Statistical Consulting: Use of modern statistical methods for data analysis with R
Instructor: Larry Goldstein, larry at usc dot edu, KAP 406D, 213 740 2405. 

Office Hours: MW 10-11

Grader: Xiaojing Xing,  xiaojinx at usc edu,  KAP 403,  213 821-1474
Lecture:  KAP 134, MW 2:00-3:20

Text and Course Coverage

An Introduction to Statistical Learning, James, Witten, Hastie and Tibshirani
Time Permitting, from the text book the course will cover:

Chapter 1: Introduction
Chapter 2: Statistical Learning
Chapter 4: Classification
Chapter 5: Resampling Methods
Chapter 6: Linear Model Selection and Regularization
Chapter 8: Tree Based Methods
Chapter 10: Unsupervised Learning

Though the main emphasis of the course is on the handling of real data and the use of R, the course will also include various mathematical `interludes’ that explain, justify and broaden the understanding of the basis on which some of the methods introduced rest, in particular for those techniques that may not have been covered in previous core courses.

Links

R Resources: Introduction to R, Another Introduction to R,  R homepage,  Introductory example,  R-Tutorial, and R Graphics Gallery, Boston Housing Variables

Exams and Grading Policy

Grading Policy

  • 30% Homework and in class assignments
  • 30% Midterm exam, Monday Oct 3rd , n=17, median = 378
  • 35% Final Project: each student will pick a consulting topic, prepare a writeup and make a class presentation. The presentation should describe the problem considered, why it is of interest, the data available, and the goals of inference. Then the method of data analysis should be discussed, the results of that analysis, along with the conclusions made and a sense of how reliable those conclusions are. You may include R code written for specifically for the project if you find that it contains some component of interest. There are no preset limits on the length of the writeup, but ballpark it could be from 4-10 pages, without code.
  • 5% Participation in presentations of course final projects.

Assignments

Chapter 2 Exercises:   Conceptual 1-7, Applied 8-10    Additional Exercise #1, Due Sept 7th
Chapter 4 Exercises:   Conceptual 1-9, Applied 10-13  Due Sept 27th
Chapter 5 Exercises:   Conceptual 1-4, Applied 5-8      Due Oct 18th
Chapter 6 Exercises:   Conceptual 1,3,4,5,6,7, Applied 9,11  Due Nov 4th
Chapter 8 Excecises:   Conceptual 1-5, Applied 8,10,11 Due Nov 18th
Chapter 10 Exercises: Conceptual 2,4,6, Applied 7,8,9 Due Dec 2nd

Project Writeups: Due December 1st.

Project Presentations

Nov 2:  Cong Wu
Nov 7:  Han Li, Xiaoya Xiong
Nov 9:  Jie Ren, Zheng Dai
Nov 14: Xinrui He, Daoud Burghal
Nov 16: Enes Ozel, Moses Wintner
Nov 21:  Xin-Zeng Wu, Mary Same
Nov. 28: Ian Thacker, Yanqin Duanmu
Nov. 30: Melike Tuysuzoglu, Nachikethas Jagadeesan

Projects

As you begin thinking of a potential project, please keep the following items in mind:

1. The overall question or questions you would like to address.

2. What data you will use and where it can be obtained.

3. What specific predictors are available, roughly how many there are, and how large a sample size you will have.

4. What specific response you would like to predict, and what model and methods you will use to predict it.

Data Links of Interest

Important Dates and Information

October 7th, last day to register and add, or to drop without mark of W
November 11th, last day to drop a class with mark of W
December 2nd, classes end.

Full registration calendar
Statement of Academic Conduct