Yoon Lee

yllee [at] berkeley [dot] edu

SangWoo Park

spark111 [at] berkeley [dot] edu

yllee [at] berkeley [dot] edu

SangWoo Park

spark111 [at] berkeley [dot] edu

TuTh 1230-200P, on Zoom

Section 1: W 4-5P, on Zoom

Section 2: F 4-5P, on Zoom

Section 2: F 4-5P, on Zoom

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

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

Grades will be determined using a fixed scale. A raw percentage will be computed using the above breakdown, and the raw percentage will be rounded down. The letter grade will be determined using the rounded down percentage and the below given scale.

Grade Scale: A 94-100, A- 90-93, B+ 87-89, B 83-86, B- 80-82, C+ 77-79, C 73-76, C- 70-72, F 0-69

Grades will be determined using a fixed scale. A raw percentage will be computed using the above breakdown, and the raw percentage will be rounded down. The letter grade will be determined using the rounded down percentage and the below given scale.

Grade Scale: A 94-100, A- 90-93, B+ 87-89, B 83-86, B- 80-82, C+ 77-79, C 73-76, C- 70-72, F 0-69

Thursday, March 18, 2021 using Gradescope

Thursday, May 13, 2021 using Gradescope

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:

- Estimation – Review of probability; method of moments; least squares regression; regularization; maximum likelihood estimation; support vector machines (SVMs); forecasting (about 6 weeks)
- Testing – null hypothesis testing; t-test; confidence intervals; Mann-Whitney U test; multiple testing; ANOVA; Kruskall-Wallis test; likelihood ratio tests; quality control (about 6 weeks)

- Jan 19
- Probability Review

Course Syllabus - Jan 21
- Method of Moments
- Jan 26
- Method of Moments
- Jan 28
- Linear Regression
- Feb 02
- Diagnostics
- Feb 04
- Heteroscedasticity
- Feb 09
- Heteroscedasticity
- Feb 11
- Maximum Likelihood Estimation
- Feb 16
- Maximum Likelihood Estimation
- Feb 18
- Bias-Variance Tradeoff
- Feb 23
- Regularization
- Feb 25
- Regularization
- Mar 02
- Cross-Validation
- Mar 04
- Distribution Estimation
- Mar 09
- Distribution Estimation
- Mar 11
- Semiparametric Models
- Mar 16
- Support Vector Machines
- Mar 30
- Stochastic Forecasting
- Apr 01
- Null Hypothesis Testing
- Apr 06
- One-Sample Location Tests
- Apr 08
- Confidence Intervals
- Apr 13
- Two-Sample Location Tests
- Apr 15
- Multiple Testing
- Apr 20
- Multiple Comparisons
- Apr 22
- Quality Control
- Apr 27
- Weighted Control Charts
- Apr 29
- Neyman-Pearson Testing