Director, 2013-14: Paul Roback (MSCS), multilevel models, GLM, classification methods
Faculty, 2013-14: Anthony Becker (Economics), microeconomics, public policy, econometrics, statistics; Laura Boehm (MSCS), spatial data analysis, Bayesian methods; Sharon Lane-Getaz (MSCS), statistics education; Julie Legler (MSCS), biostatistics, GLM, correlated data; Matthew Richey (MSCS), computational mathematics and software design; Katherine Ziegler-Graham (MSCS), biostatistics
With the growing abundance of data gathered in nearly every field, statistical methods have become invaluable for transforming data into useful information. As a subject, statistics is interdisciplinary, spanning the sciences (natural and social), the humanities, and even the arts. Examples of areas of applications include economics, biology, health, education, actuarial sciences, and law. An increasing number of majors and concentrations require or recommend a statistics course.
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 a prerequisite of 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, a prerequisite for the required foundation: either AP Statistics, Statistics 110, Statistics 212, or Statistics 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 courses):
Computer Science 125: Computer Science for Scientists and Mathematicians (taken Spring 2013 or later)
Economics 385: Econometrics
Mathematics 262: Probability Theory (strongly recommended for mathematics majors)
Psychology 230: Research Methods in Psychology
Sociology/Anthropology 371: Foundations of Social Science Research: Quantitative Methods
Statistics 270: Intermediate Statistics for Social Science Research
Statistics 282: Topics in Statistics
Statistics 322: Statistical Theory (strongly recommended for mathematics majors)
3. An Experiential Learning Component (Optional)
Each concentrator is encouraged to participate in experientially-based research or employment that takes statistical methods beyond the traditional classroom. This can occur on or off campus. Prior approval by the director of Statistics Program and a letter after the fact from a supervisor are required to earn credit. Excellent opportunities for experiential learning in statistics are available through academic internships (Statistics 294), the mathematics practicum (Mathematics 390), and the Center for Interdisciplinary Research (CIR) (Mathematics, Statistics, Computer Science 389). As a CIR fellow, students can work during the academic year or summer with faculty on research from a variety of disciplines.
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 I, 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. One laboratory meeting per week. 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 or equivalent. Offered each semester. Enrollment limited for seniors. 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 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 focuses on the use of statistics in a social science context. Students investigate three essential questions: How can one reliably measure something? How does one design valid research? How does one analyze research results? Topics include ANOVA designs (for example, one-way and two-way with interaction), data reduction methods, and principles of measurement. Interdisciplinary groups work together on case studies throughout the term. Prerequisites: Statistics 110 or 212 or 263, or equivalent preparation, or permission of the instructor. Offered fall 2013-14 and alternate years.
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.
Students explore special topics in statistics. Topics vary from year to year. May be repeated if topics are different. Offered periodically.
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. Counts toward neuroscience concentration.
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. Counts toward neuroscience concentration.
Mathematics, Statistics, and Computer Science 389 Research Methods (0.5 credit)
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 math, statistics, and computer science literature, consulting effectively, and communicating proficiently. Exposure to post-graduate opportunities in math, statistics, and computer science disciplines is also provided. Open to students 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. May be offered as a 1.00 credit course or .50 credit course.
398 Independent Research
This course focuses on handling data: visualization, finding patterns, and communicating with data. Exploration of fundamental concepts, including recursion, iteration, algorithm efficiency, loops, decision structures, encapsulation, and computing ethics. Students work individually and in teams to apply basic principles and structures to create programs that model graphical, mathematical, and scientific processes. Prerequisite: Calculus or consent of the instructor.
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 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.
Sociology/Anthropology 371 Foundations of Social Science Research: Quantitative Methods
Students gain the skills necessary to conduct and critically evaluate quantitative research. Students learn the underlying theoretical assumptions and orientations of quantitative research, including research design, sampling techniques, strategies for data collection, and approaches to analysis. Students gain practice in data analysis by conducting a research project and using the Statistical Package for the Social Sciences (SPSS), a standard in sociology. Open to sociology/anthropology majors only. Prerequisite: Statistics 110 or 212. Offered annually in the fall semester. Counts toward environmental studies major (social science track).