Journal articles: 'Bar charts'

1

Reese, Allan. "Bay! Bar charts." Significance 4, no. 1 (2007): 41–44. http://dx.doi.org/10.1111/j.1740-9713.2007.00221.x.

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Chan, Chan‐Ieong, Alan Ching Biu Tse, and Frederick H. K. Yim. "Comparing and combining individual x‐charts and x‐bar charts." International Journal of Quality & Reliability Management 20, no. 7 (2003): 827–35. http://dx.doi.org/10.1108/02656710310491230.

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Control charts have played an important role in monitoring the performance of operation processes, ever since their invention. Traditionally, according to Juran's idea and others, x‐bar charts are more sensitive than individual x‐charts. However, such a conclusion is valid only under certain conditions. Individual x‐charts can outperform x‐bar charts in some situations, especially in cases of minor and extreme changes of the center value. Since each chart has its own advantages and disadvantages, the idea of combining the results of these two charts is studied. The finding seems to be useful for practitioners in quality control.

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Keim, D. A., M. C. Hao, and U. Dayal. "Hierarchical pixel bar charts." IEEE Transactions on Visualization and Computer Graphics 8, no. 3 (2002): 255–69. http://dx.doi.org/10.1109/tvcg.2002.1021578.

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Hlawatsch, M., F. Sadlo, M. Burch, and D. Weiskopf. "Scale-Stack Bar Charts." Computer Graphics Forum 32, no. 3pt2 (2013): 181–90. http://dx.doi.org/10.1111/cgf.12105.

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Streit, Marc, and Nils Gehlenborg. "Bar charts and box plots." Nature Methods 11, no. 2 (2014): 117. http://dx.doi.org/10.1038/nmeth.2807.

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Aslam, Muhammad, Ali Hussein AL-Marshadi, and Nasrullah Khan. "A New X-Bar Control Chart for Using Neutrosophic Exponentially Weighted Moving Average." Mathematics 7, no. 10 (2019): 957. http://dx.doi.org/10.3390/math7100957.

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The existing Shewhart X-bar control charts using the exponentially weighted moving average statistic are designed under the assumption that all observations are precise, determined, and known. In practice, it may be possible that the sample or the population observations are imprecise or fuzzy. In this paper, we present the designing of the X-bar control chart under the symmetry property of normal distribution using the neutrosophic exponentially weighted moving average statistics. We will first introduce the neutrosophic exponentially weighted moving average statistic, and then use it to design the X-bar control chart for monitoring the data under an uncertainty environment. We will determine the neutrosophic average run length using the neutrosophic Monte Carlo simulation. The efficiency of the proposed plan will be compared with existing control charts.

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Schnürer, Raimund, Martin Ritzi, Arzu Çöltekin, and René Sieber. "An empirical evaluation of three-dimensional pie charts with individually extruded sectors in a geovisualization context." Information Visualization 19, no. 3 (2020): 183–206. http://dx.doi.org/10.1177/1473871619896103.

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This study compares participants’ performance in extracting information from three-dimensional pie charts with individually extruded sectors in a single map frame against two-dimensional pie and bar charts in adjacent map frames. Specifically, we examine the response accuracy and response times of 181 adults who were asked to (1) identify the highest magnitude, (2) estimate a proportion, (3) perform both at the same time, and (4) solve a map-related task using the two chart setups. For each task, charts were shown on backgrounds with increasing visual complexity: a blank, a borders-only, and a choropleth map. Furthermore, we tested whether participants’ performance improved through additional practice with the two chart types. We did not observe any differences in participants’ aggregated response accuracy or response times between the tested three-dimensional and two-dimensional chart types for the (1) highest magnitude task and (2) proportion task. However, participants solved the (3) combination task with two-dimensional pie and bar charts on a blank background more accurately and were faster in fulfilling the (4) spatial task with three-dimensional pie charts. The first difference, however, leveled for participants who gained more practice and who accomplished the combination task on maps with higher visual complexity, whereas the second difference persisted even for more trained subjects.

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Wijnker, Winnifred, Ionica Smeets, Peter Burger, and Sanne Willems. "Debunking strategies for misleading bar charts." Journal of Science Communication 21, no. 07 (2022): A07. http://dx.doi.org/10.22323/2.21070207.

