Statistics

(College of Letters and Science)

Thomas Lee, Ph.D., Chairperson of the Department

Department Office. 4118 Mathematical Sciences Building; 530-752-2361; http://www.stat.ucdavis.edu

Faculty. http://www.stat.ucdavis.edu/people/faculty.html


(College of Letters and Science)

Thomas Lee, Ph.D., Chairperson of the Department

Department Office. 4118 Mathematical Sciences Building; 530-752-2361; http://www.stat.ucdavis.edu

Faculty. http://www.stat.ucdavis.edu/people/faculty.html

The Major Program

Statistics enables us to make inferences about entire populations, based on samples extracted from those populations. Statistical methods can be applied to problems from almost every discipline and they are vitally important to researchers in agricultural, biological, environmental, social, engineering, and medical sciences.

The Program. Statistics majors may receive either a Bachelor of Arts or a Bachelor of Science degree. Both the A.B. and the B.S. programs require theoretical and applied course work and underscore the strong interdependence of statistical theory and the applications and computational aspects of statistics. The B.S. degree program has four tracks: General Track, Applied Statistics Track, Computational Statistics Track, and the Statistical Data Science Track. The A.B. degree program has one track.  

A.B. in Statistics-Applied Statistics Option Track emphasizes statistical applications. This track is recommended for students who are interested in applications of statistical techniques to various disciplines, especially the social sciences.

Major Advisor. D. Paul

Students are encouraged to meet with an advisor to plan a program as early as possible. Sometime before or during the first quarter of the junior year, students planning to major in Statistics should consult with a faculty advisor to plan the remainder of their undergraduate programs.

Career Alternatives. Probability models, statistical methods, and computational techniques are used in a great many fields, including the biological, physical, social, and health sciences, business, and engineering. The wide applicability of statistics is reflected in the strong demand for graduates with statistical training in both the public and private sectors. Employment opportunities include careers in data and policy analysis in government and industry, financial management, quality control, insurance and healthcare industry, actuarial science, engineering, public health, biological and pharmaceutical research, law, and education. Students with an undergraduate degree in statistics have entered advanced studies in statistics, economics, finance, psychology, medicine, business management and analytics, and other professional school programs.

Preparatory Subject Matter
Units: 20-23
MAT 016A
Short Calculus (Active)
3
MAT 016B
Short Calculus (Active)
3
MAT 016C
Short Calculus (Active)
3
or
MAT 017A
Calculus for Biology and Medicine (Active)
4
MAT 017B
Calculus for Biology and Medicine (Active)
4
MAT 017C
Calculus for Biology and Medicine (Active)
4
or
MAT 021A
Calculus (Active)
4
MAT 021B
Calculus (Active)
4
MAT 021C
Calculus (Active)
4
MAT 022A
Linear Algebra (Active)
3
Choose one:
4
ECS 010
Introduction to Programming (Discontinued)
4
ECS 030
Programming and Problem Solving (Discontinued)
4
ECS 032A
Introduction to Programming (Active)
4
ECS 036A
Programming and Problem Solving (Active)
4
ECS 040
Software Development and Object-Oriented Programming (Discontinued)
4
or
The equivalent of one of the above.
 
STA 032
Gateway to Statistical Data Science (Active)
4
Depth Subject Matter
Units: 45-48
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 130A
Mathematical Statistics: Brief Course (Active)
4
STA 130B
Mathematical Statistics: Brief Course (Active)
4
Choose one:
4
STA 137
Applied Time Series Analysis (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
Choose three:
12
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
or
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
MAT 168
Optimization (Active)
4
One approved four unit course:
4
STA 194HA
Special Studies for Honors Students (Active)
4
STA 194HB
Special Studies for Honors Students (Active)
4
STA 199
Special Study for Advanced Undergraduates (Active)
1-5
Three upper division elective courses outside of Statistics
9-12
Electives are chosen with and must be approved by the major advisor. Electives should follow a coherent sequence in one single disciple where statistical methods and models are applied and should cover the quantitative aspects of the discipline. A list of pre-approved electives can be found on the Statistics Department website.
 
Total: 65-71

(College of Letters and Science)

Thomas Lee, Ph.D., Chairperson of the Department

Department Office. 4118 Mathematical Sciences Building; 530-752-2361; http://www.stat.ucdavis.edu

Faculty. http://www.stat.ucdavis.edu/people/faculty.html

The Major Program

Statistics enables us to make inferences about entire populations, based on samples extracted from those populations. Statistical methods can be applied to problems from almost every discipline and they are vitally important to researchers in agricultural, biological, environmental, social, engineering, and medical sciences.

The Program. Statistics majors may receive either a Bachelor of Arts or a Bachelor of Science degree. Both the A.B. and the B.S. programs require theoretical and applied course work and underscore the strong interdependence of statistical theory and the applications and computational aspects of statistics. The B.S. degree program has four tracks: General Track, Applied Statistics Track, Computational Statistics Track, and the Statistical Data Science Track.

