Stat 431, Fall 2007

Statistical Inference

Homework assignments     Schedule of topics     Announcements

People

      Office hours
Instructor Mikhail Traskin mtraskin@wharton Monday/Wednesday, 10:30 am – 12:00 pm, 466 Jon M. Huntsman Hall
TA Xu Han hanxu3@wharton Thursday, 3:00 – 5:00 pm, 427.2 Jon M. Huntsman Hall

Additional help

Visit the StatLab: location and schedule at http://stat.wharton.upenn.edu/~sivana/statlab.html.

Lectures

109 Steinberg Hall - Dietrich Hall. Monday/Wednesday, 1:30 – 3:00 pm.

Course homepage

Refer to http://stat.wharton.upenn.edu/~mtraskin/courses/stat431/fall07/index.html (this page) for announcements, handouts, homework assignments and other materials.

Course description

This course is about making decisions under uncertainty using statistical methods. The topics include estimation, confidence intervals, hypothesis testing, single and multiple linear regression, one-way and two-way analysis of variance, variable selection, logistic regression and categorical data analysis.

Interpretation of the results and analysis of assumptions is an important part of the course. Statistical computing package will be extensively used to carry out the computations. However no special emphasis will be made on the details of computations.

Prerequisites

Familiarity with basic probability theory is assumed. Stat 430 or equivalent should provide sufficient background. Otherwise chapters 2, 3, 4 and 5 of the Devore's book will be a good substitute together with a short review of fundamental concepts used by the course, written by Prof. David Freedman, University of California, Berkeley.

Statistical computing package

JMP version 7, available in the Wharton Computer Labs, F75/F80 Jon M. Huntsman Hall (Wharton account required: see http://accounts.wharton.upenn.edu). Individual copies are also available for purchase at http://estore.e-academy.com. A six-month license costs $29.95 and twelve-month is $49.95.

Text book

J. L. Devore, Probability and Statistics for Engineering and the Sciences, 7th ed.

Assignments

Grading

Homework assignments

Weekly assignments will be due at the beginning of lecture each Monday. Problems involving computer calculations should be worked using JMP. No extensions to the due date will be given. However, the lowest homework assignment score will be omitted from the final grade calculation. Unsubmitted work counts as a zero score.

You may work with and help each other, however you must submit your own solutions, with your own writeup, unless otherwise noted.

Exams

In-class midterm: Wednesday, October 24th, 1:30 – 3:00 pm, 109 Steinberg Hall - Dietrich Hall. No make-up.

Final: Wednesday, December 12th, 12:00 – 2:00 pm, 351 SHDH.

Both exams are open book and open notes. Calculators may be used but no laptops are allowed.

Schedule of topics

Lectures will closely follow the text which will be occasionally supplemented with handouts on topics going beyond those covered in the book. A review of basic probability theory and common distributions, e.g. chapters 2, 3, 4 and 5 in Devore, might be useful. Topics discussed in sections 3.4 (binomial distribution), 4.3 (normal distribution) and 5.4 (central limit theorem) are of special interest.

Lec# Date Topic Text
01 Wed 05 Sep Introduction/overview  
02 Mon 10 Sep Normality; boxplots; QQ plots (probability plots) 1.4, 4.6
03 Wed 12 Sep Confidence intervals: known variance; Large-sample CIs 7.1, 7.2
04 Mon 17 Sep Confidence intervals for population proportion; CIs: unknown variance; Non-normal population distribution 7.2 – 7.4
05 Wed 19 Sep One-sample hypothesis testing 8.1, 8.2
06 Mon 24 Sep One-sample hypothesis testing 8.3 – 8.5
07 Wed 26 Sep Two-sample inference: testing and intervals 9.1, 9.2
08 Mon 01 Oct Two-sample inference: testing and intervals 9.3 – 9.5
09 Wed 03 Oct Single factor (one-way) ANOVA 10.1, 10.2
10 Mon 08 Oct Single factor (one-way) ANOVA 10.2, 10.3
11 Wed 10 Oct Two factor (two-way) ANOVA 11.1
  Mon 15 Oct Fall break, no class  
12 Wed 17 Oct Two factor (two-way) ANOVA 11.2
13 Mon 22 Oct Simple linear least-squares regression 12.1, 12.2
  Wed 24 Oct Midterm  
14 Mon 29 Oct Simple linear least-squares regression; Correlation coefficient 12.3 – 12.5
15 Wed 31 Oct Simple regression and variable transformation 13.1, 13.2
16 Mon 05 Nov Simple nonlinear regression; polynomial regression 13.3
17 Wed 07 Nov Multiple linear least-squares regression 13.4
18 Mon 12 Nov Multiple linear least-squares regression 13.4, 13.5
19 Wed 14 Nov Variable selection 13.5
20 Mon 19 Nov Logistic regression 13.2, slides
  Wed 21 Nov More on logistic regression. Q&A.  
21 Mon 26 Nov Categorical data analysis: goodness of fit 14.1, 14.2
22 Wed 28 Nov Categorical data analysis: goodness of fit 14.2, 14.3
23 Mon 03 Dec Bootstrap  
24 Wed 05 Dec Distribution-free procedures 15.1, 15.2

Announcements