STAT 501: Regression Methods
This graduate-level course provides an introduction to regression analysis. A researcher is frequently interested in using sample data to study relationships, with the ultimate goal of developing a model to predict the future value of a dependent variable.
STAT 414: Introduction to Probability Theory
STAT 414 focuses on the theory of introductory probability.
STAT 415: Introduction to Mathematical Statistics
STAT 415 builds on the material covered in STAT 414 and concentrates on the theoretical aspects of statistical inference, such as sufficiency, estimation, hypothesis testing, regression, analysis of variance, chi-square tests, and nonparametric approaches. The course aims are:
STAT 480: Introduction to SAS
This course provides students with fundamental skills of programming, data management, and exploratory data analysis using SAS software. Students have the opportunity to study a wide range of SAS data-related procedures through classes, demonstrations, and homework assignments.
STAT 484: Topics in R Statistical Language
The purpose of this course is to become acquainted with the fundamental R toolset for statistical analysis and visualization. Learn to use R to handle and alter data effectively. Learn about some of R's most regularly used statistical methods. Investigate easy programming in R. ....more
STAT 507: Epidemiological Research Methods
The course focuses on epidemiological research procedures, such as study design and data collection and analysis.
STAT 506: Sampling Theory and Methods
The goal of this course is to provide sampling design and analysis approaches that will be beneficial for research and management in a variety of fields. A well-designed sampling technique allows us to summarize and interpret data with fewer assumptions and difficulties.
STAT 510: Applied Time Series Analysis
This course will give students a basic understanding of the nature and basic processes used to analyze such data, you will quickly realize that this is only the first step toward being able to confidently understand what trends may exist within a set of data and the complexities of using this information to make predictions or forecasts.
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