## STA114/MTH136: Statistics

 Lec: Old Chem 116 Lab: Soc/Psych 133 Mon & Wed 2:20-3:35pm Thu 12:40-1:55 or 2:15-3:35pm Prof: Robert L. Wolpert TA: Hui Huang E-mail: wolpert@stat.duke.edu huang@stat.duke.edu Office: Old Chem 211c, 684-3275 Old Chem 212, 684-4365 OH: Fri 2:15-3:15pm Not Yet Known Mon & Wed 3:35-4:00pm (in classroom)

### Description

An introduction to the concepts, theories and methods of statistical inference. We discuss both the ideas and methods of modern Bayesian statistical science as well as the classical methods based on sampling theory. Statistics is a vast field, and a first one-semester course can offer only a brief introduction, with a deeper look at a few key ideas. The goal of this course is to provide such an introduction and to illustrate through examples how Statistics serves as the foundation for all scientific reasoning and inference amid uncertainty.

Statistical modeling and inference are based on the mathematical theory of probability and solving practical problems usually requires integration or optimization in several dimensions. Thus this course requires a solid mathematical background (calculus and linear algebra at the level of MTH 103 and MTH 104) and proficiency in basic probability theory (MTH 135, STA 104, or STA 213); students without strong preparation in these will need to invest significant additional time to fill in the gaps. Solving all but the simplest problems also requires computer skills and familiarity with statistical software that will be taught in the weekly Discussion Section on Thursday afternoons.

The course text is Morris DeGroot, Probability and Statistics (2nd edn). All class materials are distributed on-line via the web; for example, you may view homework assignments (and sometimes class notes) on the Syllabus. A class e-mail directory will be available on-line.

### Homework Assignments

The only way to be sure you're learning the course material is to solve problems (or, as Sophocles put it, One must learn by doing the thing; for though you think you know it, you have no certainty until you try.) Only the simplest problems can be solved without the use of a computer, so we will have weekly discussion section to introduce you to statistical Computing using the S-Plus statistical computing environment. Ten weekly problem sets are assigned through the on-line syllabus. Homeworks are collected at the START of class each Wednesday, after which solutions will be posted on the web. Late homeworks will not be accepted for full credit without a Dean's Excuse, but the lowest homework score will be dropped. Partial credit is available for homeworks turned in no more than a few hours late.

You may work with other students on the homework problems, but your final answers should be written up independently: copying homework solutions is not allowed. You are encouraged to ask the Professor and the TA for help on your homework, after you have tried to solve the problems on your own. Questions about homework scores should first be addressed to the TA.

Help is available! The TA and I both have office-hours (see above); in addition, Duke Statistics maintains an open Help Session every Mon-Thu afternoon (4-6pm) in room 211 Old Chem (just outside my office), where a statistics graduate student will be happy to help you.

### Tests & Projects

This exam and several other course materials are offered in PostScript form. The ACPub cluster computers are all configured properly to display and print PS documents; if your home computer is not, click here for Windows platforms and here for Linux or Macs to get the GSview add-in.

#### Projects

If you wish, you may submit an optional Term Project, which will take the place of the Final Exam. Projects are described in some detail here.