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Graphs are useful to communicate concisely about complex issues. Although they facilitate intuitive reading of data, trends, and predictions, hasty readers may still come to the wrong conclusions, especially if graphs are misleading due to violated design conventions. To provide evidence about how to prevent misinformation from spreading by misleading graphs, this two-survey experimental study investigates the effectiveness of four correction methods as debunking strategies to correct bar charts with manipulated vertical axes. All four methods showed positive effects. The most effective one is aimed at correcting the initial image by presenting an accurate alternative graph. A reduced effect remained visible after one week.

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Shayanfar, Ali. "Beware of Bar Charts for Plotting Calibration Curves for Analytical Method Development." Journal of AOAC INTERNATIONAL 103, no. 5 (2020): 1424–25. http://dx.doi.org/10.1093/jaoacint/qsaa023.

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Abstract Some researchers, in their published articles in authentic scientific journals, plot calibration curves using bar charts instead of scatter plots using common software such as Excel. Bar charts can significantly affect the apparent linear range and sensitivity of the developed method, and using bar charts as calibration curves gives the wrong results. Therefore, this issue should be considered by researchers in developing analytical methods.

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Hollands, J. G., and Ian Spence. "Judgments of Change and Proportion in Graphical Perception." Human Factors: The Journal of the Human Factors and Ergonomics Society 34, no. 3 (1992): 313–34. http://dx.doi.org/10.1177/001872089203400306.

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Subjects judged change and proportion when viewing graphs in two experiments. Change was judged more quickly and accurately with line and bar graphs than with pie charts or tiered bar graphs, and this difference was larger when the rate of change was smaller. Without a graduated scale, proportion was judged more quickly and accurately with pie charts and divided bar graphs than with line or bar graphs. Perception is direct when it requires simpler or fewer mental operations; we propose that perception of change is direct with line and bar graphs, whereas perception of proportion is direct with pie charts and divided bar graphs. The results are also consistent with the proximity compatibility principle. Suggestions for improving the design of graphical displays are given.

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Morton-Owens, Emily G., and Karen L. Hanson. "Trends at a Glance: A Management Dashboard of Library Statistics." Information Technology and Libraries 31, no. 3 (2012): 36. http://dx.doi.org/10.6017/ital.v31i3.1919.

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<span style="font-family: &quot;Cambria&quot;,&quot;serif&quot;; font-size: 12pt; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Cambria; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: &quot;Times New Roman&quot;; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Systems librarians at an academic medical library created a management data dashboard. Charts were designed using best practices for data visualization and dashboard layout, and include metrics on gatecount, website visits, IM reference chats, circulation, and ILL volume and turnaround time. Several charts draw on EZproxy log data that has been analyzed and linked to other databases to reveal use by different academic departments and user roles (such as faculty or student). Most charts are bar charts and include a linear regression trendline. The implementation uses Perl scripts to retrieve data from eight different sources and add it to a MySQL data warehouse, from which PHP/Javascript webpages use Google Chart Tools to create the dashboard charts.</span>

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Hollands, J. G., and Ian Spence. "Integral and Separable Dimensions in Graph Reading." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (1997): 1352–56. http://dx.doi.org/10.1177/1071181397041002138.

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Subjects sorted decks of cards depicting pie charts and divided bar graphs on two criteria: the proportion shown in the graph, and the graph's overall size, or scaling. Sorting times and errors were measured. For divided bars, performance was impaired when subjects were required to sort the proportion and the overall scaling varied. No such impairment occurred for pie charts. The results suggest that proportion and scaling are integral dimensions for divided bar graphs, but separable dimensions for pie charts. Subjects can judge angles or slopes with pie charts having different scaling, but must estimate a ratio prior to classification with different-scale divided bars. In sum, showing proportions with divided bar graphs can be problematic if the scaling of the graph varies, but pie charts are not similarly affected.

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Al-Zaidy, Rabah, and C. Giles. "A Machine Learning Approach for Semantic Structuring of Scientific Charts in Scholarly Documents." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (2017): 4644–49. http://dx.doi.org/10.1609/aaai.v31i2.19088.

