What exactly is Exploratory Data Analysis? It is a technique used by many data analysts to explore and analyze large data sets, identifying relationships between variables, drawing conclusions based on the facts they reveal, and ultimately using that information to guide policy decisions. It makes it possible for data analysts to find patterns, spot trends, and determine how to manipulate data to get the results you want. It can even tell you what “payoff” factors may apply to your data set, and it can tell you how to make that payoff occur. This is all done by fitting a statistical model to the relevant data.
Data visualization is a way to visualize data without needing to deal with all the messy statistics and real-world patterns and relationships. You can visualize relationships between variables in an easily understood format. Using Venn diagram visualizations and other tools, data visualizations can make inferences, provide a background understanding for your data, and present multiple ways to interpret and fit a statistical model to your data. These visualizations can even help you decide what type of statistical model to use and how to fit it to your data. Here are some examples:
Visualizations such as the Venn Diagram are commonly used in univariate analysis. In this form of visualizations, a set of x-vals are drawn representing the data sets that the researcher or statistician has to deal with. A circle then appears around the center of the x-vals, connecting the highest point to the lowest point on the curve. In a univariate visualization, there is only one variable that is the focus, so visualizing the data points on one axis only is difficult, and can lead to missing some interesting information.
Graphical approaches to data analysis tend to be much more powerful because they can reveal hidden patterns and relationships through the use of visual cues. However, graphical models cannot be used without assumption assumptions. Assumptions in a graphical model can be either complicated or extremely simple. Simple assumptions can provide quick insights, but they do not tell the full story about the true nature of the relationship. On the other hand, more complex assumptions can be very important for obtaining the full statistical significance. The researcher should choose the right assumption for the data that will give the most insightful visual presentation.
Some statistical methods and techniques that rely on assumption-free visualizations (also known as principal components analysis) also rely on assumptions to remove high value variance or outliers that cannot be explained by the main component. The removal of high outliers allows the researcher to focus on the significant patterns and relationships that are actually relevant to their research topic. This also enables them to run multiple samples within the time frame that is needed in order to detect trends and deviations from the underlying structure. Although this method can remove large outliers, it may miss small changes that are crucial to the accuracy of the results.
Another common assumption made in Exploratory Data Analysis is the construction of correlated bins or scorecards. There are many potential benefits that come with using correlated bins in data analysis that range from the ability to determine important metrics to the identification of interesting differences among variables. High correlated bins can also provide an accurate representation of the distribution of the underlying variable in the space of the sample. Differentiating between correlated bins allows researchers to obtain reliable estimates of the parameters of the principal components and their derivatives.
One important tool for the data analyst is the likelihood estimate (L Estimates). The likelihood estimates determine the probability density function of the data points and help determine the range of the data points for a selected Outlier Detection algorithm. The probability density function is estimated by dividing each Outlier value by the number of data points in the subset and then calculating the range and standard deviation of the resulting distribution.
The final step in what is called exploratory data analysis is the inclusion of new and more informative explanatory variables into the original data set. This is usually achieved by the addition of correlated random effects to the original data set. This allows researchers to test various hypotheses about the relationships among the key Outliers. It also allows them to test hypotheses regarding any other terms that were not previously considered in the original equation. This allows researchers to explore non-traditional solutions to problems such as uncovering economic trends and anomalies by the use of statistical methods that do not necessarily rely on traditional economics.