File Name: understanding robust and exploratory data analysis .zip
This paper introduces the family of techniques called exploratory data analysis.
In statistics , exploratory data analysis is an approach to analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task. Exploratory data analysis was promoted by John Tukey to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis IDA ,  which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. Tukey defined data analysis in as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of mathematical statistics which apply to analyzing data.
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Lauren Kennedy and Andrew Gelman  Bayesian hierarchical weighting adjustment and survey inference. You have remained in right site to start getting this info. Download our andrew gelman bayesian eBooks for free and learn more about andrew gelman bayesian. Stern , David B. In the standard existing approach for analysis of these data, a large proportion of the measurements are discarded as being above or below detection limits.
As a discipline, statistics has mostly developed in the past century. Probability theory—the mathematical foundation for statistics—was developed in the 17th to 19th centuries based on work by Thomas Bayes, Pierre-Simon Laplace, and Carl Gauss. In contrast to the purely theoretical nature of probability, statistics is an applied science concerned with analysis and modeling of data. Modern statistics as a rigorous scientific discipline traces its roots back to the late s and Francis Galton and Karl Pearson. Fisher, in the early 20th century, was a leading pioneer of modern statistics, introducing key ideas of experimental design and maximum likelihood estimation.
Understanding Robust and Exploratory Data Analysis edited by David C. Hoaglin, Frederick Mosteller and John. W Tukey. John Wiley and Sons, New York, NY.
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