading entire HWs, so only select problems will be graded.