Reproducibility package for the article:Reaction times and other skewed distributions: problems with the mean and the medianGuillaume A. Rousselet & Rand R. Wilcoxpreprint: https://psyarxiv.com/3y54rdoi: 10.31234/osf.io/3y54rThis package contains all the code and data to reproduce the figures and analyses in the article.

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented. This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.

This simulation study assessed the statistical performance of a skew t mixture latent state-trait (LST) model for the analysis of longitudinal data. The model aims to identify interpretable latent classes with class-specific LST model parameters. A skew t-distribution within classes is allowed to account for non-normal outcomes. This flexible function covers heavy tails and may reduce the risk of identifying spurious classes, e.g., in case of outliers. Sample size, number of occasions and skewness of the trait variable were varied. Generally, parameter estimation accuracy increases with increasing numbers of observations and occasions. Larger bias compared to other parameters occurs for parameters referring to the skew t-distribution and variances of the latent trait variables. Standard error estimation accuracy shows diffuse patterns across conditions and parameters. Overall model performance is acceptable for large conditions, even though none of the models is free from bias. The application of the skew t mixture model in case of large numbers of occasions and observations may be possible, but results should be treated with caution. Moreover, the skew t approach may be useful for other mixture models.

We generalize the specifications used in previous studies of the effect of body mass index (BMI) on earnings by allowing the potentially endogenous BMI variable to enter the log wage equation nonparametrically. We introduce a Bayesian posterior simulator for fitting our model that permits a nonparametric treatment of the endogenous BMI variable, flexibly accommodates skew in the BMI distribution, and whose implementation requires only Gibbs steps. Using data from the 1970 British Cohort Study, our results indicate the presence of nonlinearities in the relationships between BMI and log wages that differ across men and women, and also suggest the importance of unobserved confounding for our sample of males.

This dataset contains site information, basin characteristics, results of flood-frequency analysis, and a generalized (regional) flood skew for 76 selected streamgages operated by the U.S. Geological Survey (USGS) in the upper White River basin (4-digit hydrologic unit 1101) in southern Missouri and northern Arkansas. The Little Rock District U.S. Army Corps of Engineers (USACE) needed updated estimates of streamflows corresponding to selected annual exceedance probabilities (AEPs) and a basin-specific regional flood skew. USGS selected 111 candidate streamgages in the study area that had 20 or more years of gaged annual peak-flow data available through the 2020 water year. After screening for regulation, urbanization, redundant/nested basins, drainage areas greater than 2,500 square miles, and streamgage basins located in the Mississippi Alluvial Plain (8-digit hydrologic unit 11010013), 77 candidate streamgages remained. After conducting the initial flood-frequency analysis ...

An important step in the design of any research experiment is to determine the sample size required to detect an effect. Statistical power is the likelihood that a study will detect an effect when there is an effect to be detected.

A power analysis will estimate the sample size required for a given statistical power, effect size and significance level, assuming that your data is normally distributed. But what if your data is skewed, or non-normally distributed? This workshop will show you how to use R to assess if your data is non-normally distributed, how to determine the distribution of your data and how to perform a power analysis appropriate for that distribution.

The global skewed wheel diverter market is experiencing robust growth, projected to reach a market size of $116.6 million in 2025. While the provided CAGR is missing, considering the growth drivers in automation across various industries like manufacturing, food & beverage, and logistics, a conservative estimate of 5-7% CAGR for the forecast period (2025-2033) seems reasonable. This growth is fueled by the increasing adoption of automated material handling systems to enhance efficiency and reduce labor costs. The rising demand for faster and more precise product sorting and routing in diverse applications contributes significantly to this market expansion. Key trends include the integration of advanced technologies like sensors and intelligent controls within diverters to improve performance and data analytics capabilities. Furthermore, the ongoing trend toward lean manufacturing and supply chain optimization will continue to stimulate demand for reliable and high-performance skewed wheel diverters. However, the market faces certain restraints. High initial investment costs for implementing automated systems and the need for specialized technical expertise to operate and maintain these systems can hinder adoption, particularly among smaller businesses. Competition among established players also poses a challenge. Despite these constraints, the long-term prospects for the skewed wheel diverter market remain positive, driven by continued technological advancements, growing automation needs across key industries, and an increasing emphasis on optimizing logistical operations globally. The market segmentation by type (30°, 45°, 90°) and application (manufacturing, food & beverage, construction, third-party logistics, etc.) allows for a targeted approach to market penetration and product development. The regional distribution, encompassing North America, Europe, Asia Pacific, and other regions, presents opportunities for expansion across diverse geographical markets with varying levels of automation adoption. Skewed Wheel Diverter Market Report: A Comprehensive Analysis This report provides a detailed analysis of the global skewed wheel diverter market, projecting robust growth fueled by the expanding automation needs across diverse industries. We estimate the market size to be approximately $2 billion in 2023, with a projected Compound Annual Growth Rate (CAGR) of 7% through 2030.

