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Faculty

  • This text was written for an introductory class in Statistics suitable for students in Business, Communications, Economics, Psychology, Social Science, or liberal arts; that is, this is the first and last class in Statistics for most students who take it. It also covers logic and reasoning at a level suitable for a general education course.  SticiGui provides text, interactive tools, lecture videos, sample exam reviews, and more for a course in basic statistical concepts.  

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  • These cheat sheets make it easy to learn about and use some of the favorite packages of RStudio. 

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  • RStudio is a set of integrated tools designed to help you be more productive with R. It includes a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting, viewing history, debugging and managing your workspace. 

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  • The Cornell Statistical Consulting Unit distributes a periodic electronic newsletter, StatNews. This newsletter discusses applications of statistics that are commonly encountered in research and teaching.

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  •  Newsletter issue of 'StatNews' distributed by the Cornell Statistical Consulting Unit on survival analysis.

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  • R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

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  • This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console. Almost every example in this compendium is driven by the same philosophy: A good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler.  A note for R fans: the majority of our plots have been created in base R, but you will encounter some examples in ggplot.

     

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  • This page supports an in-class exercise that highlights several key Bayesian concepts. The scenario is as follows: a large paper bag contains pieces of candy with wrappings of different color, and we are interested in learning about the unknown proportion of yellow-wrapped pieces of candy. After completing the exercises, we will be familiar with the following concepts and ideas: probability distributions can quantify degree of beliefprior distributionposterior distributionsequential updatingconjugacy, Cromwell’s Rule (http://en.wikipedia.org/wiki/Cromwell's_rule), the data overwhelm the prior, Bayes factors, Savage-Dickey density ratio, sensitivity analysiscoherence.

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  • Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

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  • Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

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