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SAS visual statistics is an important component of data integration. Statistical analysis enables an analyst to detect relationships between the variables that are not visually apparent, and which are not evident when the variables are observed individually.

In a previous article we explained that to better understand how data is collected and analyzed in a business environment, it is helpful to visualize the data. Visualization allows people to see the data in context. Information presented in an attractive format facilitates analysis of the data. Using visualizations, analysts can identify the relationships among the variables.

SAS is a general purpose software that is used to analyze large amounts of data. It includes functions such as those for designing graphical displays, statistics, lists, matrices, and other mathematical operations. It can be used to design data visualizations. The statistical software programs used in analytics projects are very useful in creating effective visualizations.

In a project involving visualizations, it is often more appropriate to use the SAS analysis functions rather than the routines provided in the data acquisition routines. This is because the latter routines, which are provided by some software packages, tend to have pre-defined functions that cannot be modified, customized, or adapted. As a result, the end product usually lacks a natural, artistic, or compelling visual presentation. In most cases, the results are bland and lifeless. In order to make the analysis more meaningful, it is best to use the SAS features.

When analyzing a series of data, the analyst first creates a graphical representation. It is generally a graphical representation using the graphical tools available in the programs. The graphics are sometimes called ‘banners’ because of their resemblance to banners. This type of presentation tends to create images that are evocative and inspiring. In other words, the visualizations can be regarded as a reminder of the analysis process itself.

The most popular visualization that can be used is the bar chart, since it is one of the most widely used SAS graphics. It is an average bar graph, but with several additional components such as line charts, pie charts, and scatter plots. An indicator that is considered most appropriate is the moving average, since it is often more visually appealing and easy to interpret.

Most visualization programs allow the user to change colors and backgrounds to enhance the visual representations. Many times, a color scheme or background image can be selected to represent the data, in which case it becomes a common perception tool. In the past, the bar chart was the only option available.

A table of figures is also an effective visualization. Table of figures enables the analyst to combine graphical representations into one large graphic. The table of figures is often one of the first visualization methods used, because it can help present a clear analysis. In addition, it is relatively easy to learn and it provides the analyst with the ability to create graphics that can be used anywhere. However, due to its limitation of spatial interpretation, it is often not the preferred visualization method.

Before data analysis can be performed, all the data has to be analyzed. This is where table of contents (TOC) comes in. A TOC is simply a table that contains a series of headings. It allows the analyst to summarize the whole set of data.

After statistical analysis has been completed, the reporting process begins. The report gives detailed information about the results of the analysis. In the reports, many tables and graphs are used to summarize the results, as well as to describe the relationship between the variables.

The visual analysis of this type is done using a graphical user interface (GUI). This interface lets the user control the user interface in a variety of ways, depending on the type of visualization. Another option is the ‘box plot’. This is an interactive way of presenting the results of the analysis, which can be used for visualizing relationships among variables.

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