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Large scholarly repositories are designed to provide scientists and researchers with a wealth of information that is retrieved from data present in a variety of formats. A typical scholarly document contains information in a combined layout of texts and graphic images. Common types of graphics found in these documents are scientific charts that are used to represent data values in a visual format. Experimental results are rarely described without the aid of one form of a chart or another, whether it is 2D plot, bar chart, pie chart, etc. Metadata of these graphics is usually the only content that is made available for search by user queries. By processing the image content and extracting the data represented in the graphics, search engines will be able to handle more specific queries related to the data itself. In this paper we describe a machine learning based system that extracts and recognizes the various data fields present in a bar chart for semantic labeling. Our approach comprises of a graphics and text separation and extraction phase, followed by a component role classification for both text and graphic components that are in turn used for semantic analysis and representation of the chart. The proposed system is tested on a set of over 200 bar charts extracted from over 1,000 scientific articles in PDF format.

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Alemi, Farrokh, and Thomas Sullivan. "Tutorial on Risk Adjusted X-Bar Charts." Quality Management in Health Care 9, no. 3 (2001): 57–65. http://dx.doi.org/10.1097/00019514-200109030-00007.

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Demir, Seniz, David Oliver, Edward Schwartz, et al. "InteractiveSIGHT: textual access to simple bar charts." New Review of Hypermedia and Multimedia 16, no. 3 (2010): 245–79. http://dx.doi.org/10.1080/13614568.2010.534186.

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Abell, Walter, Clare Churcher, and Jason Lee. "An evaluation of interactive stacked bar charts." International Journal of Computer Applications in Technology 34, no. 4 (2009): 285. http://dx.doi.org/10.1504/ijcat.2009.024081.

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Elzer, Stephanie, Sandra Carberry, and Ingrid Zukerman. "The automated understanding of simple bar charts." Artificial Intelligence 175, no. 2 (2011): 526–55. http://dx.doi.org/10.1016/j.artint.2010.10.003.

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Lin, Yun, Yi Tang, Yanfei Zhu, Fangbin Song, and Wenzhe Tang. "A Perception Study for Unit Charts in the Context of Large-Magnitude Data Representation." Symmetry 15, no. 1 (2023): 219. http://dx.doi.org/10.3390/sym15010219.

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Unit charts are a common type of chart for visualizing scientific data. A unit chart is a chart used to communicate quantities of things by making the number of symbols on the chart proportional to the number of items represented. An accurate perception of the order of magnitude is essential to evaluating whether a unit chart can effectively convey information. Previous studies have primarily focused on perceptual properties at small order-of-magnitude scales or the efficacy of pictographs in unit charts. However, few researchers have explored the perceptual effectiveness of unit charts when representing large orders of magnitude. In this study, we performed a series of sampling measurements to investigate the visual–perceptual characteristics of unit charts when representing asymmetric interactions such as large-scale numbers. The results showed that under the restriction of the current conventional display medium, unit charts still offer a significant advantage over bar charts in a single-scale visual overview. However, this comes at the cost of a longer response time. Although this study constitutes basic research, accumulating evidence about how people reason about magnitudes beyond human perception is critical to the field of information science. This study may contribute to understanding how viewers perceive unit charts and the factors that influence graphical perception. This article provides some specific guidelines for designing unit charts that may be useful to visualization designers.

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Rolfsen, Christian Nordahl, and Christoph Merschbrock. "Acceptance of Construction Scheduling Visualizations: Bar-charts, Flowline-charts, Or Perhaps BIM?" Procedia Engineering 164 (2016): 558–66. http://dx.doi.org/10.1016/j.proeng.2016.11.658.

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Rahn, G. E., S. G. Kapoor, and R. E. DeVor. "Single-Subgroup Performance Measures and Diagnostic Procedures for X-Bar Control Charts." Journal of Engineering for Industry 116, no. 2 (1994): 216–24. http://dx.doi.org/10.1115/1.2901933.

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Although Shewhart control charts have had a tremendous impact on quality improvement, the inability to precisely measure chart performance has limited their role, and subsequently overall effectiveness in the control of manufacturing processes. Measures of performance in terms of operational characteristics (OC) are defined on two distinct levels: (a) single-subgroup level, which examines the probability of a rule violation at any given subgroup (b) multiple-subgroup level, which considers the probability of one or more rule violations throughout process monitoring. Single-subgroup performance measures for X-bar charts that employ four rules are formulated. These measures are exact expressions of operational characteristics, except for the numerical approximation to the integral of the normal distribution. Applications of these models to simulated data demonstrate their accuracy in predicting chart performance. In addition, a diagnostic methodology is described which utilizes the derived performance measures to predict the mean of a shifted distribution. The proposed diagnostic procedure is illustrated in validation and application examples.

