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Time series data are intriguing yet complicated information to work with. While this course will provide students with a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is a small first…
STAT 580 (2 credits) and STAT 581 (1 credit) are two separate courses that are taken one after the other; STAT 580 one semester followed by STAT 581 a subsequent semester. Students enroll in the online courses at the end of their program of study. (…
Users of statistics -- researchers, government agencies like the Census Bureau and the Bureau of Labor Statistics, companies like the automakers and drug industry, etc. -- make extensive use of the computer in applying statistical methods to their proble…
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.
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