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	Graphical Methods - LoveToKnow 1911 (Site not responding. Last check: 2007-10-10)
		GRAPHICAL METHODS, devices for representing by geometrical figures the numerical data which result from the quantitative investigation of phenomena.
		The simplest application is met with in the representation of tabular data such as occur in statistics.
		The method of polar co-ordinates is more rarely used, being only specially applicable when one of the variables is a direction or recorded as an angle.
	2.1911encyclopedia.org /G/GR/GRAPHICAL_METHODS.htm (423 words)

	Systems, methods and graphical user interfaces for controlling tone reproduction curves of image capture and forming ...
		The graphical user interface of claim 9, wherein the current value of the control function can be altered by selecting the selectable slider pointer and altering the current position of the slider pointer relative to the slider portion.
		The graphical user interface of claim 10, wherein the appearance of the bottom subportion of the slider portion changes when the current position of the slider pointer relative to the slider portion is altered.
		The method of claim 19, further comprising, for that slider, altering the determined appearance value as the position of the slider pointer relative to the slider portion is altered.
	www.freepatentsonline.com /6614456.html (9163 words)

	The Elegance of Graphical Representation (Site not responding. Last check: 2007-10-10)
		Graphical representation using scalar measurement and comparison of such results, to theoretical calculations, is a significant alternate route to determine the accuracy of one’s analysis.
		Part II can equally be solved graphically by method superposition of additional graphical vector analysis to the established graphical method shown also theoretical calculations.
		By graphical analysis, 5,000 vertical load is extended and drawn to scale and projected above body as shown; line R1 is drawn to scale = 4095 [F1 component already established] at an angle of 11° - 24¢; coefficient of friction : = 0.2 = 11.3°.
	www.icec.ca /articles/Graphical_Article.htm (882 words)

	Graphical Method
		Graphical analysis is the simplest method for obtaining results in both life data and accelerated life testing analyses.
		Although they have limitations (presented in Comments on the Graphical Method section) in general graphical methods are easily implemented and easy to interpret.
		The easiest parameter estimation method (to use by hand) for complex distributions, such as the Weibull distribution, is the method of probability plotting.
	www.weibull.com /AccelTestWeb/graphical_method.htm (941 words)

	Graphical Models (Site not responding. Last check: 2007-10-10)
		Probabilistic graphical models are graphs in which nodes represent random variables, and the (lack of) arcs represent conditional independence assumptions.
		Indeed, it is common to use frequentists methods to estimate the parameters of the CPDs.
		Classical control theory is mostly concerned with the special case where the graphical model is a Linear Dynamical System and the utility function is negative quadratic loss, e.g., consider a missile tracking an airplane: its goal is to minimize the squared distance between itself and the target.
	www.cs.ubc.ca /~murphyk/Bayes/bnintro.html (6628 words)

	Amazon.fr : Graphical Models: Methods for Data Analysis and Mining: Livres en anglais: Christian Borgelt,Rudolf Kruse (Site not responding. Last check: 2007-10-10)
		This book provides a self-contained introduction to the learning of graphical models from data, and is the first to include detailed coverage of possibilistic networks - a relatively new reasoning tool that allows the user to infer results from problems with imprecise data.
		The methods described here are applied in a number of industries, including a recent quality testing programme at a major car manufacturer.
		Since this book is about graphical models and reasoning with them, we start by saying a few words about reasoning in general, with a focus on inferences under imprecision and uncertainty and the calculi to model these (cf.
	www.amazon.fr /Graphical-Models-Methods-Analysis-Mining/dp/0470843373 (608 words)

	gorilla: Product: 'Graphical Methods for the Design of Experiments'
		Graphical Methods for Experimental Design presents a strategic view of the planning of experiments, and provides a number of graphical tools that are useful for justifying the effort required for experimentation, identifying variables and candidate statistical models, selecting the set of run conditions and for assessing the quality of the design.
		In addition, the graphical framework for creating fractional factorial designs is used to present experimental results in a way that is easier to understand than a set of model coefficients.
		Many of the graphical techniques are accessible without any knowledge of statistical models however, requiring only some familiarity with the plotting of functions and with the concept of projection from elementary mechanical drawing.
	www.gorilla.it /gorilla/product.4print.asp?SessionID=t&sku=0387947507 (460 words)

	List of graphical methods . V model . Nomogram . Dymaxion map . Topographic map . Binary decision diagram . Johnston ...
		The current model which is at the moment available in the 1997 version is mandatory within German federal administration and military and has emerged as standard for software development in a lot of industrial companies as well.
		It is a method for the visual representation of three-dimensional objects in two dimensions in which the angles between the projection of the x, y, and z axis axes are all the same, or 120°.
		For objects with surfaces that are substantially perpendicular to andor parallel with one another, it corresponds to rotation of the object by +- 45° about the vertical axis, followed by rotation of approximately +- 35.264° [= arcsin tan 30° ] about the horizontal axis starting from an...
	www.uk.knowledge-info.org /List_of_graphical_methods-UK-2060220-iq (776 words)

