Requirements for the Major
Honors in the Major
Courses
Room 1220 MSC, 1300 University Avenue, Madison, WI 53706; 608/262-2598; office@stat.wisc.edu; www.stat.wisc.edu
Professors Bates, Box (Emeritus), Chappell, Clayton, DeMets, Doksum, Draper (Emeritus), Harris (Emeritus), Johnson, Kurtz, Loh, Miller (Emeritus), Newton, Nordheim, Shao, Tsui, Wahba, Wardrop (Emeritus), Yandell; Associate Professors Fine, Larget, Lin, C. Zhang, Zhu; Assistant Professors Ane, Chung, Keles, Qian, Z. Zhang
Undergraduate advisor for major: Rick Nordheim, 1110 MSC, nordheim@stat.wisc.edu
Faculty diversity liaison: Jun Shao, shao@stat.wisc.edu
Modern statistics is an exciting subject that affects most aspects of modern living. It has been developed to deal rationally and objectively with the uncertainty that accompanies variation in phenomena as highly complex as the interplay of the many factors that affect our environment. It derives a vitality in coping with practical problems arising in all fields of scientific activity, including the social, business, biological, agricultural, medical, natural, and engineering sciences. Investigators' efforts to learn about a specific phenomenon, be it the response of a patient to a certain medical treatment or the effectiveness of a particular instructional program on a student's learning, are impacted by the presence of natural variation. The field of statistics is concerned with valid and efficient ways to learn more about these phenomena in the presence of such variation. It is an inductive science in which information is extracted from sample data in order to draw inferences. This process most often involves planning experiments to ensure that valid answers to questions are obtained from the sample.
The Department of Statistics has a broad program to fulfill many needs of students at all levels. In particular, the department offers several introductory courses designed for nonstatistics majors in various disciplines. Statistics 301 offers an introduction to statistical methods and provide students with techniques immediately applicable to various subject areas. Statistics 301 can be used to satisfy the statistics requirement for many undergraduate majors, and can be used to satisfy the campus QR (quantitative reasoning) B requirement. For students in the College of Letters and Science, Statistics 301 (or any higher-numbered statistics department course) can be used to partially satisfy the B.S. mathematics requirement. In addition, Statistics 301 can fulfill a requirement or prerequisite for a number of graduate programs. Statistics 224 and 324 require one semester of calculus and are designed to provide an introduction to statistical methods for students in engineering.
Statistics 371 is an additional introductory course primarily for students interested in the biological sciences; Statistics 571 is a more advanced version designed mainly for graduate students in the agricultural and life sciences, although it is also suitable for especially motivated undergraduate students. Statistics 541 provides an introduction to statistical methods for students interested in the medical sciences. Statistics 441 is for students in pharmacy.
At the intermediate and advanced levels there is a wide range of courses either for students who wish to pursue statistics as an undergraduate or graduate major or for students who wish to gain further insight into their own area through additional statistical training. In particular, either of the two one-year course sequences, Statistics 309-310 and 311-312, provides an introduction to mathematical statistics for students who have completed three semesters of calculus. The latter sequence is targeted for students in engineering; other students are encouraged to focus on 309-310. The remaining statistics department courses provide in-depth exposures to various special methods of statistics such as regression analysis, design and analysis of experiments, survey sampling, multivariate analysis, and categorical data analysis. These courses generally require completion of an introductory or mathematical statistics course.
In addition to the general degree requirements, a major in statistics must complete:
At least 20 credits in statistics above 302. These credits must include Statistics 309, 310 or equivalents, 424 and other courses from Statistics 333, 349, 351, 411, 421, 426, 456, and 641. (The undergraduate adviser can approve substitutions under appropriate circumstances.)
At least 3 additional credits from Comp Sci 302, 412, or 525.
At least 6 additional credits from the courses Math 340, 443*, 475, 521*, 522*, 629, and 632
*courses recommended
At least 12 credits in an approved (by the student's advisor) area of concentration of application.
An individual course can be used to fulfill only one of the above three requirements. To be accepted as a major, a student must have completed a basic calculus sequence (Math 221-222-234 or equivalent) with at least a 2.0 GPA.
