Instructor:

Anil Aswani
4119 Etcheverry
Office hours – Tu 200-300P; Th 1130-1230P
aaswani [at] berkeley [dot] edu

GSI:

Ilgin Dogan
ilgindogan [at] berkeley [dot] edu

Ruojie Zeng
rzeng5 [at] berkeley [dot] edu

Lectures:

TuTh 1230-200P, 105 North Gate

Discussions:

Section 1: W 4-5P, 166 Barrows
Section 2: F 4-5P, 106 MoffittLibrary

Website:

Optional Textbook:

Introduction to Probability and Statistics for Engineers and Scientists, by Sheldon Ross

Prerequisites:

IEOR 172 or STAT 134 or an equivalent course in probability theory

Grading:

Project (20%); homeworks (20%); midterm (20%); final exam (40%)

Midterm:

Tuesday, March 17, 2020   1230-200P

Final Exam:

Thursday, May 14, 2020   3-6P

Description:

This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Applications in forecasting and quality control.

Outline:

Specific topics that will be covered include:
  • Regression – Basic optimization; maximum likelihood estimation; least squares regression; high-dimensional regression; support vector machines (SVM's) (about 6 weeks)
  • Forecasting – ARAR algorithm; Holt-Winters algorithm; Holt-Winters seasonal algorithm (about 1 week)
  • Hypothesis Testing – Review of probability; t-test; confidence intervals; Mann-Whitney U test; multiple testing; ANOVA; Kruskall-Wallis test; likelihood ratio tests; quality control (about 6 weeks)

Lecture Notes: