printer friendly pageSTATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Chi, K. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (TA)
STATS 100: Mathematics of Sports
This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g,
Stats 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g.,
Math 51) and basic probability is strongly recommended.
Terms: Win
| Units: 3
| UG Reqs: GER:DB-Math, WAY-AQR
STATS 141: Biostatistics (BIO 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Win
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR
Instructors:
Erdmann-Pham, D. (PI)
;
Gablenz, P. (TA)
;
Morrison, T. (TA)
...
more instructors for STATS 141 »
Instructors:
Erdmann-Pham, D. (PI)
;
Gablenz, P. (TA)
;
Morrison, T. (TA)
;
Vu, V. (TA)
;
Zhou, K. (TA)
STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
Instructors:
Chi, K. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (TA)
STATS 195: Introduction to R
This short course runs for four weeks. It is recommended for students who want to use R in statistics, science or engineering courses, and for students who want to learn the basics of data science with R. The goal of the short course is to familiarize students with some of the most important R tools for data analysis. Lectures will focus on learning by example and assignments will be application-driven. No prior programming experience is assumed.
Terms: Win
| Units: 1
Instructors:
Zhang, I. (PI)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Palacios, J. (PI)
...
more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Palacios, J. (PI)
;
Sabatti, C. (PI)
;
Schramm, T. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)
STATS 200: Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite:
STATS 116. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut, Win, Sum
| Units: 4
Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
...
more instructors for STATS 200 »
Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
;
Cherian, J. (TA)
;
Jang, J. (TA)
;
Jeong, Y. (TA)
;
Kanekar, R. (TA)
;
Lee, J. (TA)
;
Lu, S. (TA)
;
Ma, G. (TA)
;
Salerno, M. (TA)
;
Tanoh, I. (TA)
;
Xie, R. (TA)
;
Zhang, I. (TA)
;
Zhao, S. (TA)
STATS 203: Introduction to Regression Models and Analysis of Variance
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites: A post-calculus introductory probability course, e.g.
STATS 116, basic computer programming knowledge, some familiarity with matrix algebra, and a pre- or co-requisite post-calculus mathematical statistics course, e.g.
STATS 200.
Terms: Win
| Units: 3
STATS 208: Bootstrap, Cross-Validation, and Sample Re-use
By re-using the sample data, sometimes in ingenious ways, we can evaluate the accuracy of predictions, test the significance of a conclusion, place confidence bounds on an unknown parameter, select the best prediction architecture, and develop more accurate predictors. In this course, we will describe the many ways that samples get reused to achieve these goals, including the bootstrap, the parametric bootstrap, cross-validation, conformal prediction, random forests, and sample splitting. We also develop basic theory justifying such methods. Prerequisite: course in statistics or probability.
Terms: Win
| Units: 3
Instructors:
Donoho, D. (PI)
;
Wang, Y. (TA)
STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, EPI 206, MED 206)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win
| Units: 3
Instructors:
Ioannidis, J. (PI)
;
Kvarven, A. (PI)
Filter Results: