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Principal Course Distribution Requirement

Principal courses offer introductions to the breadth of disciplines in the College. They acquaint students with the subject matter in an area, with the types of questions that are asked about that subject matter, with the knowledge that has been developed and is now basic to the area, and with the methods and standards by which claims to truth are judged.

Students must complete courses in topical groups in three major divisions (humanities, natural sciences and mathematics, and social sciences). For the B.A., three courses are required from each division, with no more than one course from any topical group. The B.G.S. requires two courses from each division, with no more than one from any topical group. To fulfill the requirement, a course must be designated as a principal course according to the codes listed below.

These are the major divisions, their topical subgroups, and the codes that identify them:

Humanities

  • HT: Historical studies
  • HL: Literature and the arts
  • HR: Philosophy and religion

Natural Sciences and Mathematics

  • NB: Biological sciences
  • NE: Earth sciences
  • NM: Mathematical sciences
  • NP: Physical science

Social Sciences

  • SC: Culture and society
  • SI: Individual behavior
  • SF: Public affairs

No course may fulfill both a principal course distribution requirement and a non-Western culture or second-level mathematics course requirement. Laboratory science courses designated as principal courses may fulfill both the laboratory science requirement and one of the distribution requirements. No free-standing laboratory course may by itself fulfill either the laboratory science requirement or a principal course requirement. Students should begin taking principal courses early in their academic careers. An honors equivalent of a principal course may fulfill a principal course requirement.

View all approved principal course distribution courses »

Non-Western Culture Requirement

A non-Western culture course acquaints students with the culture, society, and values of a non-Western people, for example, from Asia, the Pacific Islands, the Middle East, or Africa. Students must complete one approved non-Western culture course.

One approved non-Western culture course is required. Occasionally courses with varying topics fulfill the non-Western culture course requirement. See the Schedule of Classes for details. These courses are coded NW.

View all approved non-Western culture courses »

Transfer and Earned Credit Course Codes

These codes are used to evaluate transfer credit and to determine which academic requirements a course meets.

  • H: Humanities
  • N: Natural Sciences and Mathematics
  • S: Social Sciences
  • W: World Civilization and Culture
  • U: Undesignated Elective Credit (course does not satisfy distribution requirement)

