STATS 50: Mathematics of Sports (MCS 100)
The use of mathematics, statistics, and probability in the analysis of sports performance, sports records, and strategy. Topics include mathematical analysis of the physics of sports and the determinations of optimal strategies. New diagnostic statistics and strategies for each sport. Corequisite:
STATS 60, 110 or 116.
Terms: Spr

Units: 3

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Hwang, J. (PI)
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:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Sklar, M. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (TA)
;
Greaves, D. (TA)
;
Guan, L. (TA)
;
Lemhadri, I. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (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: 45

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Bi, N. (PI)
;
Miolane, N. (PI)
;
Pavlyshyn, D. (PI)
...
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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: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
...
more instructors for STATS 116 »
Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
;
Bi, N. (TA)
;
Cauchois, M. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (TA)
;
Zhang, A. (TA)
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: 35

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
STATS 167: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 267)
Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of
STATS 60 or 116.
Terms: not given this year, last offered Spring 2016

Units: 4

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
STATS 191: Introduction to Applied Statistics
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and crossvalidation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Win

Units: 34

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
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