Traffic analytics, rankings, and competitive metrics for skew.com as of June 2025

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 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 ...

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).

This dataset represents a thoroughly transformed and enriched version of a publicly available customer shopping dataset. It has undergone comprehensive processing to ensure it is clean, privacy-compliant, and enriched with new features, making it highly suitable for advanced analytics, machine learning, and business research applications.

The transformation process focused on creating a high-quality dataset that supports robust customer behavior analysis, segmentation, and anomaly detection, while maintaining strict privacy through anonymization and data validation.

➡ Data Cleaning and Preprocessing : Duplicates were removed. Missing numerical values (Age, Purchase Amount, Review Rating) were filled with medians; missing categorical values labeled “Unknown.” Text data were cleaned and standardized, and numeric fields were clipped to valid ranges.

➡ Feature Engineering : New informative variables were engineered to augment the dataset’s analytical power. These include: • Avg_Amount_Per_Purchase: Average purchase amount calculated by dividing total purchase value by the number of previous purchases, capturing spending behavior per transaction. • Age_Group: Categorical age segmentation into meaningful bins such as Teen, Young Adult, Adult, Senior, and Elder. • Purchase_Frequency_Score: Quantitative mapping of purchase frequency to annualized values to facilitate numerical analysis. • Discount_Impact: Monetary quantification of discount application effects on purchases. • Processing_Date: Timestamp indicating the dataset transformation date for provenance tracking.

➡ Data Filtering : Rows with ages outside 0–100 were removed. Only core categories (Clothing, Footwear, Outerwear, Accessories) and the top 25% of high-value customers by purchase amount were retained for focused analysis.

➡ Data Transformation : Key numeric features were standardized, and log transformations were applied to skewed data to improve model performance.

➡ Advanced Features : Created a category-wise average purchase and a loyalty score combining purchase frequency and volume.

➡ Segmentation & Anomaly Detection : Used KMeans to cluster customers into four groups and Isolation Forest to flag anomalies.

➡ Text Processing : Cleaned text fields and added a binary indicator for clothing items.

➡ Privacy : Hashed Customer ID and removed sensitive columns like Location to ensure privacy.

➡ Validation : Automated checks for data integrity, including negative values and valid ranges.

This transformed dataset supports a wide range of research and practical applications, including customer segmentation, purchase behavior modeling, marketing strategy development, fraud detection, and machine learning education. It serves as a reliable and privacy-aware resource for academics, data scientists, and business analysts.

Fukomys_anselli_genotypesThe genotypes of nine microsatellite loci obtained using the fragment analysis (ABI PRISM 3130) and scored using th GeneMapper 3.7. For each individual, its ID, sex, reproductive status, weight and group membership is shown.

Linear regression is one of the most used statistical techniques in neuroscience, including the study of the neuropathology of Alzheimer’s disease (AD) dementia. However, the practical utility of this approach is often limited because dependent variables are often highly skewed and fail to meet the assumption of normality. Applying linear regression analyses to highly skewed datasets can generate imprecise results, which lead to erroneous estimates derived from statistical models. Furthermore, the presence of outliers can introduce unwanted bias, which affect estimates derived from linear regression models. Although a variety of data transformations can be utilized to mitigate these problems, these approaches are also associated with various caveats. By contrast, a robust regression approach does not impose distributional assumptions on data allowing for results to be interpreted in a similar manner to that derived using a linear regression analysis. Here, we demonstrate the utility of applying robust regression to the analysis of data derived from studies of human brain neurodegeneration where the error distribution of a dependent variable does not meet the assumption of normality. We show that the application of a robust regression approach to two independent published human clinical neuropathologic data sets provides reliable estimates of associations. We also demonstrate that results from a linear regression analysis can be biased if the dependent variable is significantly skewed, further indicating robust regression as a suitable alternate approach.

Rate (in percent) of correctly detected number of major factors under different variations of PA when only major factors were present under a skewed response 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.

Report of Skew Bevel Helical Gearbox Market is currently supplying a comprehensive analysis of many things which are liable for economy growth and factors which could play an important part in the increase of the marketplace in the prediction period. The record of Skew Bevel Helical Gearbox Industry is providing the thorough study on the grounds of market revenue discuss production and price happened. The report also provides the overview of the segmentation on the basis of area, contemplating the particulars of earnings and sales pertaining to marketplace.

Overhead Crane Anti-Skew Control Market Outlook

According to our latest research, the global market size for Overhead Crane Anti-Skew Control reached USD 1.42 billion in 2024, reflecting a robust demand across industrial sectors. The market is anticipated to progress at a CAGR of 6.8% from 2025 to 2033, with the total value forecasted to attain USD 2.76 billion by 2033. This growth trajectory is underpinned by the increasing emphasis on operational safety, reduction of equipment downtime, and the rising automation trends within manufacturing and logistics environments worldwide. The market’s expansion is further fueled by technological advancements, regulatory compliance requirements, and the need for higher efficiency in material handling processes.

One of the primary growth drivers for the Overhead Crane Anti-Skew Control Market is the escalating focus on workplace safety and accident prevention in heavy industries such as manufacturing, construction, and logistics. Skewing in overhead cranes can lead to significant operational hazards, including structural damage to cranes, rails, and supporting infrastructure, as well as increased risks to personnel. As regulatory bodies worldwide continue to tighten safety mandates, industries are compelled to adopt advanced anti-skew control systems, both hardware and software-based, to ensure compliance and mitigate liability. This regulatory push is particularly pronounced in developed regions, where workplace safety standards are stringently enforced, but is increasingly being mirrored in emerging markets as well, contributing to the global growth of the market.