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Streiner, David L. "Speaking Graphically: An Introduction to Some Newer Graphing Techniques." Canadian Journal of Psychiatry 42, no. 4 (1997): 388–94. http://dx.doi.org/10.1177/070674379704200405.

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The vast majority of graphs appearing in the psychiatric literature consist of the traditional line graphs, histograms, and bar charts. Over the past decade, new graphing techniques have appeared which make the data easier to read and which present much more information than simply group means and confidence intervals. These methods include horizontal bar charts, dot charts, stem-and-leaf plots, box plots, and notched box plots. This paper describes these new techniques, as well as older ones, such as smoothing, and warns against using some of the options found in graphics programs: 3-dimensional (3-D) graphs, stacked graphs, and pie charts.

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Talbot, Justin, Vidya Setlur, and Anushka Anand. "Four Experiments on the Perception of Bar Charts." IEEE Transactions on Visualization and Computer Graphics 20, no. 12 (2014): 2152–60. http://dx.doi.org/10.1109/tvcg.2014.2346320.

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Cox, Nicholas J. "Speaking Stata: Multiple bar Charts in Table form." Stata Journal: Promoting communications on statistics and Stata 16, no. 2 (2016): 491–510. http://dx.doi.org/10.1177/1536867x1601600214.

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Perks, Pat, and Stephanie Prestage. "Bar and Pie Charts: Ideas for the Classroom." Teaching Statistics 22, no. 2 (2000): 52–55. http://dx.doi.org/10.1111/1467-9639.00019.

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Hegazy, Tarek, Emad Elbeltagi, and Kehui Zhang. "Keeping Better Site Records Using Intelligent Bar Charts." Journal of Construction Engineering and Management 131, no. 5 (2005): 513–21. http://dx.doi.org/10.1061/(asce)0733-9364(2005)131:5(513).

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Keim, Daniel A., Ming C. Hao, Umesh Dayal, and Meichun Hsu. "Pixel Bar Charts: A Visualization Technique for Very Large Multi-Attribute Data Sets." Information Visualization 1, no. 1 (2002): 20–34. http://dx.doi.org/10.1057/palgrave.ivs.9500003.

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Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data presenting only a very small number of data values (as in the case of bar charts) and may have a high degree of overlap occluding a significant portion of the data values (as in the case of the x-y plots). In this article, the authors therefore propose a generalization of traditional bar charts and x-y plots, which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present detailed information of the data records. The so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. It is shown that, for an effective pixel placement, a complex optimization problem has to be solved. The authors then present an algorithm which efficiently solves the problem. The application to a number of real-world e-commerce data sets shows the wide applicability and usefulness of this new idea, and a comparison to other well-known visualization techniques (parallel coordinates and spiral techniques) shows a number of clear advantages.

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Tian, Tao, and Bo Li. "Statistic Analysis on Coal and Gas Outburst Accident in China Coal Mines from 2006 to 2012." Applied Mechanics and Materials 291-294 (February 2013): 847–52. http://dx.doi.org/10.4028/www.scientific.net/amm.291-294.847.

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A statistical analysis was conducted on the 182 gas accidents occurring 2006 to 2012 mainly from the two aspects of number of accidents and death toll of coal mines．By applying pie charts，bar charts，packing diagram and tendency charts，the rule of gas accidents was distinctly revealed．

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Nordahl-Rolfsen, Christian, and Christoph Merschbrock. "Corrigendum to Acceptance of Construction Scheduling Visualizations: Bar-charts, Flowline-charts, Or Perhaps BIM?" Procedia Engineering 164 (2016): 583. http://dx.doi.org/10.1016/j.proeng.2017.02.225.

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Hink, Jessica K., Michael S. Wogalter, and Jason K. Eustace. "Display of Quantitative Information: Are Grables better than Plain Graphs or Tables?" Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 23 (1996): 1155–59. http://dx.doi.org/10.1177/154193129604002302.

