Statistical Graphics and Data Analysis

statistical-graphics

Statistical Graphics (also called ODS) is very useful for creating graphical data visual presentations which can be presented to support research, management, or business decisions. It comes in many forms, such as graphs, charts, scatter plots, maps, and histograms. In statistical graphics, data are usually presented in two or more dimensions. This type of presentation is sometimes referred to as “data visualization.” Statistics come in many shapes and forms, from graphs, charts, histograms, and scatter plots.

Some areas of statistical graphics include those that can be used for research and statistics that deal with probability and statistics for continuous variables. Examples include histograms, scatter plots, maps, and pie charts. The former provides visual information by mapping values over time (histograms), while the latter illustrate statistical concepts by representing data in a graphical form. Data visualizations can also be called “graphics representations.” For example, using statistical maps to represent the locations of cancer patients and treatment centers allows researchers to more easily observe and monitor cancer progression. These types of visualizations also allow for the creation or reproduction of statistical data sets.

Statistics can be presented in many different forms using statistical graphics software. Statistics are essentially the language of the scientific world, used to describe and reveal patterns and relationships between variables. Visualization is a way to present these patterns and relationships using figures, graphs, charts, or graphical representation. Statistical graphics provide the basis on which to interpret and communicate scientific data. In other words, all of the research done would not be successful without the use of statistical graphs, charts, or maps.

There are many different statistical graphics software programs on the market today. In order to use statistical maps, you must have a program capable of producing line graphs, scatterplots, or point and figure illustrations. Line graphs display data by showing the lines and shades in separate cells, while scatterplots or point and figure graphics are more complex and only graphically portray the data in the format of a scatter plot or point chart. Point and figure illustrations provide more detail than either scatterplots or point charts, but they are not without their problems. The main problem lies in drawing a meaningful relationship between the data points and the graphical depiction of the data distribution.

Fortunately, the solutions to this problem are readily available through a variety of software products. Most of the best statistical graphics software packages come with comprehensive tutorials to show users how to generate and customize their own statistical maps and scatter plots. A high-quality package should also allow the user to import and export data from databases, and make use of various statistical techniques and statistical graphics options. A quality package should allow the user to manipulate data sets, plot maps and histograms, or gauge statistical relationships among variables. Finally, it should allow the user to save and print statistical maps and scatter plots. In addition, a good statistical graphics software should be able to quickly and easily import or export data sets from databases, allowing the user to run a variety of statistical analysis tasks without having to create and save complicated reports each time.

Statistics, like all scientific study, is largely empirical in nature; meaning that there is a great deal of hard, back-end statistical investigation required before a hypothesis can be drawn or a result established. Most statistical graphics software will allow the user to create graphical presentations of the results obtained from statistical studies, providing first edition views of statistical summaries, graphs, or scatter plots. Moreover, the best packages will offer first edition access to historical data and to additional research material. Finally, the best packages will provide easy construction and maintenance of statistical maps, scatter plots, or point and figure graphics.

Tukey’s statistical graphics library allows users to construct descriptive statistics, which are critical for exploratory data analysis. First edition packages of Tukey include a powerful graphing tool, a data analysis tool, and an exploratory data analysis tool. Tukey exploratory statistics software includes an R interface for maximum flexibility and a built-in image viewer with a display manager for easy management and modification of figures. Additionally, a powerful chart package called rchio is included with the tukey package for constructing bar charts and scatter plots.

Like histogram or bar charting tools, a bivariate histogram visualizes data in terms of its mean and standard deviation. Histograms can be used to display time trends, data quality indices, or other statistical graphics like histograms of log mean or squares of values. The best graphical statistical graphics packages should allow the user to create high-quality bivariate histograms and visual maps. For example, those packages that have support for cubic spline maps (smooth curves) and high-order correlation functions should work well for binning, non-normal data, and time series analysis.

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