	Representations & Methods (Site not responding. Last check: 2007-10-10)
		Graphical methods usually involve a certain amount of trial and error, and they may leave some doubt as to whether we have looked closely enough at all of the right parts of the graph.
		Numerical methods are algorithmic, in the sense that they provide a definite sequence of steps to follow, and they are usually iterative, which means that a few relatively simple steps are repeated over and over again to obtain the successively better estimates.
		Like graphical methods, numerical methods involve a certain amount of trial and error; but the "trials" of numerical methods are carefully controlled, and the errors are then systematically reduced.
	www.wmueller.com /precalculus/models/5.html (271 words)

	Framework for Categorical Data Analysis
		Graphical methods for categorical data are still in infancy.
		Model-based methods for analyzing categorical data, such as logistic regression and log-linear models, are discrete analogs of methods of regression and analysis of variance for quantitative data.
		Statistical and graphical methods are of practical value to the extent that they are available and easy to use.
	www.math.yorku.ca /SCS/Courses/grcat/grcframe.html (387 words)

	Graphical Methods For (Site not responding. Last check: 2007-10-10)
		Graphical methods have an advantage over numerical methods for model validation because they readily illustrate a broad range of complex aspects of the...
		Graphical Methods for Experimental Design presents a strategic view of the planning of experiments, and provides a number of graphical tools that are useful...
		Graphical methods for class prediction using dimension reduction techniques on DNA microarray data.
	dataanalysis.inlydata.com /graphicalmethodsfor (830 words)

	Graphical Methods for Categorical Data
		Statistical methods for categorical data, such as loglinear models and logistic regression, represent discrete analogs of the analysis of variance and regression methods for continuous response variables.
		However, while graphical display techniques are common adjuncts to analysis of variance and regression, methods for plotting contingency table data are not as widely used.
		Graphical methods still have a long way to go.
	www.math.yorku.ca /SCS/sugi/sugi17-paper.html (3769 words)

	4.4.4. How can I tell if a model fits my data?
		Graphical methods have an advantage over numerical methods for model validation because they readily illustrate a broad range of complex aspects of the relationship between the model and the data.
		Numerical methods for model validation tend to be narrowly focused on a particular aspect of the relationship between the model and the data and often try to compress that information into a single descriptive number or test result.
		One common situation when numerical validation methods take precedence over graphical methods is when the number of parameters being estimated is relatively close to the size of the data set.
	www.itl.nist.gov /div898/handbook/pmd/section4/pmd44.htm (768 words)

	Comments on the Graphical Method
		Although the graphical method is simple, it is quite laborious.
		Confidence intervals on all of the results cannot be ascertained using graphical methods.
		The maximum likelihood estimation parameter estimation method described next overcomes these shortfalls, and is the method utilized in ALTA.
	www.weibull.com /AccelTestWeb/comments_on_the_graphical_method.htm (184 words)

	Amazon.fr : Learning in Graphical Models: Livres en anglais: Michael Irwin Jordan (Site not responding. Last check: 2007-10-10)
		Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity.
		Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts.
		Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data.
	www.amazon.fr /Learning-Graphical-Models-Michael-Jordan/dp/0262600323 (539 words)

	Companion Website for Friendly's Visualizing Categorical Data
		Graphical methods for quantitative data are well developed and widely used.
		Readers will also appreciate the implementation of these methods in general macros and programs that are described in the book.
		With expertise in statistics, graphics, and macro writing, Michael has taught graduate-level courses in Multivariate Data Analysis and Computer Methods in Psychology using SAS software for over 25 years.
	support.sas.com /publishing/bbu/companion_site/56571.html (227 words)

	Graphs and graphical methods
		A graph reveals much more clearly such features as linearity or nonlinearity, maxima and minima, points of inflection, etc. Graphical methods of smoothing data and of differentiation and integration are sometimes easier than numerical methods.
		Graphs and graphical methods suffer, of course, from the limitations of a two-dimensional surface.
		Figure Caption: A graphical method of determining the limit of error in a slope.
	www.chemistry.adelaide.edu.au /external/soc-rel/content/datagraph.htm (1496 words)

	Graphical plots for investigating heterogeneity in meta-analysis (Site not responding. Last check: 2007-10-10)
		As compared with other graphical methods, the L'Abbe plot is useful to identify not only the studies having different results from other studies, but also the study arms that are responsible for such differences.
		A method is suggested to standardize the distance between study points and the overall RR line in a L'Abbe plot before making comparisons for investigating clinical and/or methodological heterogeneity.
		It is concluded that if they are used appropriately, the graphical plots are very useful in determining the focus of heterogeneity investigation in a meta-analysis.
	www.cochrane.org /colloquia/abstracts/baltimore/MarylandA16.htm (292 words)

	Graphical methods I: Slug wars
		Graphical methods can be useful for mathematicians to get to grips with a complex problem: they can provide strikingly simple ways of depicting complex behaviour, and are also capable of representing complex and copious data in a straightforward way.
		Graphical representation of data or behaviour allows for rapid assimilation of the "big picture" and enables one to employ geometric intuition which can lead to physical deductions.
		There are obvious limitation to the use of graphical methods (for example, they work best when there are only a few variables to consider, and drawing graphs in more than three dimensions can be awkward), but their widespread use in problems of physics, chemistry, biology, statistics, and more proves their utility.
	pass.maths.org /issue38/features/wilson (2588 words)

