Please note: This is NOT the most current catalog.
Director, 2010-11: Julie Legler (MSCS), biostatistics and latent variable modeling
Faculty, 2010-11: Anthony Becker (Economics), microeconomics, public policy, econometrics, statistics; Sharon Lane-Getaz (MSCS), statistics education; Matthew Richey (MSCS), computational mathematics and software design; Paul Roback (MSCS), Bayesian methods and regression modeling; Katherine Ziegler-Graham (MSCS), biostatistics
Statistics is an interdisciplinary subject whose tools are of primary importance in a variety of disciplines: actuarial science, biology, economics, education, psychology, medicine, law and other social sciences.
OVERVIEW OF THE CONCENTRATION
At St. Olaf, students can combine their interests in statistics with any major and acquire a background that leads to graduate study and abundant career opportunities. To find out more about the statistics concentration, visit the Statistics program.
REQUIREMENTS FOR THE CONCENTRATION
Four courses: two required foundation courses in statistical modeling (plus prerequisites of Calculus I and Introductory Statistics), and two electives (as described below). Concentrators are encouraged to participate in an experiential learning opportunity, such as those available with the Center for Interdisciplinary Research.
1. Required Foundation
Statistics 272: Statistical Modeling
Statistics 316: Advanced Statistical Modeling
In addition, two courses are prerequisites for the required foundation: (1) Mathematics 120 or 121: Calculus I, and (2) either AP Statistics, Statistics 110, Statistics 212, or Economics 263 (or permission of instructor)
Math-Economics double majors can substitute Economics 385: Econometrics for Statistics 316: Advanced Statistical Modeling.
2. Electives (Students choose at least two of the following upper-level courses):
Statistics 276: Design of Experiments
Statistics 282: Topics in Statistics
Statistics 285: Global Health and Biostatistics
Statistics 294: Internship (with prior approval of Statistics Program Director)
Statistics 298: Independent Study
Statistics 322: Statistical Theory (strongly recommended for mathematics majors)
Statistics 398: Independent Research
Economics 385: Econometrics
Mathematics 262: Probability Theory (strongly recommended for mathematics majors)
Mathematics 384: Seminar in Applied Mathematics
Mathematics 390: Mathematics Practicum (with prior approval of Statistics Program Director)
Psychology 230: Psychology Research Methods
3. An Experiential Learning Component (Optional)
Each concentrator is encouraged to participate in experientially-based research experience or employment that takes statistical methods beyond the traditional classroom. This can occur on or off-campus. Prior approval by the director of Statistics and a letter after the fact from a supervisor are required to earn credit. Opportunities during the school year include independent study or consulting as a fellow in the Center for Interdisciplinary Research or during the summer as a member of an Interdisciplinary Research Team.
Note: For students considering graduate school in statistics or a closely related field, the following courses are recommended: Mathematics 126 or 128: Calculus II, Mathematics 220: Elementary Linear Algebra, Mathematics 226: Multivariable Calculus, Mathematics 230: Differential Equations, Mathematics 242: Modern Computational Mathematics, Mathematics 244 and 344: Real Analysis I and II, Computer Science 251-252: Software Design and Implementation.
Credit will not be given for more than one of Statistics 110, Statistics 212, or Statistics 263; credit will not be given for Statistics 110, Statistics 212 or Statistics 263 following Statistics 272.
This course is an introduction to basic concepts in statistics in the spirit of the liberal arts. Students will learn practical applications and the language and reasoning involved in analyzing behavioral and health science data. Topics include central tendency, dispersion, probability, random variables, binomial and normal distributions, estimation and hypothesis testing, contingency tables, analysis of variance, and correlation. Computer applications are integrated throughout. Not recommended for students who have successfully completed a term of calculus. Offered both semesters. Credit will not be given for more than one of Statistics 110, Statistics 212, or Statistics 263; credit will not be given for Statistics 110, Statistics 212, or Statistics 263 following completion of Statistics 272.
A first course in statistical methods for scientists, this course addresses issues for proposing/designing an experiment, as well as exploratory and inferential techniques for analyzing and modeling scientific data. Topics include probability models, exploratory graphics, descriptive techniques, statistical designs, hypothesis testing, confidence intervals, and simple/multiple regression. Prerequisite: Mathematics 120, 121, or equivalent, and an introductory science course. Offered each semester. Credit will not be given for more than one of Statistics 110, Statistics 212, or Statistics 263; credit will not be given for Statistics 110, Statistics 212, or Statistics 263 following completion of Statistics 272.
This course emphasizes skills necessary to understand and analyze data. Topics include descriptive statistics, probability, random variables, sampling theory, estimation, classical hypothesis testing, and practical and theoretical understanding of simple and multiple regression analysis. Applications to economics and business problems use real data, realistic applications, and Minitab for Windows. Written reports link statistical theory and practice with communication of results. Prerequisite: Mathematics 120 or 121, and one of Economics 110-121, or consent of the instructor. Offered each semester. Credit will not be given for more than one of Statistics 110, Statistics 212, or Statistics 263; credit will not be given for Statistics 110, Statistics 212, or Statistics 263 following completion of Statistics 272.
