# Undergraduate Courses

For details about the class, credits, pre-requisites, and sections, please see the VT Course Catalog by clicking here

## 2004: Introductory Statistics

Fundamental concepts and methods of statistics with emphasis on interpretation of statistical arguments. An introduction to design of experiments, data analysis, correlation and regression, concepts of probability theory, sampling errors, confidence intervals, and hypothesis tests.

## 2964: Field Study

Pass/fail only. Variable credit course.

## 3005-3006: Statistical Methods

**3005:**: Basic statistical methodology: exploratory data techniques, estimation, inference, comparative analysis by parametric, nonparametric, and robust procedures. Analysis of variance (one-way), multiple comparisons, and categorical data.

**3006:** Analysis of variance, simple and multiple, linear and nonlinear regression, analysis of covariance. Use of MINITAB.

## 3094: Introduction to Programming in SAS

Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax, and formats. Other topics include loops, SAS Macros, and PROC IML. Emphasis is placed on using these tools for statistical analyses.

## 3104: Probability and Distributions

Probability theory, including set theoretic and combinatorial concepts; in-depth treatment of discrete random variables and distributions, with some introduction to continuous random variables; introduction to estimation and hypothesis testing.

## 3504: Nonparametric Statistics

Statistical methodology based on ranks, empirical distributions, and runs. One and two sample tests, ANOVA, correlation, goodness of fit, and rank regression, R-estimates and confidence intervals. Comparisons with classical parametric methods. Emphasis on assumptions and interpretation.

## 3604: Statistics for the Social Sciences

Statistical methods for nominal, ordinal, and interval levels of measurement. Topics include descriptive statistics, elements of probability, discrete and continuous distributions, one and two sample tests, measures of association. Emphasis on comparison of methods and interpretations at different measurement levels.

## 3615-3616: Biological Statistics

Descriptive and inferential statistics in a biological context.

**3615:** Fundamental principles, one- and two-sample parametric inference, simple linear regression, frequency data.

**3616:** One- and two-way ANOVA, multiple regression, correlation, nonparametrics, using the MINITAB computer package.

## 3704:Statistics for Engineering Applications

Introduction to statistical methodology with emphasis on engineering experimentation: probability distributions, estimation, hypothesis testing, regression, and analysis of variance.

Only one of the courses 3704, 4604, 4705, and 4714 may be taken for credit.

## 4004: Methods of Statistical Computing

The objective of this course is to develop a fundamental understanding on simulation-based statistical computing method and the necessary programming skills. The course will cover following major components: 1) The programming and statistical analyses using the R statistical programming language; 2) generation of random variable including inverse transformation method, acceptance-rejection method, and transformation method; 3) Monte Carlo method in statistics computing; 4) bootstrap method and permutation test.

## 4024: Communication in Statistical Collaborations

Theory and examples of effective communication in the context of statistical collaborations. Practice developing the communication skills necessary to be effective statisticians using peer feedback and self-reflection. Topics include helping scientists answer their research questions, writing about and presenting statistical concepts to a non-statistical audience, and managing an effective statistical collaboration meeting.

## 4105-4106: Theoretical Statistics

**4105:** Probability theory, counting techniques, conditional probability; random variables, moments; moment generating functions; multivariate distributions; transformations of random variables; order statistics.

**4106:** Convergence of sequences of random variables; central limit theorem; methods of estimation; hypothesis testing; linear models; analysis of variance.

## 4204: Experimental Designs

Fundamental principles of designing and analyzing experiments with application to problems in various subject matter areas. Discussion of completely randomized, randomized complete block, and latin square designs, analysis of covariance, split--plot designs, factorial and fractional designs, incomplete block designs. Project.

## 4214: Methods of Regression Analysis

Multiple regression including variable selection procedures; detection and effects of multicollinearity; identification and effects of influential observations; residual analysis; use of transformations. Non-linear regression, the use of indicator variables, and logistic regression. Use of SAS. Project.

## 4444: Applied Bayesian Statistics

Introduction to Bayesian methodology with emphasis on applied statistical problems: data displaying, prior distribution elicitation, posterior analysis, models for proportions, means and regression.

## 4504: Applied Multivariate Analysis

Non-mathematical study of multivariate analysis. Multivariate analogs of univariate test and estimation procedures. Simultaneous inference procedures. Multivariate analysis of variance, repeated measures, inference for dispersion and association parameters, principal components analysis, discriminant analysis, cluster analysis. Use of SAS. Project.

## 4514: Contingency Table Analysis

Statistical techniques for frequency data. Goodness-of-fit. Tests and measures of association for two-way tables. Log-linear models for multidimensional tables. Parameter estimation, model selection, incomplete tables, ordinal categories, logistic regression. Use of BMDP and SPSS. Project.

## 4524: Sample Survey Methods

Statistical methods for the design and analysis of survey sampling. Fundamental survey designs. Methods of randomization specific to various survey designs. Estimation of population means, proportions, totals, variances, and mean squared errors. Design of questionnaires and organization of a survey. Project.

## 4534: Applied Statistical Time Series Analysis

An applied course in time series analysis. A uniform coverage of both time domain and frequency domain methods that are used in the physical, biological, and social sciences and by applied statisticians.

## 4584: Advanced Calculus for STAT

Introduction to those topics in advanced calculus and linear algebra needed by statistics majors. Infinite sequences and series. Orthogonal matrices, projections, quadratic forms. Extrema of functions of several variables. Multiple integrals, including convolution and nonlinear coordinate changes.

## 4604: Statistical Methods for Engineers

Introduction to statistical methodology with emphasis on engineering applications: probability distributions, estimation, hypothesis testing, regression, analysis of variance, quality control.

Only one of the courses 4604, 4705, and 4714 may be taken for credit.

## 4705-4706: Probability and Statistics for Engineers

Basic concepts of probability and statistics with emphasis on engineering applications.

**4705:** Probability, random variables, distribution theory, sampling distributions, estimation, hypothesis testing.

**4706:** Hypothesis testing, simple and multiple regression, analysis of variance, factorial experiments.

Only one of the courses 4604, 4705, and 4714 may be taken for credit.

## 4714: Probability and Statistics for Electrical Engineers

Introduction to the concepts of probability, random variables, estimation, hypothesis testing, regression, and analysis of variance with emphasis on application in electrical engineering.

Only one of the courses 4604, 4705, and 4714 may be taken for credit.

## 4804 (AAEC 4804): Elementary Econometrics

Economic applications of mathematical and statistical techniques: regression, estimators, hypothesis testing, lagged variables, discrete variables, violations of assumptions, simultaneous equations.

## 4974: Independent Study

Variable credit course.

## 4984: Special Study

Variable credit course.

4984: Statistical Genomics

Statistical methods for bioinformatics and genetic studies, with an emphasis on statistical analysis, assumptions and problem-solving. Topics include: basic concepts of genes and genomes, commonly used statistical methods for gene identification, association mapping and other related problems. Software: R.

## 4994: Undergraduate Research

Variable credit course.

All 4000-level statistics courses may be taken for graduate credit by non-statistics majors. Statistics graduate students may not take 4000-level statistics courses for graduate credit.