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STAT 414 focuses on the theory of introductory probability. The course goals are:To learn the theorems of basic probability. To learn applications and methods of basic probability. To develop theoretical problem-solving skills.
STAT 415 follows the content covered in STAT 414 and focuses on the theoretical treatment of statistical inference, including sufficiency, estimation, hypothesis testing, regression, analysis of variance, chi-square tests, and nonparametric methods. The…
This course introduces students to basic knowledge in programming, data management, and exploratory data analysis using SAS software. Students are provided the opportunity to learn a comprehensive set of SAS data-related techniques through lessons, demon…
STAT 481 (Intermediate SAS) builds on the skills and tools learned in STAT 480 (Introduction to SAS) to extend students' SAS programming skills to an intermediate level. Students are provided the opportunity to learn a comprehensive set of SAS data-relat…
STAT 482 (Advanced Topics in SAS) builds on the skills and tools learned in STAT 480 (Introduction to SAS) and STAT 481 (Intermediate SAS). One of the primary goals of the course is to extend students' SAS programming skills to an advanced-level.&nb…
Since its release in 1997, R has emerged as a popular tool for statistical analysis and research. The flexibility and extensibility of R are keys attributes that have driven its adoption. Some of the advantages of R are related to the command line interf…
This graduate level course provides an introduction to the basic concepts of probability, common distributions, statistical methods, and data analysis. It is intended for graduate students who have one undergraduate statistics course and who wish to revi…
This graduate level course offers an introduction into regression analysis. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variabl…
This is a graduate level course in analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated…
The course will cover most of the material in the text, Chapters 1-15. The students will be required to use statistical computer software to complete many homework assignments and the project.
Course ObjectivesTo develop a critical approach to the analysis of contingency tables To examine the basic ideas and methods of generalized linear models To link logit and log-linear methods with generalized linear models To develop basic facility in the…
Students completing this course should be able to:Select appropriate methods of multivariate data analysis, given multivariate data and study objectives; Write SAS and/or Minitab programs to carry out multivariate data analyses; Interpret results of mult…
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and co…
The course examines the methods used in epidemiologic research, including the design of epidemiologic studies and the collection and analysis of epidemiological data. Epidemiology is the study of the distribution and determinants of human disease and hea…
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