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Previous research is equivocal on the most efficient, effective methods for displaying quantitative information in tables and graphs. Guidelines suggest different display types are more appropriate for certain purposes but not others. However, there is little empirical evidence to support the recommendations. This study examines several methods of displaying quantitative information (e.g., line graphs, bar charts, tables) factorially crossed with different kinds of data extraction questions (i.e., inquiries about trends, comparisons, and exact numerical quantities). Results showed that tables, bar grables (combined bar graph and table) and line grables produced the most accurate responses, and line graphs and bar charts produced the fastest responses across question types. Results are discussed with respect to prior theoretical work and the potential benefits of hybrid forms of quantitative data displays for multiple kinds of data extraction inquiries.

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Jeong, Yuin, Sangheon Oh, Younah Kang, and Sung-Hee Kim. "Impacts of Visualizations on Decoy Effects." International Journal of Environmental Research and Public Health 18, no. 23 (2021): 12674. http://dx.doi.org/10.3390/ijerph182312674.

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The decoy effect is a well-known, intriguing decision-making bias that is often exploited by marketing practitioners to steer consumers towards a desired purchase outcome. It demonstrates that an inclusion of an alternative in the choice set can alter one’s preference among the other choices. Although this decoy effect has been universally observed in the real world and also studied by many economists and psychologists, little is known about how to mitigate the decoy effect and help consumers make informed decisions. In this study, we conducted two experiments: a quantitative experiment with crowdsourcing and a qualitative interview study—first, the crowdsourcing experiment to see if visual interfaces can help alleviate this cognitive bias. Four types of visualizations, one-sided bar chart, two-sided bar charts, scatterplots, and parallel-coordinate plots, were evaluated with four different types of scenarios. The results demonstrated that the two types of bar charts were effective in decreasing the decoy effect. Second, we conducted a semi-structured interview to gain a deeper understanding of the decision-making strategies while making a choice. We believe that the results have an implication on showing how visualizations can have an impact on the decision-making process in our everyday life.

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Hsu, Hsi-Mei, and Yan-Kwang Chen. "Grey relation pattern recogniser for X-bar control charts." International Journal of Manufacturing Technology and Management 1, no. 2/3 (2000): 271. http://dx.doi.org/10.1504/ijmtm.2000.001341.

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Kasmana, K., and F. M. Adipraja. "The Benefits of Using Bar Charts in Company Websites." IOP Conference Series: Materials Science and Engineering 662 (November 20, 2019): 032003. http://dx.doi.org/10.1088/1757-899x/662/3/032003.

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Diday, Edwin. "Principal component analysis for bar charts and metabins tables." Statistical Analysis and Data Mining: The ASA Data Science Journal 6, no. 5 (2013): 403–30. http://dx.doi.org/10.1002/sam.11188.

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Hughes, Brian M. "Just Noticeable Differences in 2D and 3D Bar Charts: A Psychophysical Analysis of Chart Readability." Perceptual and Motor Skills 92, no. 2 (2001): 495–503. http://dx.doi.org/10.2466/pms.2001.92.2.495.

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Al-Dabbagh, Mohammed Mumtaz, Naomie Salim, Amjad Rehman, et al. "Intelligent Bar Chart Plagiarism Detection in Documents." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/612787.

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This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

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Rodbard, David. "Display of Glucose Distributions by Date, Time of Day, and Day of Week: New and Improved Methods." Journal of Diabetes Science and Technology 3, no. 6 (2009): 1388–94. http://dx.doi.org/10.1177/193229680900300619.