B.S. in Statistics-General Track emphasizes statistical theory and is especially recommended as preparation for graduate study in statistics.

B.S. in Statistics-Applied Statistics Track emphasizes statistical applications. This track is recommended for students who are interested in applications of statistical techniques to various disciplines including the biological, physical and social sciences.

B.S. in Statistic-Computational Statistics Track emphasizes computing. This track is recommended for students interested in the computational and data management aspects of statistical analysis.

B.S. in Statistic-Statistical Data Science Track emphasizes data handling skills and statistical computation. This track is recommended for students interested in statistical learning methodology, advanced data handling techniques and computational aspects of statistical analysis. 

Major Advisor. D. Paul

Students are encouraged to meet with an advisor to plan a program as early as possible. Sometime before or during the first quarter of the junior year, students planning to major in Statistics should consult with a faculty advisor to plan the remainder of their undergraduate programs.

Career Alternatives. Probability models, statistical methods, and computational techniques are used in a great many fields, including the biological, physical, social, and health sciences, business, and engineering. The wide applicability of statistics is reflected in the strong demand for graduates with statistical training in both the public and private sectors. Employment opportunities include careers in data and policy analysis in government and industry, financial management, quality control, insurance and healthcare industry, actuarial science, engineering, public health, biological and pharmaceutical research, law, and education. Students with an undergraduate degree in statistics have entered advanced studies in statistics, economics, finance, psychology, medicine, business management and analytics, and other professional school programs.

General Statistics Track
Units: 82-84
Preparatory Subject Matter
31-32
MAT 021A
Calculus (Active)
4
MAT 021B
Calculus (Active)
4
MAT 021C
Calculus (Active)
4
MAT 021D
Vector Analysis (Active)
4
MAT 022A
Linear Algebra (Active)
3
or
MAT 067
Modern Linear Algebra (Active)
4
MAT 025
Advanced Calculus (Active)
4
ECS 030
Programming and Problem Solving (Discontinued)
4
or
ECS 040
Software Development and Object-Oriented Programming (Discontinued)
4
or
The equivalent.
 
Any one introductory statistics course; except STA 010.
4
Depth Subject Matter
51-52
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 131A
Introduction to Probability Theory (Active)
4
STA 131B
Introduction to Mathematical Statistics (Active)
4
STA 131C
Introduction to Mathematical Statistics (Active)
4
Choose three:
12
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
MAT 168
Optimization (Active)
4
One approved four unit course:
4
STA 194HA
Special Studies for Honors Students (Active)
4
STA 194HB
Special Studies for Honors Students (Active)
4
STA 199
Special Study for Advanced Undergraduates (Active)
1-5
MAT 125A
Real Analysis (Active)
4
MAT 108
Introduction to Abstract Mathematics (Active)
4
or
MAT 125B
Real Analysis (Active)
4
MAT 167
Applied Linear Algebra (Active)
4
Related elective courses
3-4
One upper division course approved by major advisor; it should be in mathematics, computer science or in quantitative aspects of a substantive discipline.
 
Applied Statistics Track
Units: 75-83
Preparatory Subject Matter
27-31
MAT 016A
Short Calculus (Active)
3
MAT 016B
Short Calculus (Active)
3
MAT 016C
Short Calculus (Active)
3
or
MAT 017A
Calculus for Biology and Medicine (Active)
4
MAT 017B
Calculus for Biology and Medicine (Active)
4
MAT 017C
Calculus for Biology and Medicine (Active)
4
or
MAT 021A
Calculus (Active)
4
MAT 021B
Calculus (Active)
4
MAT 021C
Calculus (Active)
4
MAT 022A
Linear Algebra (Active)
3
ECS 010
Introduction to Programming (Discontinued)
4
or
ECS 030
Programming and Problem Solving (Discontinued)
4
or
ECS 040
Software Development and Object-Oriented Programming (Discontinued)
4
or
The equivalent.
 
Two introductory courses serving as the prerequisites to upper division courses in a chosen discipline to which statistics is applied.
7-8
Any one introductory statistics course; except STA 010.
4
Depth Subject Matter
48-52
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 130A
Mathematical Statistics: Brief Course (Active)
4
STA 130B
Mathematical Statistics: Brief Course (Active)
4
Choose three:
12
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
or
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
MAT 168
Optimization (Active)
4
One approved four unit course:
4
STA 194HA
Special Studies for Honors Students (Active)
4
STA 194HB
Special Studies for Honors Students (Active)
4
STA 199
Special Study for Advanced Undergraduates (Active)
1-5
Four upper division elective courses outside of Statistics.
12-16
Electives are chosen with and must be approved by the major advisor. Electives should follow a coherent sequence in one single disciple where statistical methods and models are applied: at least three of them should cover the quantitative aspects of the discipline. A list of pre-approved electives can be found on the Statistics Department website.
 
