Statistics (STAT)

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. This course has embedded support.

This course is an introduction to statistical thinking and processes, including methods and concepts for discovery and decision-making using data. Topics include descriptive statistics; probability and sampling distributions; statistical inference; correlation and linear regression; analysis of variance, chi-squared, and t-tests; and application of technology for statistical analysis including the interpretation of the relevance of the statistical findings. Students apply methods and processes to applications using data from a broad range of disciplines. This is an honors course.

This course is an introduction to the key principles and techniques used in data science with an emphasis on descriptive and inferential statistical thinking using a computer programming language. Topics include preparing, cleaning, and analyzing existing real-world datasets visually, numerically, and algorithmically from multiple fields of study to make data-driven decisions. Additional topics include ethical considerations in data use, predictive modeling, and an introduction to machine learning.

This course provides review of the core pre-requisite skills, competencies, and concepts required to be successful in the co-requisite STAT C1000 Elementary Statistics course. By utilizing the "just-in-time" approach, students improve the necessary technical skills. With improved skills students are empowered to successfully solve problems and apply concepts utilized in statistics. The course introduces study skills specific to statistics with a strong emphasis on fostering a positive academic growth mindset.