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1. Exploratory Data Analysis
1.1. EDA Introduction


How Does Exploratory Data Analysis differ from Classical Data Analysis?

Data Analysis Approaches EDA is a data analysis approach. What other data analysis approaches exist and how does EDA differ from these other approaches? Three popular data analysis approaches are:
  1. Classical
  2. Exploratory (EDA)
  3. Bayesian
Paradigms for Analysis Techniques These three approaches are similar in that they all start with a general science/engineering problem and all yield science/engineering conclusions. The difference is the sequence and focus of the intermediate steps.

For classical analysis, the sequence is

    Problem => Data => Model => Analysis => Conclusions
For EDA, the sequence is
    Problem => Data => Analysis => Model => Conclusions
For Bayesian, the sequence is
    Problem => Data => Model => Prior Distribution => Analysis => Conclusions
Method of dealing with underlying model for the data distinguishes the 3 approaches Thus for classical analysis, the data collection is followed by the imposition of a model (normality, linearity, etc.) and the analysis, estimation, and testing that follows are focused on the parameters of that model. For EDA, the data collection is not followed by a model imposition; rather it is followed immediately by analysis with a goal of inferring what model would be appropriate. Finally, for a Bayesian analysis, the analyst attempts to incorporate scientific/engineering knowledge/expertise into the analysis by imposing a data-independent distribution on the parameters of the selected model; the analysis thus consists of formally combining both the prior distribution on the parameters and the collected data to jointly make inferences and/or test assumptions about the model parameters.

In the real world, data analysts freely mix elements of all of the above three approaches (and other approaches). The above distinctions were made to emphasize the major differences among the three approaches.

Further discussion of the distinction between the classical and EDA approaches Focusing on EDA versus classical, these two approaches differ as follows:
  1. Models
  2. Focus
  3. Techniques
  4. Rigor
  5. Data Treatment
  6. Assumptions
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