Computational Statistics Track
Units: 79
Preparatory Subject Matter
27
MAT 021A
Calculus (Active)
4
MAT 021B
Calculus (Active)
4
MAT 021C
Calculus (Active)
4
MAT 021D
Vector Analysis (Active)
4
MAT 022A
Linear Algebra (Active)
3
ECS 060
Data Structures and Programming (Discontinued)
4
Any one introductory statistics course; except STA 010.
4
Depth Subject Matter
52
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 131A
Introduction to Probability Theory (Active)
4
STA 131B
Introduction to Mathematical Statistics (Active)
4
Choose two:
8
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
One approved four unit course:
4
STA 194HA
Special Studies for Honors Students (Active)
4
STA 194HB
Special Studies for Honors Students (Active)
4
STA 199
Special Study for Advanced Undergraduates (Active)
1-5
Programming, Data Management & Data Technologies:
8
ECS 130
Scientific Computation (Active)
4
or
ECS 145
Scripting Languages and Their Applications (Active)
4
ECS 165A
Database Systems (Active)
4
Scientific Computational Algorithm and Visualization; choose two:
8
ECS 122A
Algorithm Design and Analysis (Active)
4
ECS 129
Computational Structural Bioinformatics (Active)
4
ECS 140A
Programming Languages (Active)
4
ECS 158
Programming on Parallel Architectures (Active)
4
ECS 163
Information Interfaces (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
Choose two:
8
MAT 124
Mathematical Biology (Active)
4
MAT 128A
Numerical Analysis (Active)
4
MAT 128B
Numerical Analysis in Solution of Equations (Active)
4
MAT 129
Fourier Analysis (Active)
4
MAT 145
Combinatorics (Active)
4
MAT 148
Discrete Mathematics (Active)
4
MAT 160
Mathematics for Data Analytics and Decision Making (Active)
4
MAT 165
Mathematics and Computers (Active)
4
MAT 167
Applied Linear Algebra (Active)
4
MAT 168
Optimization (Active)
4
Statistical Data Science Track
Units: 79
Preparatory Subject Matter
27
MAT 021A
Calculus (Active)
4
MAT 021B
Calculus (Active)
4
MAT 021C
Calculus (Active)
4
MAT 021D
Vector Analysis (Active)
4
MAT 022A
Linear Algebra (Active)
3
Choose one:
4
ECS 010
Introduction to Programming (Discontinued)
4
ECS 030
Programming and Problem Solving (Discontinued)
4
ECS 040
Software Development and Object-Oriented Programming (Discontinued)
4
One introductory statistics course; except STA 010.
4
STA 032 or STA 100 preferred.
 
Depth Subject Matter
52
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 131A
Introduction to Probability Theory (Active)
4
STA 131B
Introduction to Mathematical Statistics (Active)
4
STA 131C
Introduction to Mathematical Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
ECS 171
Machine Learning (Active)
4
MAT 167
Applied Linear Algebra (Active)
4
or
MAT 168
Optimization (Active)
4
Choose one:
4
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
MAT 128A
Numerical Analysis (Active)
4
ECS 122A
Algorithm Design and Analysis (Active)
4
ECS 158
Programming on Parallel Architectures (Active)
4
ECS 163
Information Interfaces (Active)
4
ECS 165A
Database Systems (Active)
4
One approved four unit course:
4
STA 194HA
Special Studies for Honors Students (Active)
4
STA 194HB
Special Studies for Honors Students (Active)
4
STA 199
Special Study for Advanced Undergraduates (Active)
1-5
Total: 79-84

Thomas (C.M.) Lee, Ph.D., Chairperson of the Program

Ethan Anderes, Ph.D., Vice Chairperson for Graduate Affairs

Program Office. 4118 Mathematical Sciences Building; 530-554-1367; http://www.stat.ucdavis.edu

Faculty. http://www.stat.ucdavis.edu/people/faculty.htm

The Department offers undergraduate majors a path into the Statistics M.S. program through the Integrated Degree Program (I.D.P.). This program is intended for students who seek to be employed as statisticians in government or industry. The minimum major GPA requirement is 3.200 at the end of the junior year, although students with demonstrated excellence in academic work (with a major GPA of 3.500 or above) are most likely to be admitted. Before moving into the graduate phase, I.D.P. students must satisfy all requirements of the B.S. degree.

To apply for the I.D.P., undergraduate students must submit the Statistics I.D.P. form along with supporting documents during the last quarter of their junior year, to enter the I.D.P. in the first quarter of their senior year. In addition, applicants must submit an application to the M.S. program during the senior year, prior to the deadline of the MS application. Students with a major GPA of 3.500 or above may waive the GRE requirement in the M.S. application. Before applying to the I.D.P., students are strongly advised to consult with both the undergraduate and graduate advisors.

