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Statistical Topic

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  • This collection of case studies includes the following topics: Stock Prices; Breast Cancer Research; Effect of Fitness Program; Water Use in Los Angeles; Oral Hygiene in the ICS-II project; Brinks vs NYC; Effect of Exercise on Heart Disease; National Assessment of Educational Progress; The London Underground; Suicides of Women and Men; Temperature in San Francisco; Lead Intake; Voting for Johnson; Salaries of Yale Men; K-Mart Cookie Sales; Skeleton Differences between Tribes; Advertising for Detergents; Did Mendel Fudge his Data; Rainfall in the United Kingdom; Jury selection in Alameda County; Racial Bias in Jury Selection: Swain vs Alabama.; Gender Bias in Jury Selection: The Case of Dr. Spock.; The ELISA test for the AIDS Virus.; School Careers in the Netherlands in 1959.; The Northridge Earthquake of January 1994.; The Trial of the Pix.

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  • How can we accurately model the unpredictable world around us? How can we reason precisely about randomness? This course will guide you through the most important and enjoyable ideas in probability to help you cultivate a more quantitative worldview.

    By the end of this course, you’ll master the fundamentals of probability and random variables, and you’ll apply them to a wide array of problems, from games and sports to economics and science.  This course includes 62 interactive quizzes and more than 400 probabilty-based problems with solutions.  Access to this course requires users to sign up for a free account.

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  • Negative Correlation is a poem by Maarten Manhoff (1972 - ); the pen name for Ernst Wit of Lancaster University in the United Kingdom. The poem was written in 2003.

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  • 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.

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  • 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.

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  • 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.

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  • 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.

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  • This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. Many screenshots and screencasts (with no audio) will be included, but if further clarification is needed on these or any other aspect of the book, please create a GitHub issue here or email me with a reference to the error/area where more guidance is necessary.  It is recommended that you have R version 3.3.0 or later, RStudio Desktop version 1.0 or higher, and rmarkdown R package version 1.0 or higher. 

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  • Everyday, massive amounts of data are generated in every part of our lives. That makes data fluency an indispensable skill to help you succeed - no matter what industry you’re in -- and DataCamp is here to help.  With bite-sized lessons in how to use R, Python, and SQL for data science, DataCamp allows you to learn in a way that fits your schedule, on any device.

    9 courses are available for those who create a free account, and 123 courses are available for individuals who sign up for a $29/month membership ($25/month is you pay yearly).  Businesses can also pay $300 per member per year for employees to learn these skills.

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  • swirl is a software package for the R programming language that turns the R console into an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.

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