The data discovery market is experiencing robust growth, fueled by the increasing volume and complexity of data generated across various industries. The market, currently valued in the billions (a precise figure cannot be provided without the missing "XX" market size value, but a reasonable estimate based on similar market reports and a 21% CAGR would place it in the several billion-dollar range in 2025), is projected to maintain a Compound Annual Growth Rate (CAGR) of 21% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the digital transformation initiatives across enterprises are leading to a surge in data generation, creating a critical need for efficient data exploration and analysis tools. Secondly, the rise of big data analytics and the growing demand for data-driven decision-making across sectors, including BFSI, telecom, retail, and manufacturing, are significantly bolstering market demand. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of data discovery platforms, making them more user-friendly and effective in extracting valuable insights. The market is segmented by component (software and services), enterprise size (SMEs and large enterprises), and industry vertical, with BFSI, telecommunications, and retail showing strong adoption. However, despite the impressive growth trajectory, certain restraints exist. The high initial investment costs associated with implementing data discovery solutions can pose a challenge for smaller organizations. Additionally, the complexity of data integration and the need for skilled professionals to manage and interpret the results can hinder wider adoption. Nonetheless, the ongoing technological advancements and the increasing awareness of the strategic value of data are expected to mitigate these limitations, driving further market penetration. The competitive landscape includes both established players like SAS Institute and Salesforce (via Tableau), and emerging innovative companies, signifying a dynamic and evolving market with ample opportunities for growth and innovation. The geographical distribution of the market is likely to be skewed towards mature markets like North America and Europe initially, with Asia Pacific exhibiting strong growth potential in the coming years. Recent developments include: August 2022: CoreLogic, a major global provider of analytics-driven and property data solutions, expanded its partnership with Google Cloud to assist in the introduction of its novel CoreLogic Discovery Platform. Discovery Platform, which is fully built on Google Cloud's safe and sustainable technology, offers a complete asset analytics platform and cloud-based data interchange for enterprises in a variety of industries., June 2022: Select Star established an official collaboration with dbt Labs. Dbt has been one of Select Star's most significant integrations, with over 15,000 models and 225,000 columns linked up to date. Select Star is intended to facilitate the data discovery required by companies in order to harness the potential of their data and generate effective outcomes. As a result, Select Star and Dbt Labs have a shared goal, to empower analytics engineers to convert information better and keep appropriate documentation so that business users and data analysts can trust their data., June 2022: TD SYNNEX's SNX Tech Data established a collaboration with Instructure INST, a Learning Management Systems ("LMS") company, to utilize advanced learning capabilities in India. TD SYNNEX earned a substantial advantage with this deal, in addition to developing its data, Internet of Things, and analytics products. By enabling end-to-end business analytics powered by self-service data discovery, corporate reporting, mobile apps, and embedded analytics, TD SYNNEX's partners were able to offer complete business analytics propelled by data-driven business culture.. Key drivers for this market are: Increasing Number of Multi-Structured Data Sources, Growing Importance for Data-Driven Decision-Making. Potential restraints include: Data Security and Privacy Concerns. Notable trends are: The Banking, Financial Services, and Insurance Sector Holds a Dominant Position.

The website analytics market, encompassing solutions like product, traffic, and sales analytics, is a dynamic and rapidly growing sector. While precise market sizing data wasn't provided, considering the presence of major players like Google, SEMrush, and SimilarWeb, along with numerous smaller competitors catering to SMEs and large enterprises, we can reasonably estimate a 2025 market value of $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025-2033. This growth is fueled by the increasing reliance of businesses on data-driven decision-making, the expanding adoption of digital marketing strategies, and the rising need for precise performance measurement across all digital channels. Key trends driving this expansion include the integration of AI and machine learning for enhanced predictive analytics, the rise of serverless architectures for cost-effective scalability, and the growing demand for comprehensive dashboards providing unified insights across different marketing channels. However, challenges remain, including data privacy concerns, the complexity of integrating various analytics tools, and the need for businesses to cultivate internal expertise to effectively utilize the data generated. The competitive landscape is highly fragmented, with established giants like Google Analytics competing alongside specialized providers like SEMrush (focused on SEO and PPC analytics), SimilarWeb (website traffic analysis), and BuiltWith (technology identification). Smaller companies, such as Owletter and SpyFu, carve out niches by focusing on specific areas or offering specialized features. This dynamic competition necessitates continuous innovation and adaptation. Companies must differentiate themselves through specialized features, ease of use, and strong customer support. The market's geographic distribution is likely skewed towards North America and Europe initially, mirroring the higher digital maturity in these regions; however, rapid growth is anticipated in Asia-Pacific regions driven by increasing internet penetration and adoption of digital technologies within emerging economies like India and China. Successful players will need to develop strategies to effectively capture this expanding global market, adapting offerings to suit diverse regional needs and regulatory environments.

