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Graphical representation and practical data analysis
Computational North-Holland
Statistics
& Data Analysis
5 (1987) 83-84
83
Editorial
Graphical Representation and Practical Data Analysis Kazumasa
WAKIMOTO
Okayama University, Tsushima Naka 2-l-1, Okayama, Japan
Masashi SHIONOGI
GOT0 Kaiseki Center, Izumicho l-22-41, Suita, Osaka, Japan
It can be said that graphical methods play increasingly important roles in the process of statistical data analysis due to their simplicity and ease of interpretation. Of course, the spread of various graphic terminals and desk-top computers as hardware has also contributed to this. From the viewpoint of the mutual roles between formal and graphical methods in statistical data analysis, in this special issue we intend to consider recent graphical methods and their applicability to practical examples. Thus, we think this kind of approach is necessary and useful for a better understanding of both formal and graphical methods. For example, formal methods are usually applied systematically or routinely, but another look at their ‘images’ in the ‘mirror’ reflected through graphical methods will lead to their reconsideration from different viewpoints. On the contrary, newly developed graphical approaches to the familiar formal methods may clarify their differences and peculiarities in concepts and/or applications. Further, we must pay attention to the fact that there is ‘something’ inherent in data which cannot be represented by formal approaches, but rather can be exhibited graphically. Formal methods are constructed based on group rules (i.e. summary statistics of samples), whereas graphical methods intend to evaluate and/or represent not only group rules but also individual rules within groups. In other words, the ‘something’ described above may provide the revelation of some fact not revealed by formal methods. We include in this issue five invited papers that are all written with the above intention and motivation. 0167-9473/87/$3.50
0 1987, Elsevier Science Publishers
B.V. (North-Holland)
84
Editorial
Baba presents a graphical representation of a sort of categorical regression analysis where a response variate is continuous and some explanatory variates are categorical. This method can be regarded as a new usage of the constellation graph as proposed by Wakimoto. However, it is of great value that we can clearly interpret the group rules and the behaviors of individuals in these groups based on such a graphical representation method. Matsubara et al. present graphical methods to compare multivariate data. These methods also apply to hypothesis testing. It is remarkable that these methods reflect to some extent inferential interpretation, considering the fact that ordinary graphical methods for multivariate data are rather descriptive. Tasaki et al. focus on graphical data analysis of experimental studies. Nowadays, great importance is attached to exploratory aspects of statistical data analysis, and accordingly too much importance is paid to graphical data analysis in observational studies. This paper is valuable in that it emphasizes graphical interpretation for comparisons in experimental studies. In particular, the fact that cluster analysis can be utilized parallel to the analysis of variance seems to present wide and insightful ways in the interpretation of experimental data. A goodness of fit test based on linked line chart is presented by Shirahata. On the basis of the reported good theoretical performances of the test, we can forecast the use and extension of these methods. Wakimoto et al. also apply the linked line chart proposed by them for testing the goodness of fit in the case of the multinomial distribution and report the resulting with its inspectional interpretation. The theme itself is common but it is suggested that we can interpret the test results more profoundly based on visual inspection. Finally, we must note that all of the methods included in this issue work powerfully with the help of computer graphics. All the computer programs are available from authors or guest editors on request.
Acknowledgements Professor Stanley P. Azen, the Editor-in-Chief of this journal kindly and carefully examined all the papers involved in this special issue and corrected flaws in the English. We, Wakimoto and Goto, as guest editors of this issue, would like to thank Professor Stanley P. Azen for his laborious work on behalf of the authors. We also would like to thank Dr. Noboru Ohsumi, the associate editor of this journal, who acted as intermediary between Professor Stanley P. Azen and us.
Statistics
& Data Analysis
5 (1987) 83-84
83
Editorial
Graphical Representation and Practical Data Analysis Kazumasa
WAKIMOTO
Okayama University, Tsushima Naka 2-l-1, Okayama, Japan
Masashi SHIONOGI
GOT0 Kaiseki Center, Izumicho l-22-41, Suita, Osaka, Japan
It can be said that graphical methods play increasingly important roles in the process of statistical data analysis due to their simplicity and ease of interpretation. Of course, the spread of various graphic terminals and desk-top computers as hardware has also contributed to this. From the viewpoint of the mutual roles between formal and graphical methods in statistical data analysis, in this special issue we intend to consider recent graphical methods and their applicability to practical examples. Thus, we think this kind of approach is necessary and useful for a better understanding of both formal and graphical methods. For example, formal methods are usually applied systematically or routinely, but another look at their ‘images’ in the ‘mirror’ reflected through graphical methods will lead to their reconsideration from different viewpoints. On the contrary, newly developed graphical approaches to the familiar formal methods may clarify their differences and peculiarities in concepts and/or applications. Further, we must pay attention to the fact that there is ‘something’ inherent in data which cannot be represented by formal approaches, but rather can be exhibited graphically. Formal methods are constructed based on group rules (i.e. summary statistics of samples), whereas graphical methods intend to evaluate and/or represent not only group rules but also individual rules within groups. In other words, the ‘something’ described above may provide the revelation of some fact not revealed by formal methods. We include in this issue five invited papers that are all written with the above intention and motivation. 0167-9473/87/$3.50
0 1987, Elsevier Science Publishers
B.V. (North-Holland)
84
Editorial
Baba presents a graphical representation of a sort of categorical regression analysis where a response variate is continuous and some explanatory variates are categorical. This method can be regarded as a new usage of the constellation graph as proposed by Wakimoto. However, it is of great value that we can clearly interpret the group rules and the behaviors of individuals in these groups based on such a graphical representation method. Matsubara et al. present graphical methods to compare multivariate data. These methods also apply to hypothesis testing. It is remarkable that these methods reflect to some extent inferential interpretation, considering the fact that ordinary graphical methods for multivariate data are rather descriptive. Tasaki et al. focus on graphical data analysis of experimental studies. Nowadays, great importance is attached to exploratory aspects of statistical data analysis, and accordingly too much importance is paid to graphical data analysis in observational studies. This paper is valuable in that it emphasizes graphical interpretation for comparisons in experimental studies. In particular, the fact that cluster analysis can be utilized parallel to the analysis of variance seems to present wide and insightful ways in the interpretation of experimental data. A goodness of fit test based on linked line chart is presented by Shirahata. On the basis of the reported good theoretical performances of the test, we can forecast the use and extension of these methods. Wakimoto et al. also apply the linked line chart proposed by them for testing the goodness of fit in the case of the multinomial distribution and report the resulting with its inspectional interpretation. The theme itself is common but it is suggested that we can interpret the test results more profoundly based on visual inspection. Finally, we must note that all of the methods included in this issue work powerfully with the help of computer graphics. All the computer programs are available from authors or guest editors on request.
Acknowledgements Professor Stanley P. Azen, the Editor-in-Chief of this journal kindly and carefully examined all the papers involved in this special issue and corrected flaws in the English. We, Wakimoto and Goto, as guest editors of this issue, would like to thank Professor Stanley P. Azen for his laborious work on behalf of the authors. We also would like to thank Dr. Noboru Ohsumi, the associate editor of this journal, who acted as intermediary between Professor Stanley P. Azen and us.