Statistics 328 Schedule

The schedule below will be kept if time and other circumstances permit. It is likely that it will be adapted according to a current classroom progress; it can also be subject to an adjustment or change.

Key:
VR: W. N. Venables and B. D. Ripley: Modern Applied Statistics with S (4th ed.), Springer 2002

HT: R. V. Hogg and E. A. Tanis: Probability and Statistical Inference 6th Edition, Prentice Hall, 2001

Daily Schedule:

Jan. 20 : Overview and Introduction to Splus

  • Splus class examples

    Jan 22 : Splus and R, data summaries, distributions and tests

  • Splus class examples and boxplot, histogram and q-q examples and , lottery scatter plot example
  • Splus class examples
  • VR: 1.1 - 1.4, 5.1 - 5.3
  • HT: 1.1 - 1.5
  • HT: Chapters 6 & 7
  • help function in R (help(boxplot), help(t.test))

  • Homework 1 due Thurs. Jan 29

    Introduction to R should be available at at UpClose 714 S. Sixth, Champaign, by Friday January 23
    It is also available off the R-homepage!

    Jan. 27 : Generating Random Data, Uniform and other Distributions

  • VR: 5.2
  • R/Splus functions: .Random.seed, set.seed
  • R help file for set.seed

    Jan. 29 : Generating Random Data, other Distributions

  • HT: Chapter 3 Discrete Distribution
  • HT: Chapter 4 Continuous Distribution
  • R/Splus function: rnorm, rbinom
  • Class notes on Generating Random Data

  • 2nd half of class: Computer Lab, 113 Illini Hall
  • Lab 1: Introduction
  • Please read Chapter 1: Intoduction and preliminaries, p. 1-6 (from An Introduction to R)
  • If you feel you need some more practice:
    Work through examples in An Introduction to R: 2.1-2.7, 3.1-3.4, 5.3

  • Homework 2 due Thurs. Feb. 5
    and Solutions

    Feb. 3 : Matrix Operations and Linear Regression

  • VR: 3.9
  • Splus matrix operations class examples
  • Generating correlated normal data class examples

    Feb. 5 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 2: More Introduction

  • Homework 3 due Thurs. Feb. 12
    and Solutions

    Feb. 10 : S-lab, R functions, and Linear Regression

  • S-Lab (handout): Lab 1-3
  • See if you can install the R version of S-lab (off class homepage) on your own PC?

    Feb. 12 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 3: More Introduction

  • See if you can install the R version of S-lab (off class homepage) on your own PC?
  • S-Lab (handout): Lab 5 and 9 (We will do S-lab 5 later)
    hard copies are available outside 116E in a envelope S-lab.

  • Homework 4 due Thurs. Feb 26
    and Solutions

    Feb. 17 : No class

  • Work through S-Lab 1-3, 9
  • Ying Wei will be available W 3-4 for office hours in the Computer Lab.
    You can e-mail her if you have questions: yingwei@uiuc.edu

    Feb. 19 : No class

  • Work through S-Lab 1-3, 9

    No homework due this week (Homework 4 due Thus. Feb. 26)

    Feb. 24 : Linear Regression, Diagnositics and Inference

  • VR: 6.1-6.4, p. 17-18, 3.7
  • HT: 7.8, 7.9, 8.8
  • Other ref: S. Weisberg: Applied Linear Regression copies available on reserve in the Mathematics Libraray
  • R/Splus function: lm
  • R class questions (pdf format)
  • R/Splus class examples and plots (pdf format)
  • CI class handout (pdf format)

    Feb 26 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 4: Linear Regression
  • S-Lab (handout): Lab 10-13
    hard copies are available outside 116E in a envelope S-lab.

  • Homework 5 due March 4
    and Solutions
  • Note: page 100 and page 104 are missing from the handout, sorry!

    March 2 : Nonlinear Regression, Gauss-Newton, and CI's

  • VR: Chapter 8: 8.1 - 8.4
  • Splus Stormer class example
  • Nonlinear Regression Parameter CI's (pdf format)
  • R/Splus function: nls

    March 4 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 5: Nonlinear Regression

  • Homework 6 due March 11
    and Solutions

    March 9 : Expected Value, Monte Carlo Integration, and Variance Reduction

    March 11 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 6: Expected Value, Monte Carlo Integration, and Variance Reduction
  • S-Lab (handout): Lab 8
    hard copies are available outside 116E in a envelope S-lab.

  • Homework 7 due March 18
    and Solutions

    March 16 : Nonparametric Bootstrap

  • VR: 5.7, p. 133-138
  • Ref: An Introduction to the Bootstrap by Efron and Tibshirani, Chapters 6-9

    March 18 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!
  • Lab 7: Nonparametric Bootstrapping

  • No Homework have a good break!
    (Bootstrapping will be on the midterm, after break, Thurs. April 1 and will be due April 8 )

    March 20-28: Spring Break, yipee!

    March 30 : Jackknife-after-bootstrap

  • VR: 5.7, p. 133-138

    April 1 : Computer Lab, 113 Illini Hall

  • Lab 8: More Bootstrap and Jackknife
  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20, just come to one!

  • Take-home Midterm (pdf format) due Thursday April 8 at 5PM

    April 6 : Better Bootstrap CI's, Permutations Tests

  • VR: 5.7 p. 145

    April 8 : Computer Lab, 113 Illini Hall

  • Lab 9: Better Bootstrap
  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20 and , just come to one!

  • Homework 8 due April 15
    and Solutions

    April 13 : Density Estimation, Nonparametric regression: smoothing splines

  • VR: 5.6 p. 126-132
  • Ref: Density Estimation for Statistics and Data Analysis, by B.W. Silverman
  • VR: 8.7 and 8.8
  • R/Splus function: smooth.spline, ksmooth
  • Ref: Generalized Additive Models, by T.J. Hastie and R.J. Tibshriani

    April 15 : Computer Lab, 113 Illini Hall

  • Lab 10: Density Estimation and Splines
  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20 and , just come to one!

  • Homework 9 due April 22
    and Solutions

    April 20 : Splines, Neural Network Models, Classfication and Regression Trees

  • VR: 8.11
  • Ref: Pattern Recognition and Neural Networks, by B.D. Ripley
  • VR: Chapter 9

    April 22 : Computer Lab, 113 Illini Hall

  • Lab 11: Splines, Neural Networks and Trees
  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20 and , just come to one!

  • No homework this week, please work on your project !

    April 27 : Markov Chain Monte Carlo (MCMC) Introduction

  • MCMC and refs
  • Ref: (1996) "Markov chain concepts related to sampling algorighms" by Gilks, et al., Chapter 3 of Markov chain Monte Carlo" by Gilks, et al., Chapter 1 of Markov chain Monte Carlo in Practice

    April 29 : Computer Lab, 113 Illini Hall

  • There are 2 lab sessions 9:00-9:40 and 9:40-10:20 and , just come to one!

    May 4 : Student Project Presentations!

  • Volunteer to present for 10 minutes of fame!
  • Thanks to volunteers so far!

    Project due date: (Final Exam is scheduled Friday May 7, 8-11 and
    I will be in my office to take your projects, if you would like to give them to me in person.).
    All projects are due by 5PM on Monday, May 10

    Have a good summer!