Once a student enters the graduate phase of the I.D.P., they follow the course requirements for the Master's degree (44 units, 18 of which are graduate level). A maximum of 6 units taken in the undergraduate phase can be transferred to the M.S. provided they have not been used to satisfy any requirements of the B.S. degree.

Major Advisor. Debashis Paul

Graduate Advisor. Ethan Anderes (Statistics)

(College of Letters and Science)

Thomas Lee, Ph.D., Chairperson of the Department

Department Office. 4118 Mathematical Sciences Building; 530-752-2361; http://www.stat.ucdavis.edu

Faculty. http://www.stat.ucdavis.edu/people/faculty.html

The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods.
Statistics
Units: 20
STA 106
Applied Statistical Methods: Analysis of Variance (Active)
4
STA 108
Applied Statistical Methods: Regression Analysis (Active)
4
STA 130A
Mathematical Statistics: Brief Course (Active)
4
STA 130B
Mathematical Statistics: Brief Course (Active)
4
or
STA 131A
Introduction to Probability Theory (Active)
4
STA 131B
Introduction to Mathematical Statistics (Active)
4
Choose one:
4
STA 101
Advanced Applied Statistics for the Biological Sciences (Active)
4
STA 104
Applied Statistical Methods: Nonparametric Statistics (Active)
4
STA 135
Multivariate Data Analysis (Active)
4
STA 137
Applied Time Series Analysis (Active)
4
STA 138
Analysis of Categorical Data (Active)
4
STA 141A
Fundamentals of Statistical Data Science (Active)
4
STA 141B
Data & Web Technologies for Data Analysis (Active)
4
STA 141C
Big Data & High Performance Statistical Computing (Active)
4
STA 144
Sampling Theory of Surveys (Active)
4
STA 145
Bayesian Statistical Inference (Active)
4
STA 160
Practice in Statistical Data Science (Active)
4
Preparation.
4
STA 013
Elementary Statistics (Active)
4
or
STA 032
Gateway to Statistical Data Science (Active)
4
or
STA 100
Applied Statistics for Biological Sciences (Active)
4
Additional preparatory courses will be needed based on the course prerequisites listed in the catalog.
 
