Explore the Hubble Deep Fields from a statistical point of view. Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).
Explore the Hubble Deep Fields from a statistical point of view. Watch out for the booby traps of bias, the vagueness of variability, and the shiftiness of sample size as we travel on a photo safari through the Hubble Deep Fields (HDFs).
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: Mantel-Haenszel estimator of common odds ratio, confounding in logistic regression, univariate/multivariate analysis, bias vs. variance, and simulations.
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.
This is a graduate level survey course that stresses the concepts of statistical design and analysis in biomedical research, with special emphasis on clinical trials. Perfect for students and teachers wanting to learn/acquire materials for this topic.
A song for use in helping students to learn the four levels of measurement (nominal, ordinal, interval, ratio) in appropriate hierarchical order and to identify examples of each in context. Lyrics by Larry Lesser and music by Larry Lesser and Dominic Dousa copyright 2015. This song is part of an NSF-funded library of interactive songs that involved students creating responses to prompts that are then included in the lyrics (see www.causeweb.org/smiles for the interactive version of the song, a short reading covering the topic, and an assessment item).
The goal of this text is to provide a broad set of topics and methods that will give students a solid foundation in understanding how to make decisions with data. This text presents workbook-style, project-based material that emphasizes real world applications and conceptual understanding. Each chapter contains:
The text is highly adaptable in that the various chapters/parts can be taken out of order or even skipped to customize the course to your audience. Depending on the level of in-class active learning, group work, and discussion that you prefer in your course, some of this work might occur during class time and some outside of class.
This website provides a comprehensive overview of descriptive statistics (mean/median/mode, range, standard deviation, and variance) through informative webpages with examples, links to data sets, and problems for the readers to try for themselves.