A collection of Statistics related Haikus collected by Nicholas Horton from his Math 190 (statistical Methods for Undergraduate Research) course at Smith College in Spring, 2010. These are included in the Statistics Haiku Project at http://www.math.smith.edu/~nhorton/haikustat.html
A collection of Statistics related Haikus collected by Nicholas Horton from his Math 190 (statistical Methods for Undergraduate Research) course at Smith College in Spring, 2005. These are included in the Statistics Haiku Project at http://www.math.smith.edu/~nhorton/haikustat.html
A game to aid in teaching experimental design and significance testing (especially one sample, two sample, and matched pair situations). Tangrams are puzzles in which a person is expected to place geometrically shaped pieces into a particular design. The on-line Tangram Game provides students the opportunity to design many versions of the original game in order to test which variables have the largest effect on game completion time. A full set of student and instructor materials are available and were created by Kevin Comiskey (West Point), Rod Sturdivant (Ohio State University) and Shonda Kuiper (Grinnell College) as part of the Stat2Labs collection.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.
The emphasis in this course will be understanding statistical testing and estimation in the context of "omics" data so that you can appropriately design and analyze a high-throughput study. Since the measurement technologies are evolving rapidly, important objectives of the course are for students to gain a basic understanding of statistical principles and familiarity with flexible software tools so that you can continue to assess and use new statistical methodology as it is developed for new types of data.
By the end of the course, you should be able to tailor the analysis of your data to your needs while maintaining statistical validity. You should come out of the course with insight so that you can assess the validity of new statistical methodologies as they are introduced as well as understand appropriate statistical analyses for data types not discussed in the class.
Perfect for students and teachers wanting to learn/acquire materials for this topic.
The objective of this course is to learn and apply statistical methods for the analysis of data that have been observed over time. Our challenge in this course is to account for the correlation between measurements that are close in time. Perfect for students and teachers wanting to learn/acquire materials for this topic.
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 complications. Perfect for both students and teachers wanting to learn/acquire materials for this topic.
Epidemiology is the study of the distribution and determinants of human disease and health outcomes, and the application of methods to improve human health. This course examines the methods used in epidemiologic research, including the design of epidemiologic studies and the collection and analysis of epidemiological data. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Those who complete this course will 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; and interpret results of multivariate data analyses. Perfect for students and teachers wanting to learn/acquire materials for this topic.