STATS 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, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Blevins, E. (PI)
;
Harrison, M. (PI)
...
more instructors for STATS 60 »
Instructors:
Baiocchi, M. (PI)
;
Blevins, E. (PI)
;
Harrison, M. (PI)
;
Hsu, T. (PI)
;
King, L. (PI)
;
Miolane, N. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Tong, L. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
Azadkia, M. (TA)
;
Cao, S. (TA)
;
Han, K. (TA)
;
Lemhadri, I. (TA)
;
Rajanala, S. (TA)
;
Ruan, F. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Miolane, N. (PI)
;
Mukhopadhyay, S. (PI)
;
Bhattacharya, S. (TA)
...
more instructors for STATS 110 »
Instructors:
Miolane, N. (PI)
;
Mukhopadhyay, S. (PI)
;
Bhattacharya, S. (TA)
;
Seiler, B. (TA)
;
Tuzhilina, E. (TA)
STATS 110U: Statistical Methods in Engineering and the Physical Sciences
For Summer UG Visitors only. Same as 110. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum

Units: 5

Grading: Letter or Credit/No Credit
Instructors:
Mukhopadhyay, S. (PI)
STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum

Units: 4

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Kaluwa Devage, P. (PI)
;
Mohanty, P. (PI)
;
Siegmund, D. (PI)
...
more instructors for STATS 116 »
Instructors:
Kaluwa Devage, P. (PI)
;
Mohanty, P. (PI)
;
Siegmund, D. (PI)
;
Zhu, X. (PI)
;
Bi, N. (TA)
;
Cao, S. (TA)
;
Li, S. (TA)
;
Misiakiewicz, T. (TA)
;
SUR, P. (TA)
STATS 116U: Theory of Probability
For Summer UG Visitors only. Same as
Stats 116. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum

Units: 4

Grading: Letter or Credit/No Credit
Instructors:
Kaluwa Devage, P. (PI)
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, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Blevins, E. (PI)
;
Harrison, M. (PI)
...
more instructors for STATS 160 »
Instructors:
Baiocchi, M. (PI)
;
Blevins, E. (PI)
;
Harrison, M. (PI)
;
Hsu, T. (PI)
;
King, L. (PI)
;
Miolane, N. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Tong, L. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
Azadkia, M. (TA)
;
Cao, S. (TA)
;
Han, K. (TA)
;
Lemhadri, I. (TA)
;
Rajanala, S. (TA)
;
Ruan, F. (TA)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
...
more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Mohanty, P. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Taylor, J. (PI)
;
Tran, L. (PI)
;
Cai, F. (TA)
;
Feldman, M. (TA)
;
Guo, K. (TA)
;
Markovic, J. (TA)
;
Qian, J. (TA)
;
Ray, S. (TA)
;
Tirlea, M. (TA)
;
Wu, H. (TA)
STATS 202U: Data Mining and Analysis
For Summer UG Visitors only. Sames as
Stats 202. This course is offered remotely only via video segments. TAs will host remote weekly office hours using an online platform such as Zoom.
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Tran, L. (PI)
STATS 203V: 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. This course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. Prerequisites: a postcalculus introductory probability course, e.g.
STATS 116. In addition, a corequisite postcalculus mathematical statistics course, e.g.
STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Kaluwa Devage, P. (PI)
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