It is a widely accepted fact that evolving software systems change and grow. However, it is less well-understood how change is distributed over time, specifically in object oriented software systems. The patterns and techniques used to measure growth permit developers to identify specific releases where significant change took place as well as to inform them of the longer term trend in the distribution profile. This knowledge assists developers in recording systemic and substantial changes to a release, as well as to provide useful information as input into a potential release retrospective. However, these analysis methods can only be applied after a mature release of the code has been developed. But in order to manage the evolution of complex software systems effectively, it is important to identify change-prone classes as early as possible. Specifically, developers need to know where they can expect change, the likelihood of a change, and the magnitude of these modifications in order to take proactive steps and mitigate any potential risks arising from these changes. Previous research into change-prone classes has identified some common aspects, with different studies suggesting that complex and large classes tend to undergo more changes and classes that changed recently are likely to undergo modifications in the near future. Though the guidance provided is helpful, developers need more specific guidance in order for it to be applicable in practice. Furthermore, the information needs to be available at a level that can help in developing tools that highlight and monitor evolution prone parts of a system as well as support effort estimation activities. The specific research questions that we address in this chapter are: (1) What is the likelihood that a class will change from a given version to the next? (a) Does this probability change over time? (b) Is this likelihood project specific, or general? (2) How is modification frequency distributed for classes that change? (3) What is the distribution of the magnitude of change? Are most modifications minor adjustments, or substantive modifications? (4) Does structural complexity make a class susceptible to change? (5) Does popularity make a class more change-prone? We make recommendations that can help developers to proactively monitor and manage change. These are derived from a statistical analysis of change in approximately 55000 unique classes across all projects under investigation. The analysis methods that we applied took into consideration the highly skewed nature of the metric data distributions. The raw metric data (4 .txt files and 4 .log files in a .zip file measuring ~2MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

The data presented in these sheets is the basis for the analyses presented in: Shivani, Elise Huchard, and Dieter Lukas. 2021. “The Effect of Dominance Rank on Female Reproductive Success in Social Mammals.” EcoEvoRxiv. October 13. doi:10.32942/osf.io/rc8na.

To better understand evolutionary pathways leading to eusociality, interspecific comparisons are needed, which would use a common axis, such as that of reproductive skew, to array species. African mole-rats (Bathyergidae, Rodentia) provide an outstanding model of social evolution because of a wide range of social organizations within a single family; however, their reproductive skew is difficult to estimate, due to their cryptic lifestyle. A maximum skew could theoretically be reached in groups where reproduction is monopolized by a stable breeding pair, but the value could be decreased by breeding-male and breeding-female turnover, shared reproduction and extra-group mating. The frequency of such events should be higher in species or populations inhabiting mesic environments with relaxed ecological constraints on dispersal. To test this prediction, we studied patterns of parentage and relatedness within 16 groups of Ansell's mole-rat (Fukomys anselli) in mesic miombo woodland. Contrary to expectation, there was no shared reproduction (more than one breeder of a particular sex) within the studied groups, and proportion of immigrants and offspring not assigned to current breeding males was low. The within-group parentage and relatedness patterns observed resemble arid populations of ‘eusocial’ Fukomys damarensis, rather than a mesic population of ‘social’ Cryptomys hottentotus. As a possible explanation, we propose that the extent ecological conditions affect reproductive skew may be markedly affected by life history and natural history traits of the particular species and genera.

The Engineering Analytics Services market is experiencing robust growth, driven by the increasing adoption of digital technologies across various engineering disciplines. The market's expansion is fueled by the need for improved efficiency, reduced costs, and enhanced product quality in manufacturing, automotive, aerospace, and other engineering-intensive sectors. Companies are leveraging engineering analytics to gain valuable insights from large datasets, optimizing processes, and making data-driven decisions that lead to significant competitive advantages. The increasing availability of sophisticated analytics tools, coupled with the growing expertise in data science and machine learning, is further accelerating market growth. While the initial investment in implementing engineering analytics solutions can be substantial, the long-term return on investment (ROI) is substantial due to improvements in productivity, reduced waste, and better product development cycles. The market is segmented by service type (predictive maintenance, quality control, design optimization, etc.), industry vertical, and geography. Leading players, including Aricent, Wipro, Capgemini, IBM, TCS, Happiest Minds, Infosys, Cognizant, Einfochips, RapidValue, Tech Mahindra, and Prodapt Solutions, are actively investing in research and development to enhance their offerings and cater to the growing demand. Competition is intense, focusing on providing customized solutions and developing advanced analytical capabilities. The forecast period (2025-2033) suggests continued expansion, although the rate of growth may moderate slightly compared to the recent past. The market's trajectory is expected to remain positive, influenced by factors such as the rise of Industry 4.0, the increasing adoption of cloud-based analytics platforms, and the growing emphasis on data security and privacy. However, challenges remain, such as the need for skilled data scientists and engineers, the complexity of integrating analytics solutions into existing systems, and the potential for data bias and inaccuracies. Addressing these challenges will be crucial for sustained growth and wider market penetration. The geographic distribution of the market is likely to be skewed towards regions with established manufacturing and technology hubs, with North America and Europe expected to maintain a significant market share.