All Biostatistics courses

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Introductory course concerning the concepts of statistical reasoning and the role of statistical principles as the scientific basis for public health research and practice. Prerequisite: Permission of instructor. LEC
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First-semester course of a two-semester introductory statistics course that provides an understanding of the proper application of statistical methods to scientific research with emphasis on the application of statistical methodology to public health practice and research. This course focuses on basic principles of statistical inference with emphasis on one or two sample methods for continuous and categorical data. This course fulfills the core biostatistics requirement. Prerequisite: Calculus or Permission of Instructor. LEC
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This course will utilize statistical packages (SAS and SPSS) for data management and analysis. Collection and management of data along with one, two and multiple sample parametric procedures will be covered for categorical and continuous data. Simple linear regression will also be covered. LEC
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Second level statistics course that provides an understanding of more advanced statistical methods to scientific research with an emphasis on the application of statistical methodology to public health practice, public health research, and clinical research. Special focus will be upon the utilization of regression methodology and computer applications of such methodology. Prerequisite: BIOS 714 or equivalent. LEC
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Methods for designed experiments including one-way analysis of variance (ANOVA), two-way ANOVA, repeated measures ANOVA, and analysis of covariance are emphasized. Post- ANOVA tests, power and testing assumptions required in NOVA are discussed and applied. Outlier detection using robust estimators also are incorporated. Boxplots, histograms and scatterplots are used to display data. Prerequisite: PRE 710/711 or BIOS 714/717 or equivalent. Preferred: BIOS 715. Knowledge of statistical software, basic statistical plotting methods, p-values, two-sample t-test and simple linear regression is assumed. LEC
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This course will study nonparametric methods in many situations as highlighted by the following topics: Students will learn how nonparametric methods provide exact p-values for tests, exact coverage probabilities for confidence intervals, exact experimentwise error rates for multiple comparison procedures, and exact coverage probabilities for confidence bands. This course will be using EXCEL and SAS to conduct various procedures. Prerequisite: Fundamentals of Biostatistics I (BIOS 714) or the equivalent or consent of instructor. LEC
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Simple linear regression, multiple regression, logistic regression, nonlinear regression, neural networks, autocorrelation, interactions, and residual diagnostics. Applications of the methods will focus on health related data. Prerequisite: 1) Fundamentals of Biostatistics I (BIOS 714) or the equivalent and 2) Fundamentals of Biostatistics II (BIOS 717) or Analysis of Variance (BIOS 720) or Permission of the Instructor. LEC
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An intermediate level statistics course that provides an understanding of the more advanced statistical methods to scientific research with emphasis on the application of statistical methodology to clinical research, public health practice, public health research and epidemiology. Prerequisite: BIOS 714, BIOS 715, and BIOS 717 or permission of the instructor. LEC
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This course is an advanced statistical course for students who have had fundamental biostatistics and linear regression. Topics to be covered include Hotelling's T-squared test, MANOVA, principal components, factor analysis, discriminant analysis, canonical analysis, and cluster analysis. More advanced topics such as Multidimensional Scaling or Structural Equation Modeling might be introduced if time allows. Computers will be extensively used through the whole course, and students are suggested to be familiar with some statistical software before taking this course. Although students are allowed to use the software they are comfortable with, SAS will be the primary statistical package used to demonstrate examples in this course. PREREQUISITES: BIOS 730 Applied Linear Regression or equivalents or permission of instructor. LEC
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An introduction to computer programming in the R (S-Plus) and STATA language environments. The students will develop and run codes appropriate for most standard statistical models and related data analyses; develop code to import data of various types; conduct graphical and data analyses; apply appropriate models to investigate patterns and specific hypotheses. Statistical models, statistical computing, programming, R, S-Plus, STATA. Prerequisite: Permission of instructor. LEC
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The design, implementations, analysis, and assessment of controlled clinical trials. Basic biostatistical concepts and models will be emphasized. Issues of current concern to trialists will be explored. Prerequisite: By permission of instructor. LEC
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This is a graduate level course preparing a student for the SAS base programming certification exam. We will cover the topics required for a student to pass the SAS base programming certification exam given by SAS. To this end, topics we will study will include, referencing files and setting options, creating list reports, understanding data step processing, creating and managing variables, reading and combining SAS data sets, do loops, arrays, and reading raw data from files. After the completion of the course the student should be able to create SAS programs to read data from external files, manipulate the data into variables to be used in an analysis, generate basic reports showing the results. Prerequisite: Permission of the Instructor LEC
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This is a graduate level course preparing a student for the SAS advanced programming certification exam. We will cover the topics required for a student to pass the SAS advanced programming certification exam given by SAS. To this end, topics we will study include array processing, use of data step views, using the data step to write SAS programs, efficient use of the sort procedure, introduction to the macro language in SAS, and accessing data using SAS PROC SQL. After the completion of the course the student should be able to create SAS programs to read data from external files, manipulate the data into variables to be used in an analysis, generate basic reports showing the results. Prerequisites: BIOS 820 or equivalent (SAS Certified BASE programmer for SAS or at least one year of experience as a data analyst/programmer). LEC
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This course is an introduction to nonparametric statistical methods for data that do not satisfy the normality or other usual distributional assumptions. We will cover most of the popular nonparametric methods used for different scenarios, such as a single sample, two independent or related samples, three or more independent or related samples, goodness-of-fit tests, and measures of association. Power and sample size topics will also be covered. The course will cover the theoretical basis of the methods at an intermediate mathematical level, and will also present applications using real world data and statistical software. Prerequisite: Permission of instructor. LEC
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The emphasis of this course is on learning the basics of experimental design and the appropriate application and interpretation of statistical analysis of variance techniques. Prerequisite: Permission of instructor, BIOS 820 recommended. LEC
