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Don't simply show your data—tell a story with it!Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audienceDetermine the appropriate type of graph for your situationRecognize and eliminate the clutter clouding your informationDirect your audience's attention to the most important parts of your dataThink like a designer and utilize concepts of design in data visualizationLeverage the power of storytelling to help your message resonate with your audienceTogether, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

To feel the emotional force of music, we experience it aurally. But how can we convey musical understanding visually? Visualizing Music explores the art of communicating about music through images. Drawing on principles from the fields of vision science and information visualization, Eric Isaacson describes how graphical images can help us understand music. By explaining the history of music visualizations through the lens of human perception and cognition, Isaacson offers a guide to understanding what makes musical images effective or ineffective and provides readers with extensive principles and strategies to create excellent images of their own. Illustrated with over 300 diagrams from both historical and modern sources, including examples and theories from Western art music, world music, and jazz, folk, and popular music, Visualizing Music explores the decisions made around image creation. Together with an extensive online supplement and dozens of redrawings that show the impact of effective techniques, Visualizing Music is a captivating guide to thinking differently about design that will help music scholars better understand the power of musical images, thereby shifting the ephemeral to material.

A common and simple estimate of variability is the sample range, which is the difference between the maximum and minimum values in the sample. While other measures of variability are preferred in most instances, process owners and operators regularly use range (R) control charts to monitor process variability. The center line and limits of the R charts use constants that are based on the first two moments (mean and variance) of the distribution of the range of normal random variables. Historically, the computation of moments requires the use of tabulated constants approximated using numerical integration. We provide exact results for the moments for sample sizes 2 through 5. For sample sizes from 6 to 1000, we used the differential correction method to find Chebyshev minimax rational-function approximations of the moments. The rational function we recommend for the mean (R-chart constant d2) has a polynomial of order two in the numerator and six in the denominator and achieves a maximum error of 4.4 × 10−6. The function for the standard deviation (R-chart constant d3) has a polynomial of order two in the numerator and seven in the denominator and achieves a maximum error of 1.5 × 10−5. The exact and approximate expressions eliminate the need for table lookup in the control chart design phase.

Process capability index (PCI) is a convenient and useful tool of process quality evaluation that allows a company to have a complete picture of its manufacturing process in order to prevent defective products while ensuring the product quality is at the required level. The aim of this study was to develop a control chart for process incapability index , which differentiates between information related to accuracy and precision. Index measures process inaccuracy as the degree to which the mean departs from the target value, while index measures imprecision in terms of process variation. The most important advantage of using these control charts of , , and is that practitioners can monitor and evaluate both the quality of the process and the differences in process capability. The and charts were instead of Shewhart’s and chart since the process target values and tolerances can be incorporated in the charts for evaluation as a whole, which makes the charts capable of monitoring process stability and quality simultaneously. The proposed , , and control charts enable practitioners to monitor and evaluate process quality as well as differences in process capability. The control charts are defined using probability limits, and operating characteristic (OC) curves used to detect shifts in process quality. The method proposed in this study can easily and accurately determine the process quality capability and a case is used to illustrate the application of control charts of , , and .

The change-point model is an established methodology for the construction of self-starting control charts. Change-point charts are often nonparametric in order to be independent from any specific assumptions about the process distribution. Nonetheless, this methodology is usually implemented by considering all possible splits of a given stream of observations into two adjacent sub-samples. This can make the recent observations too influential and the chart’s signals too dependent on limited evidence. This paper proposes to correct such a distortion by using a window approach, which forces the use of only comparisons based on sub-samples of the same size. The resulting charts are “omnibus”, with respect to their having any kind of shift and also any direction of such shifts. To prove this, this paper focuses on a chart based on the Cramér–von Mises test. We report a simulation study evaluating the average number of readings to obtain a signal after a known shift has occurred. We conclude that, beyond being stable with respect to the direction of the shift, the new chart overcomes its competitors when the distribution heads toward regularity. Finally, the new approach is shown to have successful application to a real problem about air quality.

Since their introduction in the 1920s, control charts have played a key role in process monitoring and control in a variety of areas, from manufacturing to healthcare. Many of these charts are designed to monitor a single process parameter, such as the mean or the variance, of a normally distributed process, although recently, a number of charts have been developed for jointly monitoring the mean and variance. In practice, however, there are processes that follow multi-parameter nonnormal distributions, but the joint monitoring of parameters of nonnormal distributions remains largely unaddressed in the literature. This paper proposes several control charts and monitoring schemes for the origin and the scale parameters of a process that follows the two-parameter (or the shifted) exponential distribution. This distribution arises in various applications in practice, particularly with time to an event data, such as in reliability studies, and has been studied extensively in the statistical testing and estimation literature. Exact derivations and computer simulations are used to study performance properties of the proposed charts. An illustrative example is provided along with a summary and some conclusions.

The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart’s robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives.

It's time for some truly \"Excel-lent\" spreadsheet reporting Beneath the seemingly endless rows and columns of cells, the latest version of Microsoft Excel boasts an astonishing variety of features and capabilities. But how do you go about tapping into some of that power without spending all of your days becoming a spreadsheet guru? It's easy. You grab a copy of the newest edition of Excel Dashboards & Reports For Dummies and get ready to blow the pants off your next presentation audience! With this book, you'll learn how to transform those rows and columns of data into dynamic reports, dashboards, and visualizations. You'll draw powerful new insights from your company's numbers to share with your colleagues – and seem like the smartest person in the room while you're doing it. Excel Dashboards & Reports For Dummies offers: * Complete coverage of the latest version of Microsoft Excel provided in the Microsoft 365 subscription * Strategies to automate your reporting so you don't have to manually crunch the numbers every week, month, quarter, or year * Ways to get new perspectives on old data, visualizing it so you can find solutions no one else has seen before If you're ready to make your company's numbers and spreadsheets dance, it's time to get the book that'll have them moving to your tune in no time. Get Excel Dashboards & Reports For Dummies today.

Statistical Quality Control is a valuable strategy that applies to the statistical technique for monitoring a manufacturing system under particular situations. On the other hand, the fuzzy set theory is an ideal instrument to cope with an unclear situation. The existing studies are restricted, and there is still mystery behind the unclear data. This paper deals with technique: namely, the fuzzy control chart based on fuzzy process capability indices (FCPI) using triangular fuzzy numbers (TFNs). Alpha cut theory is applied in statistical quality control for fuzzy process control industrial application. This is a five-phase study that deals with the control chart using capability indices. The numerical example is also performed using the proposed technique. This paper would help to better assess/understand the manufacturing system data and would explore the application of the fuzzy control techniques.

The homogeneously weighted moving average (HWMA) chart is a recent control chart that has attracted the attention of many researchers in statistical process control (SPC). The HWMA statistic assigns a higher weight to the most recent sample, and the rest is divided equally between the previous samples. This weight structure makes the HWMA chart more sensitive to small shifts in the process parameters when running in zero-state mode. Many scholars have reported its superiority over the existing charts when the process runs in zero-state mode. However, several authors have criticized the HWMA chart by pointing out its poor performance in steady-state mode due to its weighting structure, which does not reportedly comply with the SPC standards. This paper reviews and discusses all research works on HWMA-related charts (i.e., 55 publications) and provides future research ideas and new directions.