printer friendly pageSTATS 42Q: Undergraduate Admissions to Selective Universities - a Statistical Perspective
The goal is the building of a statistical model, based on applicant data, for predicting admission to selective universities. The model will consider factors such as gender, ethnicity, legacy status, public-private schooling, test scores, effects of early action, and athletics. Common misconceptions and statistical pitfalls are investigated. The applicant data are not those associated with any specific university.
Terms: not given this year
|
Units: 2
|
Grading: Satisfactory/No Credit
STATS 47N: Breaking the Code?
Preference to freshmen. Cryptography and its counterpart, cryptanalysis or code breaking. How the earliest cryptanalysts used statistical tools to decrypt messages by uncovering recurring patterns. How such frequency-analysis tools have been used to analyze biblical texts to produce a Bible code, and to detect genes in the human genome. Overview of codes and ciphers. Statistical tools useful for code breaking. Students use simple computer programs to apply these tools to break codes and explore applications to various kinds of data.
Terms: Aut
|
Units: 3
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Holmes, S. (PI)
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 116.
Terms: Aut
|
Units: 3
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Cover, T. (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, 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:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Richards, W. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Chouldechova, O. (TA)
...
more instructors for STATS 60 »
Instructors:
Richards, W. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Chouldechova, O. (TA)
;
He, P. (TA)
;
Sun, D. (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: 4-5
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Khalessi, S. (PI)
;
Reeves, G. (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: 3-5
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
;
Labo, P. (PI)
;
Romano, J. (PI)
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: Aut
|
Units: 4-5
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Sun, D. (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
|
Grading: Letter or Credit/No Credit
Instructors:
Richards, W. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
STATS 166: Computational Biology (BIOMEDIN 366, STATS 366)
Course is designed to introduce students from the mathematical, physical and engineering sciences to selected current issues in computational biology and bioinformatics. Topics:Principles of gene expression and taxa abundance measurements by microarrays and sequencing. Kernel methods for graph gene intereaction graph construction. Phylogenetic trees and their uses in microbiome studies. Computational nonparametric statistics for the analyses of real genomic studies. Assignments: weekly reading of papers and a final project.
Terms: Spr
|
Units: 3
|
Repeatable for credit
|
Grading: Letter or Credit/No Credit
Instructors:
Holmes, S. (PI)
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: Spr
|
Units: 4
|
UG Reqs: GER:DBMath
|
Grading: Letter or Credit/No Credit
Instructors:
Diaconis, P. (PI)
;
Skyrms, B. (PI)
Filter Results: