Please note: This is NOT the most current catalog.

Statistics

(Mathematics, Statistics, and Computer Science)

###### http://www.stolaf.edu/depts/statistics/

**Director, 2008-09:** Paul Roback (MSCS), Bayesian methods and regression modeling; Julie Legler (MSCS), biostatistics and latent variable modeling

**Faculty, 2008-09:** Anthony Becker (Economics), microeconomics, public policy, econometrics, statistics; Sharon Lane-Getaz (MSCS), statistics education; Xun Pomponio (Economics), economics; Matthew Richey (MSCS), computational mathematics and software design; James Scott (MSCS), biostatistics, epidemiology; Martha Tibbetts Wallace (MSCS), mathematics education; Suzanne Wisniewski (Economics), economics; 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. 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 our website.

##### INTENDED LEARNING OUTCOMES FOR THE CONCENTRATION

https://www.stolaf.edu/committees/curriculum/ge/learning-outcomes.html

##### 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, and to enroll in QWAC (Quantitative Work Across the Curriculum) modules when offered for courses they are taking.

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 Statistical Methods

Mathematics 390, Practicum (With Prior Approval of Statistics Program Director)

Psychology 231, 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.

4. QWAC Course Credit (Optional)

Courses which offer a 0.25 course credit to add Quantitative Work Across the Curriculum (QWAC) credit should be taken by those concentrating in statistics when available.

Note: For students considering graduate school in statistics or a closely related field, the following courses are recommended: Calculus II, Multivariate Calculus, Linear Algebra, Real Analysis, Differential Equations, Modern Computational Mathematics.

###### Course Sequencing

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 or Statistics 212 following Statistics 263 or Statistics 272; credit will not be given for Statistics 263 following Statistics 212 or Statistics 272.

##### COURSES

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.

212 Statistics for the Sciences

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 both semesters.

This course emphasizes skills necessary to understand and analyze data. Topics include descriptive statistics, probability, and random variables, sampling theory, estimation, and 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.

This course takes a case-study approach to the fitting and assessment of statistical models with application to real data. Specific topics include two-sample comparisons, simple linear regression, multiple regression, model diagnostics, and logistic regression for binary response variables. Students focus 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 Fall and Spring Semesters.

285 Global Health and Biostatistics in Geneva (Abroad)

The course focuses on 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 have the opportunity to tour the WHO facility, meet WHO researchers and learn about the work of statisticians and epidemiologists there. A visit to the International Cancer Research Centre (IARC) in Lyon, France provides an international perspective of issues associated with cancer research. Students also have the opportunity to explore the art and culture of Paris for a weekend while in France.

294 Internship

298 Independent Study

316 Advanced Statistical Modeling

This course extends and generalizes methods introduced in Statistics 272. Topics include generalized linear models, including logistic and Poisson regression. Correlated data methods including longitudinal data analysis and multilevel models are covered. Applications are drawn from across the disciplines. Prerequisite: Statistics 272. Offered 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 may include Bayesian analysis, bootstrapping, Marlov Chain Monte Carlo, and other computationally intensive methods. Prerequisite: Statistics 272 and Mathematics 262. Offered Fall Semester.

394 Internship

396 Directed Undergraduate Research: "Topic Description"

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. Offer based on department decision.

398 Independent Research

related courses

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 introduces the mathematics of randomness and games of chance. Topics include combinatorial analysis, elementary probability measures, conditional probability, random variables, special distributions, expectations, generating functions, and limit theorems. Prerequisite: Mathematics 126 or 128. Offered both semesters.

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 in 2005-06 and alternate years.