## Undergraduate Statistics Courses

## 10. Statistical Thinking (4)

Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: 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. GE credit: SciEng or SocSci, Wrt | QL, SE.

## 12. Introduction to Discrete Probability (4)

Lecture—3 hours; laboratory—1 hour. Prerequisite: 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. Offered in alternate years. GE credit: SciEng | QL, SE.

## 13. Elementary Statistics (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: two years of high school algebra or the equivalent in college. 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 to students who have completed course 13V or higher. GE credit: SciEng | QL, SE.

## 13Y. Elementary Statistics (4)

Lecture—1.5 hours; web virtual lecture—5 hours. Prerequisite: two years of high school algebra or the equivalent in college. 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 course 13, or higher. GE credit: SciEng | QL, SE.

## 32. Introductory Statistical Analysis Through Computers (4)

Lecture—3 hours; laboratory—1 hour. Prerequisite: Mathematics 16B or 17C or 21B; ability to program in a high-level programming language. Probability concepts: Events and sample spaces; random variables; mass, density and distribution functions; parametric families; parameter estimation and confidence intervals; hypothesis testing; Central Limit Theorem. Recommended as alternative to course 13 for students with a background in calculus and programming. Only two units of credit allowed to students who have taken course 13, or 102; not open for credit to students who have taken course 100. GE credit: SciEng | QL, SE.

## 90X. Seminar (1-2)

Seminar--1-2 hours. Prerequisite: high school algebra and consent of instructor. Examination of a special topic in a small group setting.

## 98. Directed Group Study (1-5)

Prerequisite: consent of instructor. (P/NP grading only.)

## 99. Special Study for Undergraduates (1-5)

Prerequisite: consent of instructor. (P/NP grading only.)

## 100. Applied Statistics for Biological Sciences (4)

Lecture—3 hours; laboratory—1 hour. Prerequisite: Mathematics 16B or 17C or 21B. 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 course courses 13, 32 or 103; not open for credit to students who have taken course 102. GE credit: SciEng | QL, SE.

## 101. Advanced Applied Statistics for the Biological Sciences

Lecture—3 hours; laboratory—1 hour. Prerequisite: course 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. GE credit: SciEng | SE, QL.*—S. (S.)*

## 103. Applied Statistics for Business and Economics (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 13, 32, or 100; and Mathematics 16B or 17C or 21B. 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 given to students who have completed course 100. GE credit: SciEng | QL, SE.

## 104. Applied Statistical Methods: Nonparametric Statistics (4)

Lecture—3 hours; laboratory—1 hour. Prerequisite: course 13, 32, or 102; course 100 may replace courses 13, 32, or 102. Sign and Wilcoxon tests, Walsh averages. Two-sample procedures. Inferences concerning scale. Kruskal-Wallis test. Measures of association. Chi square and Kolmogorov-Smirnov tests. Offered in alternate years. GE credit: SciEng | QL, SE.

## 106. Applied Statistical Methods: Analysis of Variance (4)

Lecture—4 hours. Prerequisite: course 13, 32, or 102; course 100 may replace courses 13, 32, or 102. One-way and two-way fixed effects analysis of variance models. Randomized complete and incomplete block design, Latin squares. Multiple comparisons procedures. One-way random effects model. GE credit: SciEng | QL, SE, SL.

## 108. Applied Statistical Methods: Regression Analysis (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 13, 32, or 102; course 100 may replace courses 13, 32, or 102. Simple linear regression, variable selection techniques, stepwise regression, analysis of covariance, influence measures, computing packages. GE credit: SciEng | QL, SE, SL.

## 130A. Mathematical Statistics: Brief Course (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: Mathematics 16B or 17C or 21B. 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 course 131A. GE credit: SciEng | QL, SE.

## 130B. Mathematical Statistics: Brief Course (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 130A or 131A or Mathematics 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. GE credit: SciEng | QL, SE

## 131A. Introduction to Probability Theory (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: Mathematics 21A, 21B, 21C, and 22A. 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 Mathematics 135A. GE credit: SciEng | QL, SE.

