Course
This is a second course in biostatistics for students in the graduate Public Health program. The topics include descriptive statistics, probability, standard probability distributions, sampling distributions, point and confidence interval estimation, hypothesis testing, power and same size estimation, one and two-sample parametric and non-parametric methods for analyzing continuous or discrete data, simple linear regression, logistic regression and other multivariate methods. Students will use a statistical software package for data management and statistical analyses. This is a fully online course with its own office hours and discussions. STA 2023 or equivalent is a pre-requisite for this course (see UWF Catalog). It is important to have a good understanding of inferential statistics, such as confidence intervals and test of hypotheses (for two samples). Students must have completed STA 2023 or equivalent in college.
Course
This course will provide further examination of statistics and data analysis beyond an introductory course. Topics covered include data visualization, point, and interval estimation, hypothesis testing of means, variances, and proportions, and linear and logistic regressions. Emphasis will be placed on conducting reproducible research.
Course
This course gives students the opportunity to engage in group and independent research projects. Research topics and materials vary according to instructor. Technical reports and oral presentations are expected of each student.
Course
This course gives students the opportunity to engage in group and independent research projects. Research topics and materials vary according to instructor. Technical reports and oral presentations are expected of each student. Students must have completed 15 hours of graduate course work in the program and have maintained at least a 3.0 GPA. Students must also commit to both fall and spring sections of the course.
Course
This course will provide further examination of statistics and data analysis beyond an introductory course. Topics covered include data visualization, point, and interval estimation, hypothesis testing of means, variances, and proportions, and linear and logistic regressions. Emphasis will be placed on conducting reproducible research.
Course
A second course in statistics for students in the Biological Sciences. Topics covered include analysis of variance, regression analysis, nonparametric statistics, contingency tables. Offered concurrently with STA 5176; graduate students will be assigned additional work. Meets Gordon Rule Applied Mathematics Requirement.
Course
Inferential statistics from an applied point of view. Probability and sampling distributions, confidence intervals and hypothesis testing, ANOVA, correlation, simple and multiple linear regressions. SAS computer techniques. Lab required. Meets Gordon Rule Applied Mathematics Requirement.
Course
STA 2023 covers descriptive statistics, elementary probability theory, and basic statistical procedures, estimation, and inference. In addition to providing basic concepts in the mentioned areas it prepares the student for other more advanced statistical courses that are necessary for research. Meets General Education requirement in Mathematics. Meets Gordon Rule Applied Mathematics Requirement.
Course
Further concepts in design and analysis of planned experiments with emphasis on confounding and fractional replications of factorial experiments; composite designs; incomplete block designs; estimation of variance components.
Course
This is a deeper dive into regression analysis. Students will learn how to construct statistical models and disseminate results to a wide audience. There will be a focus on choosing the appropriate modeling strategy for the data and research question(s) at hand as well as the underlying matrix algebra.