All students must fulfill the L&S requirement of at least 15 credits of upper-level work in the major completed in residence. Courses that count toward this requirement are Statistics 309, 310, 333, 349, 351, 411, 421, 424, 426, 431, 456, 632, and 641
To be considered for acceptance into Honors in the Major in Statistics, a student must have completed Math 221, 222, and 234 (or equivalent) with a grade point average of 3.0 or higher in these three courses. Listed below are the requirements for Honors in the Major in Statistics.
Students should be aware that Honors in the Major is still under development, and thus should not assume that the requirements specified in this catalog are complete or fully described. Students should check with the department's major advisor at least once a year to make sure that requirements have not been modified, as well as to seek guidance about planning the best possible Honors in the Major curriculum that reflects their special interests. Students who pursue Honors in the Major must also earn a minimum cumulative GPA of 3.3 in all course work taken at UW-Madison at the time of graduation.
Most courses listed in the course descriptions section will be offered regularly unless otherwise noted. The department will be making some modifications in the near future. Please check with the department office for information on specific courses.
For a list of graduate courses and programs, see the Graduate School Catalog.
201 Principles of Statistics. I, II; 3 cr (r-E). Need for scientific methods in collecting, reporting, and interpreting information; inferences and decisions in the presence of uncertainty and statistical variation; elements of probability; random variables; Binomial and Normal probability distributions; point and interval estimation of means and proportions; tests of statistical hypotheses; estimation of variances. P: Open to Fr. Stdts may receive degree cr for no more than one of the following crses: Stat 201, 224, and 301.
224 Introductory Statistics for Engineers. I, II; 3 cr (r-N-I). P: Math 221. 2nd Sem Fr St. Stdts may receive degree cr for no more than one of the following: Stat 201, 224, and 301.
301 Introduction to Statistical Methods. I, II, SS; 3 cr (r-N-I). Distributions, measures of central tendency, dispersion and shape, the normal distribution; experiments to compare means, standard errors, confidence intervals; effects of departure from assumption; method of least squares, regression, correlation, assumptions and limitations; basic ideas of experimental design. P: Open to Fr. Stdts may receive degree cr for no more than one of the following: Stat 201, 224, 301, 324, and 371.
302 Introduction to Statistical Methods II. Irr.; 3 cr (N-I). Review of basic principles of statistical inference. Correlation and regression techniques for studying relationship among variables; design and analysis of experiments; treatment of enumerative data; non-parametric statistics; examples of applications in a variety of subjects. P: A statistics course.
309 Introduction to Mathematical Statistics. (Crosslisted with Math) I; 4 cr (N-A). Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation. P: For majors in math and stats, Math 223 or 234.
310 Introduction to Mathematical Statistics. (Crosslisted with Math) II; 4 cr (N-A). Unbiased estimation, maximum likelihood estimation, confidence intervals, tests of hypotheses, Neyman-Pearson fundamental lemma, likelihood ratio test, applications to general linear model and analysis of variance, categorical data analysis, nonparametric methods. P: For majors in Math and Stat, Math 309 or Stat 309.
311 Introduction to Mathematical Statistics. I, II; 4 cr (N-A). Elements of probability, important discrete distributions, acceptance sampling by attributes, sample characteristics, probability distributions and population characteristics, the normal distribution, acceptance sampling plans based on sample means and variances, sampling from the normal, the central limit theorem, point and interval estimation. P: Math 223 or con reg.
312 Introduction to Mathematical Statistics. I, II; 4 cr (N-A). Tests of hypotheses, control charts, goodness of fit tests; order statistics and nonparametric tests; regression theory, analysis of variance. P: For majors in Engineering or Nat Sci, Stat 311.
324 Introductory Applied Statistics for Engineers. I or II; 3 cr (I). Descriptive statistics, probability concepts and distributions, random variables. Hypothesis tests and confidence intervals for one- and two-sample problems. Linear regression, model checking, and inference. Analysis of variance and basic ideas in experimental design. Math 222. Stdts may receive degree cr for no more that one of the following: Stat 201, 224, 301 and 324. Open to Fr.
333 Applied Regression Analysis. I, II, SS; 3 cr (r-N-A). An introduction to regression with emphasis on the practical rather than the theoretical aspects. Begins with fitting a straight line, converts this problem into matrix terms and then proceeds to fitting and evaluation of general linear models. P: Cons inst.
349 Introduction to Time Series. Irr.; 3 cr (N-A). Autocorrelation, elements of spectral analysis; dynamic models; auto-regressive and moving average models; identification and fitting; forecasting; seasonal adjustment; applications in the social sciences and environmental studies. P: Stat 301 or equiv, or cons inst.