Total: 20
Courses in STA:
STA 010Statistical Thinking (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Two years of high school algebra. Statistics and probability in daily life. Examines principles of collecting, presenting and interpreting data in order to critically assess results reported in the media; emphasis is on understanding polls, unemployment rates, health studies; understanding probability, risk and odds. (Letter.) GE credit: QL, SE. Effective: 2000 Spring Quarter.
STA 012Introduction to Discrete Probability (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): Two years of high school algebra. Random experiments; countable sample spaces; elementary probability axioms; counting formulas; conditional probability; independence; Bayes theorem; expectation; gambling problems; binomial, hypergeometric, Poisson, geometric, negative binomial and multinomial models; limiting distributions; Markov chains. Applications in the social, biological, and engineering sciences. (Letter.) GE credit: QL, SE. Effective: 1999 Fall Quarter.
STA 013Elementary Statistics (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): Two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit for students who have completed STA 013V, or higher. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 013YElementary Statistics (4) Active
Lecture—1.5 hour(s); Web Virtual Lecture—5 hour(s). Prerequisite(s): Two years of high school algebra or Mathematics D. Descriptive statistics; basic probability concepts; binomial, normal, Student's t, and chi-square distributions. Hypothesis testing and confidence intervals for one and two means and proportions. Regression. Not open for credit for students who have completed STA 013, or higher. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 032Gateway to Statistical Data Science (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): MAT 016B or MAT 021B or MAT 017B. Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Alternative to STA 013 for students with a background in calculus and programming. Only two units of credit allowed to students who have taken STA 013; not open for credit to students who have taken STA 100. (Letter.) GE credit: QL, SE. Effective: 2018 Winter Quarter.
STA 090XSeminar (1-2) Active
Seminar—1-2 hour(s). Prerequisite(s): Consent of Instructor. High school algebra. Examination of a special topic in a small group setting. (Letter.) Effective: 1997 Winter Quarter.
STA 098Directed Group Study (1-5) Active
Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 1997 Winter Quarter.
STA 099Special Study for Undergraduates (1-5) Active
Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 2000 Spring Quarter.
STA 100Applied Statistics for Biological Sciences (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): MAT 016B or MAT 017B or MAT 021B. Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. Only two units credit allowed to students who have taken STA 013, STA 032 or 103; not open for credit to students who have taken STA 102. (Letter.) GE credit: QL, SE. Effective: 2017 Spring Quarter.
STA 101Advanced Applied Statistics for the Biological Sciences (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 100. Basic experimental designs, two-factor ANOVA without interactions, repeated measures ANOVA, ANCOVA, random effects vs. fixed effects, multiple regression, basic model building, resampling methods, multiple comparisons, multivariate methods, generalized linear models, Monte Carlo simulations. (Letter.) GE credit: QL, SE. Effective: 2014 Fall Quarter.
STA 103Applied Statistics for Business and Economics (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 013 or STA 013Y or STA 032 or STA 100); (MAT 016B or MAT 017B or MAT 021B). Descriptive statistics; probability; random variables; expectation; binomial, normal, Poisson, other univariate distributions; joint distributions; sampling distributions, central limit theorem; properties of estimators; linear combinations of random variables; testing and estimation; Minitab computing package. Two units credit to students who have completed STA 100. (Letter.) GE credit: QL, SE. Effective: 2018 Winter Quarter.
STA 104Applied Statistical Methods: Nonparametric Statistics (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 013 or STA 013Y or STA 032 or STA 100. Sign and Wilcoxon tests, Walsh averages. Two-sample procedures. Inferences concerning scale. Kruskal-Wallis test. Measures of association. Chi square and Kolmogorov-Smirnov tests. (Letter.) GE credit: QL, SE. Effective: 2018 Winter Quarter.
STA 106Applied Statistical Methods: Analysis of Variance (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 013 or STA 013Y or STA 032 or STA 100. Basics of experimental design. One-way and two-way fixed effects analysis of variance models. Randomized complete and incomplete block design. Multiple comparisons procedures. One-way random effects model. (Letter.) GE credit: SE. Effective: 2018 Winter Quarter.
STA 108Applied Statistical Methods: Regression Analysis (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 013 or STA 013Y or STA 032 or STA 100. Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. (Letter.) GE credit: QL, SE, SL. Effective: 2018 Winter Quarter.
STA 130AMathematical Statistics: Brief Course (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): MAT 016C or MAT 017C or MAT 021C. Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. Only 2 units of credit allowed to students who have taken STA 131A. (Letter.) GE credit: QL, SE. Effective: 2018 Winter Quarter.
STA 130BMathematical Statistics: Brief Course (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 130A or STA 131A or MAT 135A. Transformed random variables, large sample properties of estimates. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. General linear model, least squares estimates, Gauss-Markov theorem. Analysis of variance, F-test. Regression and correlation, multiple regression. Selected topics. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 131AIntroduction to Probability Theory (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): MAT 021B; MAT 021C; MAT 022A or MAT 067. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. Not open for credit to students who have completed MAT 135A. (Letter.) GE credit: QL, SE. Effective: 2018 Winter Quarter.
STA 131BIntroduction to Mathematical Statistics (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131A or MAT 135A; or Consent of Instructor. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. (Letter.) GE credit: SE. Effective: 2017 Winter Quarter.
STA 131CIntroduction to Mathematical Statistics (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131B; or Consent of Instructor. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. (Letter.) GE credit: SE. Effective: 2016 Fall Quarter.
