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OPEN.ED@PSU is an open educational resource offering free and open access to quality digital materials for teaching, learning, and scholarship.
Learn probability theory fundamentals, applications, and problem-solving skills. Covers probability assignment, discrete and continuous distributions, joint distributions, and functions of random variables.
Theoretical exploration of statistical inference, covering estimation, hypothesis testing, regression, ANOVA, chi-square tests, and nonparametric methods. Develops understanding of advanced statistical concepts and techniques.
Learn essential SAS programming skills for data management and analysis, preparing you for advanced SAS courses and certification exams.
Advance your SAS skills with data management techniques, programming tools, and preparation for the Base Programming Certification Exam. Gain confidence in using SAS for effective data manipulation.
Enhance SAS expertise with advanced programming techniques, statistical procedures, and data management skills for efficient analysis and problem-solving in complex scenarios.
Desarrolla habilidades en R para análisis estadÃstico y gráficos, incluyendo manejo de datos, procedimientos comunes, programación básica y buenas prácticas analÃticas.
Explore advanced R programming for statistical analysis, data visualization, and collaborative research. Builds on STAT 484, covering sophisticated graphics, programming techniques, and best practices for documentation.
Comprehensive introduction to probability, distributions, and data analysis for graduate students. Develop skills in applying statistical methods, interpreting results, and using Minitab software for research and reporting.
Explore regression analysis for predicting dependent variables using sample data. Learn to create mathematical models, interpret results, and apply methods in various scenarios.
Learn advanced ANOVA techniques, including factorial designs, random effects, split plots, and ANCOVA, to enhance your statistical analysis skills for complex experimental data.
Learn to design effective experiments, from basic principles to advanced techniques like factorial designs and response surface methodologies. Gain practical skills in statistical software for data analysis and project completion.
Develop critical analysis skills for contingency tables, explore generalized linear models, and gain practical experience using SAS/R for discrete data analysis.
Explore multivariate statistical methods, including t-tests, ANOVA extensions, dimension reduction, and clustering techniques. Learn to select, implement, and interpret analyses using SAS and Minitab.
Learn sampling design and analysis methods for research and management across various fields, ensuring efficient data summarization and analysis with minimal assumptions and complications.
Explore epidemiological research methods, study designs, and data analysis techniques. Learn to design studies, identify biases, and critically evaluate literature to improve population health outcomes.
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