351 Introductory Nonparametric Statistics. Irr.; 3 cr (N-A). Distribution free statistical procedures or methods valid under nonrestrictive assumptions: basic tools; counting methods; order statistics, ranks; distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; methods for discrete data with zeros and ties; computer techniques and programs; discussion and comparison with parametric methods. P: Stat 201 or 301 or 224 or cons inst.
371 Introductory Applied Statistics for the Life Sciences. I, II; 3 cr (r-N-I). The course will provide students in the life sciences with an introduction to modern statistical practice. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications. P: Math 112 & 113 or Math 114. Open to Fr. Stdts may receive cr for no more than one of the following crses: Stat 201, 224, 301, 324, & 371.
411 An Introduction to Sample Survey Theory and Methods. I; 3 cr (N-I). An elementary development of the statistical theory (and methods) used to design and analyze the results from sample surveys. Topics: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, unequal probability sampling, sampling on successive occasions, non-sampling errors, analytical sample surveys. For illustration and clarification, examples drawn from diverse areas of application. P: Stat 224, 201, 301 or an equiv intro statistics course.
421 Applied Categorical Data Analysis. II; 3 cr (N-A). Methods of analyzing multidimensional contingency tables, emphasis on practical applications. The use of computing packages for analysis of such data. Model selection, testing goodness of fit, estimation of parameters, measures of association and methods for detecting sources of significance. P: Stat 301 or cons inst.
424 Statistical Experimental Design for Engineers. (Crosslisted with ME) I, II, SS; 3 cr (N-A). Concepts of randomization, blocking, confounding, transformations, replication; block designs, factorial and fractional methodology, evolutionary operation, and response-surface methodology. P: Stat 224.
426 Reliability. (Crosslisted with ME) Irr.; 3 cr (N-A). Engineering reliability, analysis of failure data, estimates of hazard rates and failure distributions for the reliability of components and/or systems, acceptance sampling plans for quality control. P: Stat 224 or cons inst.
431 Introduction to the Theory of Probability. (Crosslisted with Math) I, II, SS; 3 cr (N-A). Probability in discrete sample spaces; combinatorial analysis; conditional probabilities, stochastic independence, Laplace limit theorem, Poisson distribution, laws of large numbers, random variables, central limit theorem, applications. P: Math 223 or 234.
441 Introduction to Biostatistics for Pharmacy. II; 3 cr (I). Introduction to statistical methods used in pharmaceutical and related biomedical applications. Topics include exploratory data analysis of random samples, theory of probability and population reference distributions, statistical inference and hypothesis testing, regression methods, and survival analysis techniques. P: Admission to School of Pharmacy, Pharm.D. prgm.
456 Applied Multivariate Analysis. II; 3 cr (N-A). Theory and applications of multivariate statistical methods. Basic concepts and statistical reasoning which underlie the techniques of multivariate analysis. Ideas rather than derivations stressed although basic models discussed to give the student some feeling for their adequacy in particular situations. Current applications in the functional areas of accounting, finance, marketing and management. Acquaintance with and use of existing computer programs in the multivariate analysis area. P: Gen Bus 304, Stat 314 or equiv.
475 Introduction to Combinatorics. (Crosslisted with Math, Comp Sci) I, II; 3 cr (N-A). Problems of enumeration, distribution, and arrangement. Inclusion-exclusion principle. Generating functions and linear recurrence relations. Combinatorial identities. Graph coloring problems. Finite designs. Systems of distinct representatives and matching problems in graphs. Potential applications in the social, biological, and physical sciences. Puzzles. Problem solving. P: Math 320 or 340 or cons inst.
525 Linear Programming Methods. (Crosslisted with Comp Sci, ISyE, Math) I, II; 3 cr (N-A). Real linear algebra over polyhedral cones; theorems of the alternative for matrices. Formulation of linear programs. Duality theory and solvability. The simplex method and related methods for efficient computer solution. Perturbation and sensitivity analysis. Applications and extensions, such as game theory, linear economic models, and quadratic programming. P: Math 443 or 320 or 340 or cons inst.
