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Curriculum

  • This UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? This course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

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  • This site offers separate webpages about statistical topics relevant to those studying psychology such as research design, representing data with graphs, hypothesis testing, and many more elementary statistics concepts.  Homework problems are provided for each section.

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  • The goal of WISE is to provide students and teachers of statistics easy access to a wide range of resources that are freely available on the internet. We invite you to explore our website and enjoy many wonderful statistical materials from around the world.

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  • There are more than a dozen different fit statistics researchers use to assess their confirmatory factor analyses and structural equation models. Here we have assembled a list of the most popular fit statistics used and recommended cut-offs that indicate a good fit. 

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  • This handout lists the most commonly used effect sizes, adjustments, and rules of thumb concerning sample size calculation. 

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  • 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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  • Which is more robust against outliers: mean or median?  This app demonstrates the (in)stability of these descriptive statistics as the value of an outlier and the number of data points change.

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  • Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

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  • Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.

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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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