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Objective: There is a need for improved methods for display of glucose distributions to facilitate comparisons by date, time of day, day of the week, and other variables for data obtained using self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM). Method: Stacked bar charts are utilized for multiple ranges of glucose values, e.g., very low, low, borderline low, target range, borderline high, high, and very high. Glucose ranges for these categories can be defined by the user, e.g., <40, 40–70, 71–80, 81–140, 141–180, 181–250, and 251–400 mg/dl. Glucose distributions can be displayed by time of day, in relation to meals, by date, or by day of week. The graphic display can be generated using general purpose spreadsheet software such as Microsoft Excel or with special purpose software. Result: Stacked bar charts are extremely compact and effective. They facilitate comparison of multiple days, multiple time segments within a day, preprandial and postprandial glucose levels, days of the week, treatment periods, patients, and groups of patients. They are superior to use of pie charts in terms of compactness and in their ability to facilitate comparisons using multiple criteria and multiple subsets of the data. One can identify episodes of hypoglycemia and hyperglycemia and can display standard errors of estimates of percentages. Interpretation of these graphs is readily learned and requires minimal training. Conclusion: Use of stacked bar charts is generally superior to use of pie charts for display of glucose distributions and can potentially facilitate the analysis and interpretation of SMBG and CGM data.

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Becker, L. E., and W. L. Cleghorn. "The Buckling Behavior of Rectangular-Bar Helical Compression Springs." Journal of Applied Mechanics 61, no. 2 (1994): 491–93. http://dx.doi.org/10.1115/1.2901479.

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Le, Thai, Cecilia Aragon, Hilaire J. Thompson, and George Demiris. "Elementary Graphical Perception for Older Adults: A Comparison with the General Population." Perception 43, no. 11 (2014): 1249–60. http://dx.doi.org/10.1068/p7801.

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We identified the graphical perceptual information needs of older adults (≥ 60 years of age) through a set of psychophysical experiments on bar, stacked, and pie charts. The results are compared with those of a general population (< 60 years of age). We conducted the experiments as online remote studies with 202 total participants across two experimental types: (1) comparison judgments of graphs (50 older adults, 50 general population) and (2) proportion judgments of graphs (52 older adults, 50 general population). Older adults took longer than the general population to complete tasks across both comparison (4.09 s) and proportion judgments (3.66 s). However, this translated to an approximately equal level of perceptual accuracy. Bar charts were the most effective graphical display when considering both speed and accuracy. Older adults were more accurate using pie charts compared with the general population in the comparison task.

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Erzin, Adil, Georgii Melidi, Stepan Nazarenko, and Roman Plotnikov. "A 3/2-approximation for big two-bar charts packing." Journal of Combinatorial Optimization 42, no. 1 (2021): 71–84. http://dx.doi.org/10.1007/s10878-021-00741-1.

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Xiong, Cindy, Vidya Setlur, Benjamin Bach, Eunyee Koh, Kylie Lin, and Steven Franconeri. "Visual Arrangements of Bar Charts Influence Comparisons in Viewer Takeaways." IEEE Transactions on Visualization and Computer Graphics 28, no. 1 (2022): 955–65. http://dx.doi.org/10.1109/tvcg.2021.3114823.

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Keim, Daniel A., Ming C. Hao, Umeshwar Dayal, and Martha Lyons. "Value-Cell Bar Charts for Visualizing Large Transaction Data Sets." IEEE Transactions on Visualization and Computer Graphics 13, no. 4 (2007): 822–33. http://dx.doi.org/10.1109/tvcg.2007.1023.

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MARCELLUS, RICHARD L. "Operating characteristics of X-bar charts with asymmetric control limits." IIE Transactions 33, no. 12 (2001): 1059–69. http://dx.doi.org/10.1080/07408170108936896.

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Liu, Hui-Rong, Chao-Yu Chou, and Chung-Ho Chen. "Minimum-loss design of x-bar charts for correlated data." Journal of Loss Prevention in the Process Industries 15, no. 6 (2002): 405–11. http://dx.doi.org/10.1016/s0950-4230(02)00043-8.

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Kolodziej, Richard, Friedrich W. Hesse, and Tanja Engelmann. "Improving negotiations with bar charts: The advantages of priority awareness." Computers in Human Behavior 60 (July 2016): 351–60. http://dx.doi.org/10.1016/j.chb.2016.02.079.

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ZHANG, YING, and PHILIPPE CASTAGLIOLA. "RUN RULES $\bar{X}$ CHARTS WHEN PROCESS PARAMETERS ARE UNKNOWN." International Journal of Reliability, Quality and Safety Engineering 17, no. 04 (2010): 381–99. http://dx.doi.org/10.1142/s0218539310003858.

