Tukey's EDA was related to two other developments in statistical theory: robust statistics and nonparametric statistics, both of which tried to reduce the sensitivity of statistical inferences to errors in formulating statistical models. This family of statistical-computing environments featured vastly improved dynamic visualization capabilities, which allowed statisticians to identify outliers, trends and patterns in data that merited further study. The S programming language inspired the systems S-PLUS and R. Tukey's championing of EDA encouraged the development of statistical computing packages, especially S at Bell Labs. This factors have been judged by different predictive factors like environmental threat, inability to find respite and affective trust and cognitive trust. This report will be falling light on organizational management with the different factors like physical, cognitive and emotional. Main advantage of EDA is providing the data visualization of data after conducting the analysis. Tukey defined data analysis in 1961 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." Įxploratory data analysis is an analysis technique to analyze and investigate the data set and summaries the main characteristics of the dataset. 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. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. Approach of analyzing data sets in statistics Part of a series on Statistics
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