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

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

Minimum grade of C in

MAT 115 or

MAT 150 or

MAT 151 or

MAT 190 or

MAT 190 or

MAT 191 or

STA 108; or permission of department

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

Minimum grade of C (2.0) or concurrent registration in

MAT 292; or permission of instructor

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

3

###
Prerequisites

Minimum grade of C (2.0) in either

STA 271 or

STA 290; or permission of instructor

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
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

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
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

Minimum grade of C (2.0) in

STA 291; or permission of instructor

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

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

Minimum grade of C (2.0) in

STA 291; or permission of instructor

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

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

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

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

Minimum grade of C (2.0) in either

STA 291 or

STA 580; knowledge of a scientific programming language

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

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

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; or permission of instructor

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

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

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

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

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

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

1–3

1–3