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Supplementary Run Rules are applied to increase the power of the standard Shewhart [Formula: see text] chart for detecting small shift. The performance of this chart is usually evaluated under the assumption of known parameters. However, in practice, the process parameters are rarely known and, usually estimated from an in-control Phase I data set. In this paper, we evaluate (using a Markov Chain model) the performances of the Run Rules [Formula: see text] chart when the process parameters are estimated, and compare them when the process parameters are assumed known. We draw the conclusion that these performances are quite different when the number of samples used during the phase I is small and we suggest new chart parameters in order to overcome this problem.

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Sim, C. H. "Combined X-bar and CRL Charts for the Gamma Process." Computational Statistics 18, no. 3-4 (2003): 547–63. http://dx.doi.org/10.1007/bf03354616.

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Bakir, Saad T. "A Nonparametric Shewhart-Type Quality Control Chart for Monitoring Broad Changes in a Process Distribution." International Journal of Quality, Statistics, and Reliability 2012 (September 11, 2012): 1–10. http://dx.doi.org/10.1155/2012/147520.

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This paper develops a distribution-free (or nonparametric) Shewhart-type statistical quality control chart for detecting a broad change in the probability distribution of a process. The proposed chart is designed for grouped observations, and it requires the availability of a reference (or training) sample of observations taken when the process was operating in-control. The charting statistic is a modified version of the two-sample Kolmogorov-Smirnov test statistic that allows the exact calculation of the conditional average run length using the binomial distribution. Unlike the traditional distribution-based control charts (such as the Shewhart X-Bar), the proposed chart maintains the same control limits and the in-control average run length over the class of all (symmetric or asymmetric) continuous probability distributions. The proposed chart aims at monitoring a broad, rather than a one-parameter, change in a process distribution. Simulation studies show that the chart is more robust against increased skewness and/or outliers in the process output. Further, the proposed chart is shown to be more efficient than the Shewhart X-Bar chart when the underlying process distribution has tails heavier than those of the normal distribution.

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MacTavish, Mia, Lakin Wecker, and Faramarz Samavati. "Perspective Charts in a Multi-Foci Globe-Based Visualization of COVID-19 Data." ISPRS International Journal of Geo-Information 11, no. 4 (2022): 223. http://dx.doi.org/10.3390/ijgi11040223.

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The use of perspective projection in data visualization has been shown to potentially aid with the perception of small values in datasets with important variations at multiple scales. We integrate perspective charts, which use perspective projection in their designs, into a geospatial visualization application for global COVID-19 data. We perform an evaluation through Amazon Mechanical Turk to evaluate the readability of these visualizations compared to traditional methods, when tools such as interactive techniques are used. Results of our evaluation show that participants more accurately retrieved small values from perspective chart visualizations than traditional bar charts on the globe. The use of perspective projection in an interactive system allows for users to read data with important variations at multiple scales without affecting the overall perception of scale in datasets.

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Rusli, Abdul Rahman, Hastuty, Sitti Masyitah Meliyana R., and Ranak Lince. "Use of Excel Software for Graphing for PAUD Luwu Timur Teachers." Mattawang: Jurnal Pengabdian Masyarakat 3, no. 1 (2022): 16–20. http://dx.doi.org/10.35877/454ri.mattawang793.

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The training aims to provide insight, abilities, and skills of PAUD Luwu Timur teachers in graphing using excel software. The method used is to conduct lectures, practice, and discussions between teachers and the implementation team. The result of this training is that the teachers are skilled and proficient in using Microsoft excel features in drawing graphs or charts, including Bar charts, Scatter, Line charts, and Pie, the teachers are also pleased and interested in using the features possessed by Microsoft Excel.

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Morris, John H., Allan Kuchinsky, Thomas E. Ferrin, and Alexander R. Pico. "enhancedGraphics: a Cytoscape app for enhanced node graphics." F1000Research 3 (July 1, 2014): 147. http://dx.doi.org/10.12688/f1000research.4460.1.

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enhancedGraphics (http://apps.cytoscape.org/apps/enhancedGraphics) is a Cytoscape app that implements a series of enhanced charts and graphics that may be added to Cytoscape nodes. It enables users and other app developers to create pie, line, bar, and circle plots that are driven by columns in the Cytoscape Node Table. Charts are drawn using vector graphics to allow full-resolution scaling.

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Journal articles on the topic 'Bar charts'

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