This course takes a case-study approach to the fitting and assessment of statistical models with application to real data. Specific topics include multiple regression, model diagnostics, and logistic regression. The approach focuses on problem-solving tools, interpretation, mathematical models underlying analysis methods, and written statistical reports. Prerequisite: Statistics 110 or 212 or 263, or equivalent preparation or permission of instructor. Offered each semester. May not take Statistics 110, Statistics 212, or Statistics 263 after completion of Statistics 272.
This course explores methods of designing and analysing scientific experiments to address research questions, emphasizing statistical thinking and applications using real data as much as underlying mathematical structures and theory. Topics include completely randomized factorial designs, randomized block designs, split-plot designs, general linear models, and random effects models. Computer applications are integrated throughout. Prerequisite: Statistics 272 or permission of instructor. Offered during 2011-12 and alternate years.
The course involves investigating issues in global health from a quantitative, research-oriented perspective. Additional course material focuses on global public health issues and methods for analyzing health data. Students work on projects with researchers from the World Health Organization in Geneva, Switzerland, where they learn about the global burden of disease and how statisticians and epidemiologists can contribute to finding solutions. Students also have the opportunity to explore the art and culture of Paris for a weekend while in France. Prerequisite: Statistics 272. Offered in 2010-11 and alternate interims.
298 Independent Study
This course extends and generalizes methods introduced in Statistics 272 by introducing generalized linear models (GLMs) and correlated data methods. GLMs cover logistic and Poisson regression, and more. Correlated data methods include longitudinal data analysis and multilevel models. Applications are drawn from across the disciplines. Prerequisite: Statistics 272. Offered annually in the spring semester.
This course is an investigation of modern statistical theory along with classical mathematical statistics topics such as properties of estimators, likelihood ratio tests, and distribution theory. Additional topics include Bayesian analysis, bootstrapping, Markov Chain Monte Carlo, and other computationally intensive methods. Prerequisite: Statistics 272 and Mathematics 262. Offered annually in the fall semester.
Students focus on writing scientific papers, preparing scientific posters, and giving presentations in the context of a specific, year-long, interdisciplinary research project. In addition, this weekly seminar series builds collaborative research skills such as working in teams, performing reviews of statistical literature, consulting effectively, and communicating proficiently. Exposure post-graduate opportunities in statistics is also provided. Open to seniors accepted into the Center for Interdisciplinary Research.
This course provides a comprehensive research opportunity, including an introduction to relevant background material, technical instruction, identification of a meaningful project, and data collection. The topic is determined by the faculty member in charge of the course and may relate to his/her research interests. Prerequisite: Determined by individual instructor. Offered based on department decision.
398 Independent Research
Economics 385 Introduction to Econometrics
Ideal for students interested in applying statistical models to economic problems, this course emphasizes theoretical foundations, mathematical structure, and applications of major econometric techniques, including ordinary least squares, generalized least squares, instrumental variables, simultaneous equation models, limited dependent variables, and time series techniques. Students in the class complete a sophisticated economic research project of their choice. Prerequisites: Statistics 263 or equivalent preparation, and either Economics 261 or Economics 262, or permission of instructor. Offered annually.
Mathematics 262 Probability Theory
This course is an introduction to the the mathematics of randomness. Topics include probabilities on discrete and continuous sample spaces, conditional probability and Bayes' Theorem, random variables, expectation and variance, distributions (including binomial, Poisson, geometric, normal, exponential, and gamma) and the Central Limit Theorem. Students use computers in the exploration of these topics. Prerequisite: Mathematics 126 or 128. Offered each semester.
Mathematics 384 Topics in Applied Mathematics
Students work intensively in a special topic of an applied character. Topics vary from year to year. May be repeated if topics are different. Offered most years.
Mathematics 390 Mathematics Practicum
Students work in groups on substantial problems posed by and of current interest to area businesses and government agencies. The student groups decide on promising approaches to their problem and carry out the necessary investigations with minimal faculty involvement. Each group reports the results of its investigations with a paper and an hour-long presentation to the sponsoring organization. Prerequisite: Permission of instructor. Offered annually during interim.
Psychology 230 Research Methods in Psychology
This course prepares the student with tools for understanding how research studies in psychology are conceptualized, designed, carried out, interpreted, and disseminated to the public. Use of library and Internet resources, ethical guidelines in the conduct of research and the skills of good scientific writing are emphasized. Students work independently and in small groups to design and conduct their own research projects. Prerequisites: Psychology 125; Statistics 110, 212, or 263. Offered each semester.