## 131B. Introduction to Mathematical Statistics (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 131A or consent of the instructor. Sampling, methods of estimation, sampling distributions, confidence intervals, testing hypotheses, linear regression, analysis of variance, elements of large sample theory and nonparametric inference. GE credit: SciEng | QL, SE.

## 131C. Introduction to Mathematical Statistics (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 131B, or consent of the instructor. Sampling, methods of estimation, sampling distributions, confidence intervals, testing hypotheses, linear regression, analysis of variance, elements of large sample theory and nonparametric inference. GE credit: SciEng | SE, QL.

## 135. Multivariate Data Analysis (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 130B or 131B; and Mathematics 22A or 67. Multivariate normal distribution; Mahalanobis distance; sampling distributions of the mean vector and covariance matrix; Hotelling's T^{2}; simultaneous inference; one-way MANOVA; discriminant analysis; principal components; canonical correlation; factor analysis. Intensive use of computer analyses and real data sets. GE credit: SciEng | QL, SE.

## 137. Applied Time Series Analysis (4)

Lecture—3 hours; term paper. Prerequisite: course 108 or the equivalent. Time series relationships, cyclical behavior, periodicity, spectral analysis, coherence, filtering, regression, ARIMA and state-space models; Applications to data from economics, engineering, medicine environment using time series software. GE credit: SciEng | QL, SE.

## 138. Analysis of Categorical Data (4)

Lecture—3 hours; discussion—1 hour. Prerequisite: course 130B or 131B, or courses 106 and 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. GE credit: SciEng | QL, SE.

## 141. Statistical Computing (4) *(Discontinued)*

Lecture--3 hours; laboratory--1 hour. Prerequisite: one introductory class in Statistics (such as 13, 32, 100, or 102), or the equivalent. Organization of computations to access, transform, explore, analyze data and produce results. Concepts and vocabulary of statistical/scientific computing.

## 141A. Fundamentals of Statistical Data Science (4) *(New Course!)*

Lecture--3 hours; laboratory--1 hour. Prerequisite: Course 10 or course 13 or course 32 or course 100. 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 course 141 or course 242.

## 141B. Data & Web Technologies for Data Analysis (4) *(New Course!)*

Lecture--3 hours; laboratory--1 hour. Prerequisite: Course 141A or ECS 145. 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.

## 141C. Big Data & High Performance Statistical Computing (4) *(New Course!)*

Lecture--3 hours; laboratory--1 hour. Prerequisite: Course 141A or ECS 40. 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.

## 144. Sampling Theory of Surveys (4)

Lecture—3 hours; discussion/laboratory—1 hour. Prerequisite: course 130B or 131B. 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. Offered in alternate years. GE credit: SciEng | QL, SE.

## 145. Bayesian Statistical Inference (4)

Lecture—3 hours; laboratory—1 hour. Prerequisite: courses 130A and 130B, or 131A and 131B, or the equivalent. 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. Offered in alternate years. GE credit: SciEng | QL, SE.

## 160. Practice in Statistical Data Science (4) *(New Course!)*

Lecture—3 hours; discussion—1 hour. Prerequisite: course 106; course 108; course 130B or course 131B; course 141 or course 141A. Principles and practice of interdisciplinary, collaborative data analysis; complete case study review and team data analysis project.

## 190X. Seminar (1-2)

Seminar--1-2 hours. Prerequisite: one of courses 13, 32, 100, 102, or 103. In-depth examination of a special topic in a small group setting.

## 192. Internship in Statistics (1-12)

Internship--3-36 hours; term paper. Prerequisite: upper division standing and consent of instructor. Work experience in statistics. (P/NP grading only.)

## 194HA-194HB. Special Studies for Honors Students (4-4)

Independent study--12 hours. Prerequisite: 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 adviser. (Deferred grading only, pending completion of sequence.)

## 198. Directed Group Study (1-5)

Prerequisite: consent of instructor. (P/NP grading only.)

## 199. Special Study for Advanced Undergraduates (1-5)

Prerequisite: consent of instructor. (P/NP grading only.)