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Students will be introduced to the design and analysis techniques when sampling from finite populations using simple, stratified, multistage, systematic, and complex sampling designs. Prerequisites: BIOS 830 and BIOS 872 or by permission of instructor. LEC
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This course provides an understanding of both the mathematical theory and practical applications for the analysis of data for response measures that are ordinal or nominal categorical variables. This includes univariate analysis, contingency tables, and generalized linear models for categorical response measures. Regression techniques covered for categorical response variables, such as logistic regression and Poisson regression methods, will include those categorical and/or continuous explanatory variables, both with and without interaction effects. Prerequisite: By permission of instructor; BIOS 820 and BIOS 840 are recommended. LEC
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This course is an introduction to model building using regression techniques. We will cover many of the popular topics in Linear Regression including: simple linear regression, multiple regression, model selection and validation, diagnostics and remedial measures. Throughout the semester, we will be utilizing primarily SAS. Prerequisite: By permission of the instructor. LEC
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This course provides an understanding of both the mathematical theory and practical applications for the analysis of time to event data with censoring. This includes univariate analysis, group comparisons, and regression techniques for survival analysis. Parametric and semi-parametric regression techniques covered will include those with categorical and/or continuous explanatory variables, both with and without interaction effects. Prerequisites: BIOS 820, 835, 840, and 871, or by permission of instructor. LEC
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This course will introduce the theory and methods of applied multivariate analysis. As the field of multivariate analysis is very wide and well developed, the course will focus on those methods that are more frequently used in biostatistical applications. Some knowledge of basic matrix algebra is necessary and will be reviewed as the course progresses. Theoretical exercises and analysis of data sets will be assigned to the student. Emphasis will be on biostatistical applications. Prerequisites: BIOS 820 Statistical Computing, BIOS 830 Experimental Design, BIOS 840 Linear Regression. LEC
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This course introduces the fundamentals of probability theory, random variables, distribution and density functions, expectations, transformations of random variables, moment generating functions, convergence concepts, sampling distributions, and order statistics. Prerequisite: By permission of instructor. LEC
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This course introduces the fundamentals of statistical estimation and hypothesis testing, including point and interval estimation, likelihood and sufficiency principles, properties of estimators, loss functions, Bayesian analysis, and asymptotic convergence. Prerequisite: BIOS 871 or by permission of instructor. LEC
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This course introduces Bayesian theory and methods for data analysis. The course includes an overview of the Bayesian approach to statistical inference, performance of Bayesian procedures, Bayesian computational issues, model criticism, and model selection. Case studies from a variety of fields are incorporated into the course. Implementation of models using Markov chain Monte Carlo methods is emphasized. Prerequisites: BIOS 871 and 872 or by permissions of instructor; BIOS 820 recommended. LEC
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This course introduces the theory and methods of linear models for data analysis. The course includes the theory of general linear models including regression models, experimental design models, and variance component models. Least squares estimation, the Gauss-Markov theorem, and less than full rank hypotheses will be covered. Prerequisites: BIOS 871 and BIOS 872 or by permission of instructor; BIOS 820 recommended. LEC
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Students will conduct a statistically oriented research project focused on the ascertainment of public health data to answer a relevant public health question or concern under the guidance of faculty in the Department of Biostatistics and in collaboration with public health and/or clinical translational researchers. An MPH practicum contract, an outline of a research report, oral presentation and other appropriate deliverables will be developed. This is similar in scope to the BIOS 898 Collaborative Experience offered to M.Sc. and Ph.D. students. Prerequisites: Completion of all Biostatistical and MPH core courses and consent of instructor. LEC
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Students will conduct a statistically oriented research project focused on the analysis of public health data under the guidance of faculty in the Department of Biostatistics. A research report, oral presentation and other appropriate deliverables will be developed. (This course may be repeated for a total of six credit hours.) Prerequisites: Completion of BIOS 891 and consent of instructor. LEC
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This course provides students with experience in collaborative research under the supervision of an experienced researcher. The student will spend one semester working under an investigator or faculty member, making independent contributions to a research project. Prerequisites: BIOS 820, 830, 835, 840, 871, 872, and 890 or by permission of instructor. LEC
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This course on Generalized Linear Models (GLM) is designed for both the applied and theoretical statistician. In this course we introduce the theoretical foundations and key applications of generalized linear models. Prerequisites: BIOS 835 Categorical Data Analysis, BIOS 840 Linear Regression, BIOS 890 Linear Models or by permission of instructor. LEC
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A longitudinal study is a research study that involves repeated observations of the same individuals and events over extended periods of time. It is typically a type of observational study, though may have design components. In medical settings these studies and related models are used to observe the developmental path of a disease or treatment through time. Often this is in the context of follow-up and long-term study of both progress and potential side-effects. As the study involves the same individuals (subject to drop-out) through several time points, statistical methods must employ random effects or "mixed models" incorporating various correlation structures. This is typically done using generalized estimating equations and marginal model approaches. Bayesian methods may also be appropriate here. Students will, after completing this course, be able to design and analyze longitudinal studies. The computer package to be employed is SAS. Prerequisites: BIOS 820, BIOS 830, BIOS 840, BIOS 871, BIOS 872, and BIOS 890 or by permission of instructor. LEC
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Latent variables refer to random variables whose realization values are not observable or cannot be measured without error, and their inferences rely on statistical models connecting latent and other observed variables. This course aims to introduce a family of such statistical models and their applications in biomedical and public health research. The course is designed as an elective course for students in the Biostatistics graduate program. We will use the statistical packages of M-plus, R, and/or SAS for the course. Prerequisite: BIOS 835 and BIOS 890, or by permission of instructor. Familiarity with vectors and matrices is strongly encouraged. LEC
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