The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codifi-cation of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria.

Graphical presentations can be used to communicate information in relational data sets succinctly and effectively. However, novel graphical presentations about numerous attributes and their relationships are often difficult to understand completely until explained. Automatically generated graphical presentations must therefore either be limited to simple, conventional ones, or risk incomprehensibility. One way of alleviating this problem is to design graphical presentation systems that can work in conjunction with a natural language generator to produce "explanatory captions." This paper presents three strategies for generating explanatory captions to accompany information graphics based on: (1) a representation of the structure of the graphical presentation (2) a framework for identifyingthe perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. We describe an implemented system and illustrate how it is used to generate explanatory cap...

... This paper presents a system to do so. It uses a text planner to determine the content and structure of the captions based on: (1) a representation of the structure of the graphical presentation and its mapping to the data it depicts, (2) a framework for identifying the perceptual complexity of graphical elements, and (3) the structure of the data expressed in the graphic. The output of the planner is further processed regarding issues such as ordering, aggregation, centering, generating referring expressions and lexical choice. We discuss the architecture of our system and its strengths and limitations. Our implementation is currently limited to 2-D charts and maps, but, except for lexical information, it is completely domain independent. We illustrate our discussion with figures and generated captions about housing sales in Pittsburgh.

Graphical presentations can be very effective in communicating large datasets and the patterns, trends and relationships in them. Charts and graphs used in reporting data usually tend to highlight relevant points and suppress unnecessary ones. This can be problematic, since such charts can be used to selectively emphasize desired aspects: in fact, a recent study of several hundred annual reports found that more than 30% of charts regarding a company's financial performance used design parameters that facilitated favorable inferences by users and hindered others that were not. Furthermore, since studies show that average users are not graphically sophisticated, many of the techniques used to achieve these biased effects may not be obvious to them. One solution to this problem is to make these design choices explicit to the user. This paper presents a data analysis interface that allows users to explore the visualization space by modifying these design decisions. This int...

Much has been written in recent years on statistical literacy, but what do we mean by “statistical literacy”? I will take a pragmatic approach and provide resources to help you to define this term for yourself and implement your idea of statistical literacy in the classroom. This paper includes a bibliography of relevant resources. The text provides notes on the bibliography and ruminates on the issues involved. Even if my conclusions differ from yours, the bibliography and discussion may still prove useful in defining, defending and implementing a statistical literacy program. Planning a statistical literacy program I will discuss the improvement of statistical literacy in the context of school, particularly in colleges or universities, the context I know best. However, much that I say will be relevant to a potential high school course, or to efforts to enhance statistical literacy by integrating it into the K-12 curriculum. There are three main steps to improving statistical literacy. 1. Define “statistical literacy”. 2. Gather the resources you need to achieve it. 3. Implement a specific plan. Defining statistical literacy We can provisionally define “statistical literacy ” as the skills a person needs in order to deal with issues of probability and statistics that arise in everyday life. That is still pretty general (e.g., “Which issues are ‘statistical ’?”). Recent years have seen many recommendations to improve quantitative and statistical literacy — usually as part of an effort to reform the teaching of mathematics and statistics

Statistical graphs are the usual form of visual communication. In the utilization of statistical graphs the emphasis is focused either on the presentation of data or their analysis. The primary function of graphs was the visual presentation of data. But, with the development and application of electronic computers and software the analytical function of graphs has an increasing importance (Schmid, 1983). Today’s graphical techniques allow comparisons between groups of data. Box-and-whisker plot is a simple graphical method that is introduced to students in teaching descriptive statistics as a useful exploratory data analysis tool for studying main characteristics of distribution, detecting outliers and extreme values. This paper deals with application of this diagram and its categorized form (trellis diagram) in analysis and modelling data from designed experiments in agriculture.