What is the Full Form of EDA?

The full form of EDA is Exploratory Data Analysis. EDA is like taking a first look at your data to understand its secrets. It helps uncover hidden patterns, unexpected features, and outliers. This is crucial before diving into any analysis, especially in fields like biological monitoring. By seeing how variables relate and where outliers pop up, EDA sets the stage for meaningful statistical tests. In your case, EDA can help identify potential causes (stressors) that might be affecting your biological data, giving you a strong foundation to build a cause-and-effect assessment.

Variable Distributions

• Histograms– Picture a bunch of data points scattered on a number line. A histogram takes all those data points and groups them into boxes along the line. 

• Boxplots- A boxplot quickly shows how data spreads. Take a box for the middle half of the data, with lines (whiskers) showing where the rest falls.

• Cumulative distribution functions- Let’s consider a ruler for your data. The CDF tells you the chance of a value falling anywhere to the left of a specific point on that ruler.

• Q-Q Plots– A Q-Q plot compares two datasets. For instance, plotting data points from each set and seeing how closely they follow each other. If they line up, the distributions are similar.

Unpacking your data is key in Exploratory Data Analysis (EDA). That means figuring out how your data points are spread out (distributed) for each variable. Histograms, boxplots, and fancy charts like Q-Q plots all help you see this distribution visually. Why is this important? Understanding distribution helps you choose the right analysis tools and avoid pitfalls. For instance, some methods assume your data follows a specific pattern (like a normal bell curve). By checking the distribution with EDA, you can ensure your chosen method works well with your data.

Other Full Forms of EDA

• Economic Development Administration
• Electronic Design Automation

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