STA 135Multivariate Data Analysis (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 130B or STA 131B); (MAT 022A or MAT 067). Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotellings T2; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 137Applied Time Series Analysis (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 108. Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 138Analysis of Categorical Data (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 130B or STA 131B) or (STA 106, STA 108). Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. Logit models, linear logistic models. Analysis of incomplete tables. Packaged computer programs, analysis of real data. (Letter.) GE credit: QL, SE. Effective: 1997 Winter Quarter.
STA 141AFundamentals of Statistical Data Science (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): (STA 108 or STA 106); (STA 032 or STA 100 or STA 013 or STA 013Y). Introduction to computing for data analysis and visualization, and simulation, using a high-level language (e.g., R). Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Not open for credit to students who have taken STA 141 or STA 242. (Letter.) Effective: 2018 Spring Quarter.
STA 141BData & Web Technologies for Data Analysis (4) Review all entries Historical
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141A. Essentials of using relational databases and SQL. Processing data in blocks. Scraping Web pages and using Web services/APIs. Basics of text mining. Interactive data visualization with Web technologies. Computational data workflow and best practices. Statistical methods. (Letter.) Effective: 2018 Winter Quarter.
STA 141BData & Web Technologies for Data Analysis (4) Review all entries Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141A. Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics. Open to all students during Open Registration. Essentials of using relational databases and SQL. Processing data in blocks. Scraping Web pages and using Web services/APIs. Basics of text mining. Interactive data visualization with Web technologies. Computational data workflow and best practices. Statistical methods. (Letter.) Effective: 2019 Winter Quarter.
STA 141CBig Data & High Performance Statistical Computing (4) Review all entries Historical
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141B or (STA 141A, ECS 010). High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. (Letter.) Effective: 2018 Winter Quarter.
STA 141CBig Data & High Performance Statistical Computing (4) Review all entries Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 141B or (STA 141A, ECS 010). Pass One and Pass Two restricted to Statistics majors and graduate students in Statistics and Biostatistics; open to all students during Open registration. High-performance computing in high-level data analysis languages; different computational approaches and paradigms for efficient analysis of big data; interfaces to compiled languages; R and Python programming languages; high-level parallel computing; MapReduce; parallel algorithms and reasoning. (Letter.) Effective: 2019 Winter Quarter.
STA 144Sampling Theory of Surveys (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (STA 130B or STA 131B) or (STA 106, STA 108). Simple random, stratified random, cluster, and systematic sampling plans; mean, proportion, total, ratio, and regression estimators for these plans; sample survey design, absolute and relative error, sample size selection, strata construction; sampling and nonsampling sources of error. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 145Bayesian Statistical Inference (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 130B or STA 131B. Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis. (Letter.) GE credit: QL, SE. Effective: 2016 Fall Quarter.
STA 160Practice in Statistical Data Science (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; (STA 130B or STA 131B); (STA 141 or STA 141A). Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project. (Letter.) Effective: 2016 Spring Quarter.
STA 190XSeminar (1-2) Active
Seminar—1-2 hour(s). Prerequisite(s): STA 013 or STA 013Y or STA 032 or STA 100 or STA 103. In-depth examination of a special topic in a small group setting. (Letter.) Effective: 2018 Spring Quarter.
STA 192Internship in Statistics (1-12) Active
Internship—3-36 hour(s); Term Paper. Prerequisite(s): Consent of Instructor. Upper division standing. Work experience in statistics. (P/NP grading only.) Effective: 1997 Winter Quarter.
STA 194HASpecial Studies for Honors Students (4) Active
Independent Study—12 hour(s). Prerequisite(s): Senior qualifying for honors. Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. (Letter.) GE credit: SE. Effective: 1997 Winter Quarter.
STA 194HBSpecial Studies for Honors Students (4) Active
Independent Study—12 hour(s). Prerequisite(s): Senior qualifying for honors. Directed reading, research and writing, culminating in the completion of a senior honors thesis or project under direction of a faculty advisor. (Letter.) GE credit: SE. Effective: 1997 Winter Quarter.
STA 198Directed Group Study (1-5) Active
Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 1997 Winter Quarter.
STA 199Special Study for Advanced Undergraduates (1-5) Active
Variable. Prerequisite(s): Consent of Instructor. (P/NP grading only.) Effective: 1997 Winter Quarter.
STA 200AIntroduction to Probability Theory (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): MAT 021A; MAT 021B; MAT 021C; MAT 022A; Consent of Instructor. Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. (Letter.) Effective: 2018 Winter Quarter.
STA 200BIntroduction to Mathematical Statistics I (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200A; or Consent of Instructor. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. (Letter.) Effective: 2018 Winter Quarter.
STA 200CIntroduction to Mathematical Statistics II (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200B; or Consent of Instructor. Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. No credit to students who have taken STA 131C. (Letter.) Effective: 2018 Spring Quarter.
STA 201SAS Programming for Statistical Analysis (3) Active
Lecture—2 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Introductory, upper division statistics course; some knowledge of vectors and matrices; STA 106 or STA 108 or the equivalent suggested. Introductory SAS language, data management, statistical applications, methods. Includes basics, graphics, summary statistics, data sets, variables and functions, linear models, repetitive code, simple macros, GLIM and GAM, formatting output, correspondence analysis, bootstrap. Prepare SAS base programmer certification exam. (Letter.) Effective: 2013 Fall Quarter.
