Statistics Courses (STA)

STA 108 Elementary Introduction to Probability and Statistics

Survey of statistics intended for undergraduates in any discipline. Graphical displays, numerical measures, relationships between variables, elements of good data collection. Basic probability, introduction to inferential techniques including confidence intervals and significance testing. Emphasis on statistical literacy.

3

STA 271 Fundamental Concepts of Statistics

Survey of basic descriptive and inferential statistics. Graphs and descriptive measures, simple linear regression and correlation, data collection, basic probability and probability models, interval estimation and significance testing, analysis of variance, use of statistical software. An appropriate preparation for more advanced statistics courses in any discipline.

3

Prerequisites

Grade of at least C in MAT 150 or STA 108 or permission of department

STA 290 Introduction to Probability and Statistical Inference

Introduction to probability models and statistical inference. Descriptive statistics, basic probability laws, discrete and continuous probability models, sampling distributions, central limit theorem, estimation, hypothesis testing, simple regression, and correlation.

3

Prerequisites

MAT 292 or permission of instructor

STA 291 Statistical Methods

Two-group comparisons, simple and multiple regression, one and two factor ANOVA, categorical data analysis, nonparametric methods.

3

Prerequisites

STA 271 or STA 290 or permission of instructor

STA 351 Probability

Basic probability theory; combinatorial probability, conditional probability and independent events; univariate and multivariate probability distribution functions and their properties.

3

Prerequisites

Grade of at least C in MAT 292

STA 352 Statistical Inference

Descriptive and inferential statistics. Emphasis on sampling distributions; theory of estimation and tests of hypotheses, linear hypothesis theory, regression, correlation and analysis of variance.

3

Prerequisites

Grade of at least C in STA 290 or permission of instructor

STA 375 Statistical Data Mining

Introduction to statistical methods for data mining; classification and prediction methods using regression and discrimination techniques; clustering methods using distance, linkage, hierarchical methods. Using statistical software to perform data mining.

3

Prerequisites

Grade of at least C in STA 291

STA 382 Introduction to Sampling Methods

Designing survey instruments; estimation of population mean, total, and proportion using simple random, stratified, systematic, and cluster sampling; other sampling techniques such as pps sampling and randomized response methods.

3

Prerequisites

STA 291 or permission of instructor

STA 383 Introduction to Nonparametric Methods

One and two sample permutation and rank tests, k-sample tests, tests of association, contingency table analysis, nonparametric bootstrapping.

3

Prerequisites

STA 291 or permission of instructor

STA 481 Introduction to Design of Experiments

Planning and analysis of experimental and observational studies. Completely randomized, blocked, split-plot, and repeated measures designs. Factorial arrangements and interaction. Power and sample size calculation.

3

Prerequisites

STA 291 or permission of instructor

STA 482 Introduction to Time Series Models

Estimation/removal of trend and seasonality, introduction to stationary stochastic processes, fitting ARMA/ARIMA models, forecasting techniques, miscellaneous topics, and introduction to a time series modeling software package.

3

Prerequisites

STA 352 or permission of instructor

STA 551 Introduction to Probability

Events and probabilities (sample spaces), dependent and independent events, random variables and probability distribution, expectation, moment generating functions, multivariate normal distribution, sampling distributions.

3

Prerequisites

Grade of at least C in STA 290 and MAT 293 or permission of instructor

STA 552 Introduction to Mathematical Statistics

Point estimation, hypothesis testing, confidence intervals, correlation and regression, small sample distributions.

3

Prerequisites

Grade of at least C in STA 551 or permission of instructor

STA 562 Statistical Computing

Statistical methods requiring significant computing or specialized software. Simulation, randomization, bootstrap, Monte Carlo techniques; numerical optimization. Extensive computer programming involved. This course does not cover the use of statistical software packages.

3

Prerequisites

STA 291 or STA 580 and knowledge of a scientific programming language

STA 565 Analysis of Survival Data

Methods for comparing time-to-event data, including parametric and nonparametric procedures for censored or truncated data, regression model diagnostics, group comparisons, and the use of relevant statistical computing packages.

3

Prerequisites

STA 291 or STA 352 or permission of instructor

STA 571 Statistical Methods for Research I

Introduction to statistical concepts. Basic probability, random variables, the binomial, normal and Student's t distributions, hypothesis tests, confidence intervals, chi-square tests, introduction to regression, and analysis of variance.

3

Corequisites

STA 571L

STA 571L Statistical Methods Laboratory I

Using statistical software packages for data analysis. Problems parallel assignments in STA 571.
1

Corequisites

STA 571

STA 572 Statistical Methods for Research II

Statistical methodology in research and use of statistical software. Regression, confidence intervals, hypothesis testing, design and analysis of experiments, one- and two-factor analysis of variance, multiple comparisons, hypothesis tests.

3

Prerequisites

STA 571 and STA 571L or permission of instructor

Corequisites

STA 572L

STA 572L Statistical Methods Laboratory II

Using statistical software packages for data analysis. Problems parallel assignments in 572.
1

Prerequisites

STA 571 and STA 571L or permission of instructor

Corequisites

STA 572

STA 573 Theory of Linear Regression

Linear regression, least squares, inference, hypothesis testing, matrix approach to multiple regression. Estimation, Gauss-Markov Theorem, confidence bounds, model testing, analysis of residuals, polynomial regression, indicator variables.

3

Prerequisites

Grade of at least C in STA 352 and MAT 310, or STA 662, or permission of instructor

STA 574 Theory of the Analysis of Variance

Multivariate normal distribution, one-way analysis of variance, balanced and unbalanced two-way analysis of variance, empty cells, multiple comparisons, special designs, selected topics from random effects models.

3

Prerequisites

Grade of at least C in STA 573 or permission of instructor

STA 575 Nonparametric Statistics

Introduction to nonparametric statistical methods for the analysis of qualitative and rank data. Binomial test, sign test, tests based on ranks, nonparametric analysis of variance, nonparametric correlation and measures of association.

3

Prerequisites

Grade of at least C in STA 352 or STA 572 or STA 662, or permission of instructor

STA 580 Biostatistical Methods

Statistical methods for biological research including: descriptive statistics; probability distributions; parametric and nonparametric tests; ANOVA; regression; correlation; contingency table analysis.

3

Prerequisites

Grade of at least C in STA 271 or STA 290, or permission of instructor

STA 581 SAS System for Statistical Analysis

Creating, importing, and working with SAS data sets. Using SAS procedures for elementary statistical analysis, graphical displays, and report generation.

1

Prerequisites

STA 271 or STA 290 or similar introductory statistics course

STA 589 Experimental Course

This number reserved for experimental courses. Refer to the Course Schedule for current offerings.

STA 593 Directed Study in Statistics

1–3

STA 594 Directed Study in Statistics

1–3

 

Powered by SmartCatalog IQ