# University of Wisconsin Stout | Wisconsin's Polytechnic University

## READY

_{}

That’s how employers describe UW-Stout graduates. Our innovative, career-focused degrees combine applied learning and the liberal arts.

Home |
Calendar |
Directories |
Maps |
Class Search |
Contact Us |
Shop |
Library |
Logins |
One Stop

That’s how employers describe UW-Stout graduates. Our innovative, career-focused degrees combine applied learning and the liberal arts.

Home
/
University Bulletins
/ Course Description Listing

- STAT- 130 Elementary Statistics
- STAT- 320 Statistical Methods
- STAT- 330 Probability And Statistics For Engineering And The Sciences
- STAT- 331 Probability And Mathematical Statistics I
- STAT- 332 Probability And Mathematical Statistics Ii
- STAT- 440 Advanced Linear Modeling-Regression And Time Series Analysis
- STAT- 499 Independent Study
- STAT- 520 Statistical Methods
- STAT- 640 Advanced Linear Modeling-Regression And Time Series Analysis
- STAT- 730 Biostatistics I
- STAT- 731 Biostatistics II
- STAT- 740 Multivariate Statistical Analysis

**STAT- 130 Elementary Statistics**
(2.00 cr.)

*Statistics*

Repeatable: No

Concepts and application of probability and statistics: data analysis (graphical displays, numerical summary measures); probability and probability distributions; concepts of statistical inference (estimation and hypothesis testing). Illustrated with output from statistical computing packages. Students may incur incidental expenses for software.

**STAT- 320 Statistical Methods**
(3.00 cr.)

*Statistics*

Repeatable: No

Methods of describing data: graphical methods, numerical summary measures, exploratory data analysis. Probability, probability distributions, expected value. Sampling distributions. Statistical inference: estimation and hypothesis testing for one-sample and two-sample problems. Regression analysis. Demonstrating with standard statistical software packages. Students may incur incidental expenses for software.

**STAT- 330 Probability And Statistics For Engineering And The Sciences**
(3.00 cr.)

Repeatable: No

Exploratory data analysis; basic probability, probability distributions, mathematical expectation, sampling distributions; basic statistical inference (estimation and hypothesis testing); topics in reliability.

**STAT- 331 Probability And Mathematical Statistics I**
(3.00 cr.)

Repeatable: No

Sample spaces. Probability functions for discrete and continuous sample spaces. Conditional probability and independence. Random variables; probability density and cumulative distribution functions; joint, marginal, and conditional distributions. Expected values, moments, and moment-generating functions. Binomial, hypergeometric, Poisson, normal, and gamma distributions.

**STAT- 332 Probability And Mathematical Statistics Ii**
(3.00 cr.)

Repeatable: No

Point estimation. Properties of point estimators: unbiasedness, efficiency, consistency, sufficiency. The method of maximum likelihood. Basic concepts of interval estimation and hypothesis testing. Inference in one-sample and two-sample problems. Simple linear regression analysis; the method of least squares. Goodness-of-fit tests. Analysis of categorical data.

**STAT- 440 Advanced Linear Modeling-Regression And Time Series Analysis**
(3.00 cr.)

Repeatable: No

Multiple regression, inference about regression parameters, remedical regression measures, quantitative and qualitative regression, model selection/validation, nonlinear regression, neural networks, logistic and Poisson regression, generalized linear models, time series, smoothing, stochastic time series, moving average and autoregressive models, auto regressive integrated moving average (ARIMA), estimating and forecasting with time series.

**STAT- 499 Independent Study**
(1.00 - 3.00 cr.)

Repeatable: Yes

Department consent

**STAT- 520 Statistical Methods**
(3.00 cr.)

Repeatable: No

Methods of describing data: graphical methods, numerical summary measures, exploratory data analysis. Probability, probability distributions, expected value. Sampling distributions. Statistical inference: estimation and hypothesis testing for one-sample and two-sample problems. Regression analysis. Demonstrating with standard statistical software packages. Students may incur incidental expenses for software.

**STAT- 640 Advanced Linear Modeling-Regression And Time Series Analysis**
(3.00 cr.)

Repeatable: No

Multiple regression, inference about regression parameters, remedical regression measures, quantitative and qualitative regression, model selection/validation, nonlinear regression, neural networks, logistic and Poisson regression, generalized linear models, time series, smoothing, stochastic time series, moving average and autoregressive models, auto regressive integrated moving average (ARIMA), estimating and forecasting with time series.

Instructor's consent

**STAT- 730 Biostatistics I**
(3.00 cr.)

Repeatable: No

Statistical analysis in biological and health sciences using case study examples. Review of descriptive statistics. Discrete and continuous probability models in biostatistics, parametric and non-parametric inference in biostatistics (estimation and tests of hypotheses), correlation, and linear, polynomial, nonlinear and logistic regression.

**STAT- 731 Biostatistics II**
(3.00 cr.)

Repeatable: No

Advanced statistical analysis of biological data focusing on health science and conservation using case study examples. Design of experiments, single-factor and multi-factor ANOVA, multivariate analysis, multiple linear regression, least-squares estimation, stepwise procedures, partial F-tests, model aptness, data reduction techniques, principles component analysis, discrimination and classification techniques, discriminant and cluster analysis.

**STAT- 740 Multivariate Statistical Analysis**
(3.00 cr.)

Repeatable: No

Aspects of multivariate analysis, matrix algebra, random vectors, graphical techniques and descriptive statistics for multivariate data, multivariate normal, Wishart distribution, inference about mean vectors, confidence regions and simultaneous comparisons of component means, comparison of several multivariate means, multivariate linear regression, principle component and factor analysis, classification-discriminants analysis, clustering and trees.

Back to Course Descriptions