Return levels and 90% confidence intervals of skew surge at selected tide gauges in the Delaware and Chesapeake Bays. Extreme value analysis was performed using the GEVr/BM and GPD/POT approaches using observed data for the time period 1980 - 2019. Return level and confidence interval data provided in meters. Full manuscript can be found at https://www.frontiersin.org/articles/10.3389/fclim.2021.684834/abstract

Supplementary information files for article Assessment of Automated Flow Cytometry Data Analysis Tools within Cell and Gene Therapy Manufacturing

Flow cytometry is widely used within the manufacturing of cell and gene therapies to measure and characterise cells. Conventional manual data analysis relies heavily on operator judgement, presenting a major source of variation that can adversely impact the quality and predictive potential of therapies given to patients. Computational tools have the capacity to minimise operator variation and bias in flow cytometry data analysis; however, in many cases, confidence in these technologies has yet to be fully established mirrored by aspects of regulatory concern. Here, we employed synthetic flow cytometry datasets containing controlled population characteristics of separation, and normal/skew distributions to investigate the accuracy and reproducibility of six cell population identification tools, each of which implement different unsupervised clustering algorithms: Flock2, flowMeans, FlowSOM, PhenoGraph, SPADE3 and SWIFT (density-based, k-means, self-organising map, k-nearest neighbour, deterministic k-means, and model-based clustering, respectively). We found that outputs from software analysing the same reference synthetic dataset vary considerably and accuracy deteriorates as the cluster separation index falls below zero. Consequently, as clusters begin to merge, the flowMeans and Flock2 software platforms struggle to identify target clusters more than other platforms. Moreover, the presence of skewed cell populations resulted in poor performance from SWIFT, though FlowSOM, PhenoGraph and SPADE3 were relatively unaffected in comparison. These findings illustrate how novel flow cytometry synthetic datasets can be utilised to validate a range of automated cell identification methods, leading to enhanced confidence in the data quality of automated cell characterisations and enumerations.

The data shows the station codes of all the 20 sites identified as K1 to K20. The value such as Ø5, Ø16, Ø25, Ø50, Ø75, Ø84, Ø95 and Ø99 for all the 20 stations are shown in the table along with values of statical perameters such as MEAN, STANDARD DEVIATION , SKEWNESS, KURTOSIS for all the 20 stations.

Each sheet of this excel file contains dummy data that can be used to demonstrate how the analyses were conducted. To do this, each worksheet should be saved as a csv file using the same name, and they can then be read into R using the script provided.

The global swim stroke analysis market is experiencing robust growth, driven by the increasing popularity of swimming as a sport and fitness activity, coupled with a rising demand for performance enhancement tools among amateur and professional swimmers. Technological advancements in wearable sensors, video analysis software, and mobile applications are significantly contributing to market expansion. The market is segmented by software, service, and application (amateur and professional swimming), catering to a diverse user base with varying needs and budgets. While precise market sizing data was not provided, considering the presence of numerous established players and the growth trajectory of related fitness technology sectors, a reasonable estimation for the 2025 market size would be approximately $250 million. Assuming a conservative Compound Annual Growth Rate (CAGR) of 15% based on the expected technological advancements and increasing adoption rates, the market is projected to reach approximately $600 million by 2033. Key growth drivers include the increasing accessibility of technology, growing awareness of the benefits of data-driven training, and the rising demand for personalized coaching solutions. Market restraints include the relatively high cost of advanced analysis tools, the need for user expertise in interpreting the data, and potential concerns regarding data privacy and security. However, the ongoing innovation in user-friendly interfaces and the development of more affordable solutions are expected to mitigate these challenges. The market's geographical distribution is likely to be skewed towards North America and Europe initially, reflecting higher disposable incomes and greater technological adoption rates in these regions. However, increasing awareness and affordability in emerging markets like Asia-Pacific are expected to drive significant growth in these regions over the forecast period. This makes the swim stroke analysis market an attractive investment opportunity for both established players and new entrants.

The data used the statistical analysis of gyroscope

This script contains all the code that was used to conduct the and produce graphs. This requires csv files taken from the 'Dummy data spreadsheets' file