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Graphical comparison of resemblance measures in phytosociology
Vegetatio, 1981A simple graphical method, COREM, is described for the comparison of quantitative resemblance measures. The basis of the method is the ordered comparison case series (OCCAS) composed of releve pairs ranked in decreasing order of similarity. For a given OCCAS, values of given resemblance functions are calculated and plotted in COREM.
L. J. Hajdu
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Graphical Tests for Power Comparison of Competing Designs
IEEE Transactions on Visualization and Computer Graphics, 2012Lineups have been established as tools for visual testing similar to standard statistical inference tests, allowing us to evaluate the validity of graphical findings in an objective manner. In simulation studies lineups have been shown as being efficient: the power of visual tests is comparable to classical tests while being much less stringent in ...
H, Hofmann +3 more
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics, 1987 A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation.
Peter J Rousseeuw
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A GRAPHIC COMPARISON OF FIVE PHENOTHIAZINES
American Journal of Psychiatry, 1959Five phenothiazine derivatives have been studied using similar groups of patients and similar methods of evaluating and recording behavioral change. All of the patients included in these reports had been hospitalized longer than one year and had been refractory to previous treatment efforts. The results indicate that this group of chronic patients, all
J A, SMITH +3 more
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Computer Science Education, 2018
Background and Context: The relationship between novices’ first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K–12 computing education.
Chen Chen +4 more
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Background and Context: The relationship between novices’ first programming language and their future achievement has drawn increasing interest owing to recent efforts to expand K–12 computing education.
Chen Chen +4 more
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Graphical approach to microprocessor comparison
Microprocessors, 1977Abstract The problem of selecting a microprocessor for a given application is shown to be aggravated by the lack of standardization in the way that instruction sets are presented. A representation method is proposed which aids the selection process by displaying microprocessor instruction sets in a graphical form, allowing easy comparison.
Michael Barton, Erik Dagless
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Graphical comparisons of multivariate data
Computational Statistics & Data Analysis, 1987Some graphical methods for comparing multivariate data are given. These methods are based on principles of 'mapping onto the two-dimensional plane' and 'ordering of multivariate data'. For illustration, they are applied to two examples which are valvular disease data and literature data. Their usefulness and effectiveness in visual comparison is shown.
Matsubara, Yoshihiro +2 more
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Visual Comparison of Graphical Models
2009 14th IEEE International Conference on Engineering of Complex Computer Systems, 2009Collaborative development, incremental design and revision management require the ability to compare different versions of software artifacts. There are well-established approaches for comparing textual artifacts such as program files. However, the situation is different in graphical modeling.
Arne Schipper +2 more
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Miropeats: graphical DNA sequence comparisons
Bioinformatics, 1995Miropeats displays DNA sequence similarity information graphically. The program discovers regions of similarity amongst any set of DNA sequences and then draws a graphic that summarizes the length, location and relative orientations of any repeated sequences.
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On Graphical Procedures for Multiple Comparisons
Journal of the American Statistical Association, 1982Abstract In a graphical procedure for comparing k treatment means in a one-way ANOVA, one displays uncertainty intervals around the sample means and judges any pair to be significantly different if and only if their uncertainty intervals do not overlap.
Yosef Hochberg +2 more
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