541 Introduction to Biostatistics. (Crosslisted with B M I) I; 3 cr (r-N-I). Course designed for the biomedical researcher. Topics include: descriptive statistics, hypothesis testing, estimation, confidence intervals, t-tests, chi-squared tests, analysis of variance, linear regression, correlation, nonparametric tests, survival analysis and odds ratio. Biomedical applications used for each topic. P: Math 221 or equiv or cons inst.
542 Introduction to Clinical Trials I. (Crosslisted with B M I) II; 3 cr (I). Intended for biomedical researchers interested in the design and analysis of clinical trials. Topics include definition of hypotheses, measures of effectiveness, sample size, randomization, data collection and monitoring, and issues in statistical analysis. Statistics graduate students should take Stat 641. P: Stat 541 or equiv or cons inst.
546 Practicum in Clinical Trial Data Analysis and Interpretation. (Crosslisted with B M I) II; 3 cr (I). Provides practice in analysis and interpretation of existing datasets from national and international clinical trials in a variety of diseases. Students will develop a research question, review clinical protocols, and analyze available data to prepare a report. P: Stat 541 or 572 & Stat 542 or 641.
571 Statistical Methods for Bioscience I. (Crosslisted with Forest, Hort) I; 4 cr (r-I). Descriptive statistics, distributions, one- and two-sample normal inference, power, one-way Anova, simple linear regression, categorical data, non-parametric methods; underlying assumptions and diagnostic work. P: College algebra: Grad st or cons inst.
572 Statistical Methods for Bioscience II. (Crosslisted with Forest, Hort) II; 4 cr (I). Continuation of Forestry 571. Polynomial regression, multiple regression, two-way Anova with and without interaction, split-plot design, subsampling, analysis of covariance, elementary sampling, introduction to bioassay. P: Stats/Forestry/Hort 571.
575 Statistical Methods for Spatial Data. I; 3 cr (A). Detecting and quantifying spatial patterns and modeling in the presence of such patterns. Spatial Point Patterns: testing nonrandomness, simulating and characterizing patterns. Lattice Data: spatial autocorrelation and regression. Geostatistics: variograms, ordinary and universal kriging, inference, assessing assumptions, and extensions. P: Stat 333 & 424; or Stat/Forest/Hort 572; or cons inst.
609 Mathematical Statistics I. I; 3 cr (A). Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems. P: Stat 309 or 431, Math 340, Math 521, or equiv or cons inst.
610 Introduction to Statistical Inference. II; 4 cr (P-D). Conditioning, distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparison of estimators, confidence sets, theory of hypothesis tests, introduction to Bayesian inference and nonparametric estimation. P: Stat 309 or Stat 431, Math 521, Math 340 or equiv or cons inst.
632 Introduction to Stochastic Processes. (Crosslisted with Math, ISyE, OTM) I, II; 3 cr (N-A). Markov chains: classification, recurrence, transcience, limit theory. Renewal theory, Markov processes, birth-death processes. Applications to queueing, branching, and other models in science, engineering and business. Topics drawn from semi-Markov processes, martingales, Brownian motion. P: Math 431, or Stat 309 & 310, or Stat 311 & 312, or Stat 313 or 314.
641 Statistical Methods for Clinical Trials. I; 3 cr (N-A). Statistical issues in the design of clinical trials, basic survival analysis, data collection and sequential monitoring. Intended for statistics graduate students; those with medical backgrounds should take Stat 542. P: Math/Stat 310 or equiv or cons inst.
642 Statistical Methods for Epidemiology. II; 3 cr (A). Methods for analysis of case-control, cross sectional, and cohort studies. Covers epidemiologic study design, measures of association, rates, classical contingency table methods, and logistic and Poisson regression. P: Stat 310 or equiv or cons inst.
643 Practicum in Coordinating Center Methods. (Crosslisted with B M I) SS; 3 cr (A). Practicum in the operation of a coordinating center in a clinical trial or epidemiologic study. Covers organization, randomization, forms design and collection, quality control and other operational responsibilities of coordinating centers. P: Stat 641 or 642 or cons inst.
692 Special Topics in Statistics. Irr.; 1-3 cr (A). Content varies. Consult department or Timetable for information. P: Cons inst.
698 Directed Study. I, II, SS; 1-6 cr (A). P: Graded on a Cr/N basis; requires cons inst.
699 Directed Study. I, II, SS; 1-6 cr (A). P: Graded on a lettered basis; requires cons inst.