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1. Exploratory Data Analysis
1.2. EDA Assumptions



What If Assumptions Do Not Hold? If some of the underlying assumptions do not hold, what can be done about it? What corrective actions can be taken? The positive way of approaching this is to view the testing of underlying assumptions as a framework for learning about the process. Assumption-testing promotes insight into important aspects of the process that may not have surfaced otherwise.
Primary Goal is Correct and Valid Scientific Conclusions The primary goal is to have correct, validated, and complete scientific/engineering conclusions flowing from the analysis. This usually includes intermediate goals such as the derivation of a good-fitting model and the computation of realistic parameter estimates. It should always include the ultimate goal of an understanding and a "feel" for "what makes the process tick". There is no more powerful catalyst for discovery than the bringing together of an experienced/expert scientist/engineer and a data set ripe with intriguing "anomalies" and characteristics.
Consequences of Invalid Assumptions The following sections discuss in more detail the consequences of invalid assumptions:
  1. Consequences of non-randomness
  2. Consequences of non-fixed location parameter
  3. Consequences of non-fixed variation
  4. Consequences related to distributional assumptions
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