Data Collection

  • "This page contains data sets provided by UCLA faculty members in the Social Sciences, OBEE, and Economics departments. The purpose of the page is to provide vivid, real-life examples of how raw data is analyzed using computer programs such as STATA for introductory statistics classes such as Stat 11, Stat 12 and Stat 13." The format of the data is .dta.
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  • This collection of data can be used for many useful statistical analyses. Data and description are in a separate file and useful for SAS data analysis too. Data are categorized by analysis type, hence easy to pic relevant data sets accordingly. The data can be used for many analysis such as, Categorical data analysis, Polynomial Linear, Nonlinear, Logistic, Poisson, Negative Binomial Regression analysis, Response Surface Regression, Binary Response Regression, Time Series Data,1-Way ANOVA/ Independent Samples t-test, Multi-Factor ANOVA, and many other data analysis.
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  • Data sets were submitted by the authors of articles in JSE. Each data set is presented along with a link to the article that references the data.
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  • Many data sets useful for modeling bivariate relationships. The data sets are formatted for use in Fathom, but text versions are also available.
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  • This site presents 19 videos of statisticians summarizing a project that they did. Each video is accompanied by a dataset so that viewers can try to recreate the statistics in the video. Video runtimes vary from about 8 minutes to as many as 35 minutes.
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  • This is a collection of data sets that were part of R packages. The data set page includes information on which package the data set comes from, the name of the data set, and the number of rows and columns included. Each set is given in .csv form with a documentation file also.
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  • This collection of datasets from Dr. John Rasp's Statistics Webpage is for his STAT 460 (Experimental Design & Advanced Data Analysis), STAT 301 (Business Statistics), STAT 201 (Intro to Business Statistics) classes. This also provides links for statistical web pages, resources for statistical studies, Homework and lecture reviews.
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  • A cartoon to aid in the discussion of volunteer sampling and response bias in surveys. The cartoon was created in 2017 by Sabrina Cappella, a student at the University of Toronto at Mississauga and took the grand prize in the 2017 A-mu-sing competition.
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  • A cartoon to aid in the discussion of the importance of replication. The cartoon was created in 2017 by Lyla El-Fayomi, a student at the University of Toronto at Mississauga and won an honorable mention in the cartoon category of the 2017 A-mu-sing competition.
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  • Big data analysis is explained in this online course that introduces the user to the tools Hadoop and Mapreduce. These tools allow for the parallel computing necessary to analyze large amounts of data.

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