STA 205Statistical Methods for Research with SAS (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): An introductory upper division statistics course and some knowledge of vectors and matrices; STA 100, or STA 102, or STA 103 suggested or the equivalent. Focus on linear statistical models widely used in scientific research. Emphasis on concepts, methods and data analysis using SAS. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. (Letter.) Effective: 2008 Fall Quarter.
STA 206Statistical Methods for Research - I (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Focus on linear statistical models. Emphasis on concepts, method and data analysis; formal mathematics kept to minimum. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, factorial designs and analysis of covariance. Use of professional level software. (Letter.) Effective: 2013 Fall Quarter.
STA 207Statistical Methods for Research II (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 206; Knowledge of vectors and matrices. Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software; formal mathematics kept to minimum. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. (Letter.) Effective: 2013 Fall Quarter.
STA 208Statistical Methods in Machine Learning (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 206; STA 207; STA 135; Or their equivalents. Focus on linear and nonlinear statistical models. Emphasis on concepts, methods, and data analysis; formal mathematics kept to minimum. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Use professional level software. (Letter.) Effective: 2013 Fall Quarter.
STA 209Optimization for Big Data Analytics (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 200A; STA 208. Optimization algorithms for solving problems in statistics, machine learning, data analytics. Review computational tools for implementing optimization algorithms (gradient descent, stochastic gradient descent, coordinate descent, Newton’s method.) (Letter.) Effective: 2018 Spring Quarter.
STA 222Biostatistics: Survival Analysis (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. (Same course as BST 222.) (Letter.) Effective: 2002 Fall Quarter.
STA 223Biostatistics: Generalized Linear Models (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C. Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. (Same course as BST 223.) (Letter.) Effective: 2002 Fall Quarter.
STA 224Analysis of Longitudinal Data (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): ((STA 222, STA 223) or (BST 222, BST 223)); STA 232B; or Consent of Instructor. Standard and advanced methodology, theory, algorithms, and applications relevant for analysis of repeated measurements and longitudinal data in biostatistical and statistical settings. (Same course as BST 224.) (Letter.) Effective: 2005 Spring Quarter.
STA 225Clinical Trials (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 223 or BST 223; or Consent of Instructor. Basic statistical principles of clinical designs, including bias, randomization, blocking, and masking. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Advanced statistical procedures for analysis of data collected in clinical trials. (Same course as BST 225.) (Letter.) Effective: 2005 Spring Quarter.
STA 226Statistical Methods for Bioinformatics (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131C; or Consent of Instructor. Data analysis experience recommended. Standard and advanced statistical methodology, theory, algorithms, and applications relevant to the analysis of -omics data. (Same course as BST 226.) (Letter.) Effective: 2007 Fall Quarter.
STA 231AMathematical Statistics I (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 131A; STA 131B; STA 131C; MAT 025; MAT 125A; Or equivalent of MAT 025 and MAT 125A. First part of three-quarter sequence on mathematical statistics. Emphasizes foundations. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. (Letter.) Effective: 2008 Summer Session 1.
STA 231BMathematical Statistics II (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231A. Second part of a three-quarter sequence on mathematical statistics. Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models. (Letter.) Effective: 2008 Summer Session 1.
STA 231CMathematical Statistics III (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231A; STA 231B. Third part of three-quarter sequence on mathematical statistics. Emphasizes large sample theory and their applications. Topics include statistical functionals, smoothing methods and optimization techniques relevant for statistics. (Letter.) Effective: 2008 Summer Session 1.
STA 232AApplied Statistics I (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131A; STA 131B; STA 131C; MAT 167. Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques. (Letter.) Effective: 2011 Fall Quarter.
STA 232BApplied Statistics II (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131A; STA 131B; STA 131C; STA 232A; MAT 167. Alternative approaches to regression, model selection, nonparametric methods amenable to linear model framework and their applications. (Letter.) Effective: 2011 Fall Quarter.
STA 232CApplied Statistics III (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 106; STA 108; STA 131C; STA 232B; MAT 167. Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. (Letter.) Effective: 2011 Fall Quarter.
STA 233Design Experiments (3) Active
Lecture—3 hour(s). Prerequisite(s): STA 131C. Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. (Letter.) Effective: 1997 Winter Quarter.
STA 235AProbability Theory (4) Active
Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): (MAT 125B, MAT 135A) or STA 131A; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAT 235A.) (Letter.) Effective: 2007 Spring Quarter.
STA 235BProbability Theory (4) Active
Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): STA 235A or MAT 235A; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAt 235B.) (Letter.) Effective: 2008 Spring Quarter.
STA 235CProbability Theory (4) Active
Lecture—3 hour(s); Term Paper/Discussion—1 hour(s). Prerequisite(s): STA 235B or MAT 235B; or Consent of Instructor. Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. Weak convergence in metric spaces, Brownian motion, invariance principle. Conditional expectation. Topics selected from: martingales, Markov chains, ergodic theory. (Same course as MAT 235C.) (Letter.) Effective: 2008 Spring Quarter.
STA 237ATime Series Analysis (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; Or the equivalent of STA 131B. Advanced topics in time series analysis and applications. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. (Letter.) Effective: 1999 Fall Quarter.
STA 237BTime Series Analysis (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; STA 237A; Or the equivalent of STA 131B. Advanced topics in time series analysis and applications. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. (Letter.) Effective: 1999 Fall Quarter.
