Statistics 13, Spring 2011
Overview

In this course, we will present a broad introduction to some of the fundamental tools and concepts of statistics. Topics include the representation and interpretation of data, descriptive statistics and graphical displays, hypothesis testing, and an introduction to correlation and regression. We will emphasize quantitative reasoning and the practice of statistics.

Lectures

   Lecture 1: Introduction
     (Bills of Mortality, early significance testing,
      "a data society?", course themes)

    Lecture 2: Reshaping and transforming data

    Lecture 3: EDA (basic graphics and extensions)

    Lecture 4: Randomized controlled trials, re-randomization tests

    Lecture 5 Testing, random number generation

    Lecture 6 A/B testing

    Lecture 7 Transformations, 2003 recall election

    Lecture 8 Four views of probability

    Lecture 9 Normality

    Lecture 10 The bootstrap (I)

    Lecture 11 The bootstrap (II)

    Lecture 12 The classics

    Lecture 14 Regression (I)

    Lecture 15 Regression (II)

    Lecture 16 The classics (II)

Homework

Your homework will be a mix of problems from the text as well as assignments that require using the computer (R). We will avoid having you do a lot of tedious hand calculations, and instead focus your "written" output on interpretations.

Labs

Lab assignments are designed around the programming language, R. You should show up in lab each Thursday, even if you intend to use your own computer to complete the assignment; this will give the TA the opportunity to tell you what he expects you to hand in and for you to ask any questions. Because R runs on almost every computing platform, you are certainly encouraged to work at home. (Who am I to stop the "spread" of computing technology?)

Due 4/7: Introduction
Due 4/14: EDA Part 1 (4 questions)
Due 4/21: Re-randomization (4 questions)
Due 5/12: The bootstrap (3 questions)
Due 5/19: Confidence intervals
Due 6/2: Regression
Instructor    Mark Hansen
8951 Mathematical Sciences Building
cocteau@stat.ucla.edu
www.stat.ucla.edu/~cocteau
AIM: cocteautt

Meeting    MW 3-4:20
1220b Kinsey Pavillion


Office Hours    Th, Fr TBD
8951 Mathematical Sciences


Grading   
35% Take-home Midterms
(4th and 8th weeks)
35% Labs and Homework
25% Take-Home Final
5% Participation

In the calculation of their final grade, students may substitute their highest Homework and Lab score for their lowest (nonzero) score.

Computing    Labs and take-home projects will be based on the R computing environment. Aside from its strength as a tool for performing statistical computations and making graphical displays, we have chosen R because it is free software and runs on a variety of platforms. You can obtain a copy of R from the Comprehensive R Archive Network (select the so-called "precompiled binary" that matches your operating system).

In addition, we will be working with R through RStudio. Download and install the desktop version.

Textbook    Martin Bland
An introduction to medical statistics