	Java Programming with JavaTools -- Mutator Methods for Graphical Objects (Site not responding. Last check: 2007-10-10)
		Two mutator methods are available for changing the positions of graphical objects on the screen.
		If two graphical objects are positioned on the screen in such a way that they overlap, the system must draw them as if one object is positioned above the other.
		Finally, uses of the "hide" and "show" method change the stacking order by placing an object at the top of the stack when it is hidden and then shown.
	www.cs.williams.edu /~cs105s00/labs/javatools/javaTools_21.html (507 words)

	Graphical Methods for Robust Design with Dynamic Characteristics (Site not responding. Last check: 2007-10-10)
		The focus of this article is a comparison of Taguchi's signal-to-noise ratio analysis with graphical methods presented by the authors for a process with dynamic characteristics.
		The results of the Taguchi method on the simulated data is discussed and shortcomings of the method in the eyes of the authors are noted.
		The second method suggested is referred to as the Gamma-plot and is less intuitive than the SS-plot.
	web.utk.edu /~mee/st673/readings/Lunani97.html (424 words)

	NRCCS - Estimation from nonrandomized treatment comparisons using subclassification on propensity scores
		Standard methods of analysis using routine statistical software (e.g., linear or logistic regressions), however, can be quite deceptive for these objectives because they provide no warnings about their propriety.
		The basic idea of propensity score methods is to replace the collection of confounding covariates in the observational study with one function of these covariates, called the propensity score (i.e., the propensity to receive treatment 1 rather than treatment 2), and then to use this score just as if it were the only confounding covariate.
		One critical advantage of propensity score methods is that they can warn the investigator that, because of inadequately overlapping covariate distributions, a particular data base cannot address the causal question at hand without either (a) relying on untrustworthy model-dependent extrapolations, or (b) restricting attention to the type of subject adequately represented in both treatment groups.
	www.symposion.com /nrccs/rubin.htm (5398 words)

	Graphic Methods of Coplanar Force Resolution
		The Parallelogram of Forces Method is one of the graphical methods developed to find the resultant of a coplanar force system.
		The resultant can be represented graphically by the diagonal of the parallelogram formed by using the two force vectors to determine the length of the sides of the parallelogram.
		The graphical methods of force decomposition could be used to determine the magnitude of the forces within the crane.
	web.mit.edu /4.441/1_lectures/1_lecture8/1_lecture8.html (693 words)

	List of graphical methods - Wikipedia, the free encyclopedia
		This is a list of graphical methods with a mathematical basis.
		See also list of computer graphics and descriptive geometry topics.
		This page was last modified 10:01, 29 November 2006.
	en.wikipedia.org /wiki/List_of_graphical_methods (62 words)

	8.4.1.1. Graphical estimation
		Once you have calculated plotting positions from your failure data, and put the points on the appropriate graph paper for your chosen model, parameter estimation follows easily.
		But along with the mechanics of graphical estimation, be aware of both the advantages and the disadvantages of graphical estimation methods.
		graphical methods do not give confidence intervals for the parameters (intervals generated by a regression program for this kind of data are incorrect)
	www.itl.nist.gov /div898/handbook/apr/section4/apr411.htm (608 words)

	Graphical Methods II
		Graphical Methods II Graphical Methods II The following section gives one or more examples of the type of plots which can be created in Splus.
		These arguments are explained in the section on graphical parameters and can be viewed in a second window to serve as a referance throughout this section.
		This can be done by clicking on the graphical parameters link using the middle mouse button.
	www.maths.lancs.ac.uk /~rowlings/Splus/Course/part9.html (538 words)

	Journal of Computational and Graphical Statistics (Site not responding. Last check: 2007-10-10)
		The purpose of JCGS is to improve and extend the use of computational and graphical methods in statistics and data analysis.
		Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing.
		It is also abstracted in Statistical Theory and Method Abstracts.
	jcgs.stat.rice.edu (159 words)

	graphical methods - Hutchinson encyclopedia article about graphical methods (Site not responding. Last check: 2007-10-10)
		Methods of solving equations and problems using the intersections of curves and lines on graphs.
		Graphical Machine Automation Control System - 16 Bit Version
		This information should not be considered complete, up to date, and is not intended to be used in place of a visit, consultation, or advice of a legal, medical, or any other professional.
	encyclopedia.farlex.com /graphical+methods?p (94 words)

	Graphical Methods for Data Analysis (Statistics) (John M. Chambers)
		Graphical Methods for Data Analysis (Statistics) (John M. Chambers)
		If you are an advanced reader, this book is a good one to have lying around for reference (especially since it costs so little and does not take up much space).
		With that said, this book covers a wide variety of methods of analysis ranging from simple histograms to advanced data transformation and also touches on a bit of probability.
	www.mason-defender.net /webstore/us/product/0412052717.htm (157 words)

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