STA 238Theory of Multivariate Analysis (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 131B; STA 135. Multivariate normal and Wishart distributions, Hotellings T-Squared, simultaneous inference, likelihood ratio and union intersection tests, Bayesian methods, discriminant analysis, principal component and factor analysis, multivariate clustering, multivariate regression and analysis of variance, application to data. (Letter.) Effective: 1999 Fall Quarter.
STA 240ANonparametric Inference (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C recommended. Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of resampling methodology. (Letter.) Effective: 2000 Winter Quarter.
STA 240BNonparametric Inference (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C recommended. Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of re-sampling methodology. (Letter.) Effective: 2000 Winter Quarter.
STA 241Asymptotic Theory of Statistics (4) Active
Lecture—3 hour(s); Term Paper. Prerequisite(s): STA 231C; STA 235A, STA 235B, STA 235C desirable. Topics in asymptotic theory of statistics chosen from weak convergence, contiguity, empirical processes, Edgeworth expansion, and semiparametric inference. (Letter.) Effective: 2000 Spring Quarter.
STA 242Introduction to Statistical Programming (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): STA 130A; STA 130B; or equivalent of STA 130A and STA 130B. Essentials of statistical computing using a general-purpose statistical language. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. (Letter.) Effective: 2009 Winter Quarter.
STA 243Computational Statistics (4) Active
Lecture—3 hour(s); Laboratory—1 hour(s). Prerequisite(s): (STA 130A, STA 130B); (MAT 067 or MAT 167); Or equivalent of STA 130A and 130B, or equivalent of MAT 167 or MAT 067. Numerical analysis; random number generation; computer experiments and resampling techniques (bootstrap, cross validation); numerical optimization; matrix decompositions and linear algebra computations; algorithms (markov chain monte carlo, expectation-maximization); algorithm design and efficiency; parallel and distributed computing. (Letter.) Effective: 2009 Winter Quarter.
STA 250Topics in Applied and Computational Statistics (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): STA 131A; STA 232A recommended, not required. Resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory. May be repeated for credit with consent of graduate advisor. (Letter.) Effective: 2006 Spring Quarter.
STA 251Topics in Statistical Methods and Models (4) Active
Lecture—3 hour(s); Discussion—1 hour(s). Prerequisite(s): STA 231B; Or the equivalent of STA 231B. Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. May be repeated for credit if topics differ; only with consent of the graduate advisor. (Letter.) Effective: 2002 Fall Quarter.
STA 252Advanced Topics in Biostatistics (4) Active
Lecture—3 hour(s); Discussion/Laboratory—1 hour(s). Prerequisite(s): (STA 222 or BST 222); (STA 223 or BST 223). Biostatistical methods and models selected from the following: genetics, bioinformatics and genomics; longitudinal or functional data; clinical trials and experimental design; analysis of environmental data; dose-response, nutrition and toxicology; survival analysis; observational studies and epidemiology; computer-intensive or Bayesian methods in biostatistics. May be repeated for credit with consent of advisor when topic differs. (Same course as BST 252.) (Letter.) Effective: 2002 Fall Quarter.
STA 260Statistical Practice and Data Analysis (3) Active
Lecture/Discussion—3 hour(s). Prerequisite(s): STA 207 or STA 232B; Working knowledge of advanced statistical software and the equivalent of STA 207 or STA 232B. Open to students enrolled in the graduate program in Statistics or Biostatistics, as the class also serves to provide professional service to clients and collaborators who work with the students. Principles and practice of interdisciplinary collaboration in statistics, statistical consulting, ethical aspects, and basics of data analysis and study design. Emphasis on practical consulting and collaboration of statisticians with clients and scientists under instructor supervision. May be repeated up to 1 Time(s). (Letter.) Effective: 2014 Fall Quarter.
STA 280Orientation to Statistical Research (2) Active
Seminar—2 hour(s). Prerequisite(s): Consent of Instructor. Guided orientation to original statistical research papers, and oral presentations in class of such papers by students under the supervision of a faculty member. May be repeated once for credit. May be repeated up to 1 Time(s). (S/U grading only.) Effective: 1999 Spring Quarter.
STA 290Seminar in Statistics (1-6) Active
Variable. Prerequisite(s): Consent of Instructor. Seminar on advanced topics in probability and statistics. (S/U grading only.) Effective: 1997 Winter Quarter.
STA 292Graduate Group in Statistics Seminar (1-2) Active
Seminar—1-2 hour(s). Prerequisite(s): Consent of Instructor. Graduate standing. Advanced study in various fields of statistics with emphasis in applied topics, presented by members of the Graduate Group in Statistics and other guest speakers. (S/U grading only.) Effective: 1997 Fall Quarter.
STA 298Directed Group Study (1-5) Active
Variable—3-15 hour(s). Prerequisite(s): Consent of Instructor. Graduate standing. Special topics in Statistics appropriate for study at the graduate level. May be repeated for credit. (Letter.) Effective: 2004 Spring Quarter.
STA 299Individual Study (1-12) Active
Variable. Prerequisite(s): Consent of Instructor. (S/U grading only.) Effective: 1997 Winter Quarter.
STA 299DDissertation Research (1-12) Active
Variable—3-36 hour(s). Prerequisite(s): Consent of Instructor. Advancement to candidacy for Ph.D. Research in Statistics under the supervision of major professor. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.
STA 390Methods of Teaching Statistics (2) Active
Lecture/Discussion—1 hour(s); Laboratory—1 hour(s). Prerequisite(s): Graduate standing. Practical experience in methods/problems of teaching statistics at university undergraduate level. Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. Emphasis on practical training. May be repeated for credit. (S/U grading only.) Effective: 2004 Spring Quarter.
STA 396Teaching Assistant Training Practicum (1-4) Active
Variable. Prerequisite(s): Consent of Instructor. Graduate standing. May be repeated for credit. (P/NP grading only.) Effective: 1997 Winter Quarter.
STA 401Methods in Statistical Consulting (3) Active
Lecture—3 hour(s); Discussion—1 hour(s). Students must be enrolled in the graduate program in Statistics or Biostatistics. Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. Clients are drawn from a pool of University clients. May be repeated for credit with consent of graduate advisor. (S/U grading only.) Effective: 2006 Spring Quarter.