Anil Aswani

4119 Etcheverry

Office hours – Tu 200-300P; Th 1130-1230P

aaswani [at] berkeley [dot] edu

4119 Etcheverry

Office hours – Tu 200-300P; Th 1130-1230P

aaswani [at] berkeley [dot] edu

Ilgin Dogan

ilgindogan [at] berkeley [dot] edu

Ruojie Zeng

rzeng5 [at] berkeley [dot] edu

ilgindogan [at] berkeley [dot] edu

Ruojie Zeng

rzeng5 [at] berkeley [dot] edu

TuTh 1230-200P, 105 North Gate

Section 1: W 4-5P, 166 Barrows

Section 2: F 4-5P, 106 MoffittLibrary

Section 2: F 4-5P, 106 MoffittLibrary

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

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

Tuesday, March 17, 2020 1230-200P

Thursday, May 14, 2020 3-6P

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.

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)

- Jan 21
- Probability Review; Course Syllabus
- Jan 23
- Method of Moments
- Jan 28
- Linear Regression
- Jan 30
- Linear Regression
- Feb 04
- Diagnostics
- Feb 06
- Heteroscedasticity
- Feb 11
- Maximum Likelihood Estimation
- Feb 13
- Maximum Likelihood Estimation
- Feb 18
- Bias-Variance Tradeoff
- Feb 20
- Regularization
- Feb 25
- Cross-Validation
- Feb 27
- Distribution Estimation
- Mar 03
- Semiparametric Models
- Mar 05
- Support Vector Machines
- Mar 10
- Markov Processes; Holt-Winters Algorithm
- Mar 12
- Midterm Review
- Mar 31
- Null Hypothesis Testing
- Apr 02
- One-Sample Location Tests
- Apr 07
- Confidence Intervals
- Apr 09
- Two-Sample Location Tests
- Apr 14
- Multiple Testing
- Apr 16
- Multiple Comparisons
- Apr 21
- Quality Control
- Apr 23
- Weighted Control Charts
- Apr 28
- Neyman-Pearson Testing
- Apr 30
- Final Review