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Statistics Director, 2000-01: Michael Kahn, Mathematics, applied statistics Faculty, 2000-01: William Carlson, Economics, statistics and econometrics; Gerald Ericksen, Psychology, statistics and experimental design; Steven Huchendorf, Economics, statistics and econometrics; Richard Kleber, emeritus; probability and mathematical statistics; Miriam Newton, Mathematics, applied statistics; Matthew Richey, Mathematics, computational mathematics and software design; Theodore Vessey, Mathematics, stochastic processes; Martha Tibbetts Wallace, Mathematics, mathematics education Statistics, the art of
designing experiments and making sense of information arising from experiments,
is an interdisciplinary subject whose tools are of primary importance
in a variety of disciplines: agriculture, business, economics, education,
engineering, healthcare, medicine, law and many more. At St. Olaf, a
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 Web site. REQUIREMENTS FOR THE CONCENTRATION St. Olaf College offers a concentration in Statistics designed to introduce the field to students who wish to pursue it either as a primary interest or as a supplement to some other discipline. This concentration may be earned in conjunction with any major. The student should note prerequisites for the listed courses. I. Mathematics 262 and 312. (Normally completed by the end of the junior year.) II. Two additional courses from the categories below, at least one of which must be from category A (for students with a major in Mathematics both must be from Category A and must be beyond the minimum number required for a Mathematics major.) courses within these categories must be chosen from an appropriate department, and must be approved in advance for each student by the director of the Statistics concentration. Those which might qualify include the following:
Course Sequencing Credit will not be given for more than one of Statistics 110, Mathematics 112, or Mathematics 212; credit will not be given for Statistics 110, Mathematics 112, or Mathematics 212 following Statistics 263 or Mathematics 312; credit will not be given for Statistics 263 following Mathematics 312. COURSES Statistics 110 Principles of Statistics 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. GE: MAR. Offered both semesters. Mathematics 112 Elementary Statistics This course introduces students to concepts in statistics using a more mathematical approach than Statistics 110. Topics include descriptive measures, probability, random variables, binomial and normal distributions, estimation and hypothesis testing, contingency tables, analysis of variance, regressions, and correlation. Computer applications using Minitab are integrated throughout. The course is designed for behavioral and health science students. Prerequisite: Mathematics placement recommendation. GE: MAR. Offered both semesters. Mathematics 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, Bayesian inference, simple/multiple regression, and methods for censored data. Prerequisite: Mathematics 119 or 120 or 122 and an introductory science course. Mathematics 262 Probability Theory This course is an introduction to 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. Statistics 263 Statistical Problem Solving 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, 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 119, 120, 122 and one of Economics 110-122, or consent of the instructor. Offered each semester. Statistics 266 Design of Experiments This introduction to the design and analysis of experiments in the behavioral and health sciences emphasizes the analysis of variance and related techniques. Computer applications using SAS and/or SPSS are integrated throughout. Prerequisites: Mathematics 262 or permission of the instructor. Offered Spring Semester only. Statistics 294 Internship Statistics 298 Independent Study Mathematics 312 Mathematical Statistics This 20th-century material has rapidly become a cornerstone of many disciplines. Students examine sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, and correlation and regression. The course utilizes computer applications using Minitab. Prerequisite: Mathematics 262. Offered both semesters. Mathematics 316 Linear Models and Data Analysis This course introduces students to statistics as the art of data analysis via exploratory graphical and numerical methods using current data sets from a variety of disciplines. Topics will be chosen from multiple regression/linear model theory; diagnostic analysis and outlier detection; and log-linear models/logistic regression analysis. Prerequisite: Mathematics 312. Offered Fall Semester only. Economics 385 Introduction to Econometrics Ideal for students interested in applying statistical models, this course emphasizes theoretical foundations, mathematical structure, and applications of major econometric techniques, including ordinary least squares, generalized least squares, dummy variables, non-linear transformations, instrumental variables, simultaneous equation modeling, and time series models. Using extensive national data series and econometric software, students create computer-based case projects and also develop a national econometric model. Offered annually. Prerequisites: Economics 261 and Statistics 263, Mathematics 312, or permission of instructor. Statistics 394 Internship Statistics 398 Independent Research |