Technological innovation is another critical factor propelling the Overhead Crane Anti-Skew Control Market forward. The integration of smart sensors, real-time monitoring, and predictive analytics into anti-skew control systems has revolutionized the way overhead cranes are managed. These advancements enable early detection of skewing issues, automatic correction mechanisms, and seamless integration with broader industrial automation systems. The convergence of the Industrial Internet of Things (IIoT) and Industry 4.0 paradigms is fostering the development of more intelligent and interconnected anti-skew solutions, which not only enhance operational efficiency but also reduce maintenance costs and prolong equipment lifespan. As industries strive for higher productivity and reduced operational disruptions, the adoption of such advanced anti-skew control systems is expected to surge.

Furthermore, the market is experiencing growth due to the increasing adoption of overhead cranes in diverse applications beyond traditional manufacturing, such as warehousing, shipping & logistics, and specialized sectors like energy & power and paper & pulp. The expansion of global trade, the proliferation of e-commerce, and the subsequent demand for efficient warehousing and logistics infrastructure have amplified the need for reliable and safe crane operations. As companies across these sectors invest in modernizing their material handling capabilities, the implementation of anti-skew control systems becomes a pivotal aspect of their operational upgrades, driving steady growth in the market.

From a regional perspective, Asia Pacific continues to dominate the Overhead Crane Anti-Skew Control Market, accounting for the largest share in 2024, followed by North America and Europe. The rapid industrialization and infrastructure development in countries like China, India, and Southeast Asian nations are fueling the demand for advanced material handling solutions, including anti-skew control systems. North America and Europe, characterized by mature industrial bases and stringent safety norms, are witnessing significant investments in upgrading aging crane infrastructure with state-of-the-art anti-skew technologies. Meanwhile, regions such as Latin America and the Middle East & Africa are emerging as potential growth hotspots, driven by ongoing industrialization and increasing awareness of workplace safety standards.

Product Type

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.

The global skewed wheel diverter market is experiencing steady growth, projected to reach a market size of $93 million in 2025. This represents a Compound Annual Growth Rate (CAGR) of 3.3% from 2019 to 2033, indicating a consistent demand for these efficient material handling solutions. Key drivers include the increasing automation within various industries like manufacturing, logistics, and packaging, where precise and rapid product diversion is crucial for optimizing production lines and reducing downtime. Rising e-commerce activity and the consequent need for high-throughput sorting systems further fuel market expansion. Furthermore, advancements in technology, leading to more robust and reliable skewed wheel diverters with improved accuracy and speed, contribute significantly to the market's growth trajectory. While specific restraints are not provided, potential challenges could include initial investment costs associated with implementing new systems, the need for skilled labor for installation and maintenance, and competition from alternative material handling technologies. The market's segmentation likely includes diverters based on capacity, material handling type, and application industry, although specific segment data is unavailable. Leading players in the skewed wheel diverter market include Bastian Solutions, Damon Group, Dematic, Fives Group, Gosunm, Handling Systems Inc., MHS Conveyor, MJC Co., Ltd, Premier Tech Chronos, Roach Manufacturing Corporations, Shanghai Simba Automation Technology Co., Ltd., Taylor Material Handling & Conveyor, and WW Cannon. These companies are continuously innovating to enhance their product offerings, focusing on factors like improved durability, ease of maintenance, and integration with existing automated systems. This competitive landscape drives ongoing improvements in product quality and efficiency, which benefits end-users across various industries. The continued growth in e-commerce and automation will likely lead to further innovation and market consolidation in the coming years. This in-depth report provides a comprehensive analysis of the global skewed wheel diverter market, offering valuable insights for industry stakeholders, investors, and strategic decision-makers. The report projects a market value exceeding $2 billion by 2030, driven by robust growth in e-commerce and the increasing automation of material handling systems. We delve into key market trends, competitive landscape, and future growth potential, focusing on crucial factors driving adoption and potential challenges.

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.

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.

Supporting figures and tables. Figure S1, the correlation of CD127hi or CD127 lo CCR7−CD45RA+/−CD8+ T cells with age. Healthy donor (A); OSCC patient (B). Significant differences compared with each group using Spearman correlation tests are indicated by asterisks (*p<0.05, p<0.01). Figure S2, the distribution of CD127hi or CD127 lo CCR7−CD45RA+/− CD8+ T cell between age-matched healthy donors and OSCC patients. The expression of (A) CD127hi or (B) CD127lo in CCR7−CD45RA−CD8+ T cell; or (C) CD127hi or (D) CD127lo in CCR7−CD45RA+CD8+ T cell were examined in PBMC from healthy donors (n = 11) or OSCC patients (n = 10) with matched age of 19 to 45 years old. Significant differences compared with each group using t-tests are indicated by asterisks (p<0.01 and ***p<0.001). Figure S3, analysis of CD127 expressing CD8+ T cells by FACS. Representative FACS analysis of CD127 expression in CCR7−CD45RA+/−CD8+ T cells in healthy donor PBMC (A), or OSCC patient's PBMC (B), tumor infiltrated lymphocytes (C), and lymph node (D). Figure S4, characterization of the surface molecule expression in CD127hi and CD127lo CCR7−CD45RA− or CCR7−CD45RA− subsets. (A) The surface CD28, PD-1 and CD57 expression pattern on these four subsets of CD8+ T cells. The CD127hi cells are presented as a solid line, and the CD127lo cells are presented as a dotted line. (B) The columns indicate the percentage of surface marker expression in different individuals from three groups (Healthy donor, n = 3; OSCC patients, n = 6.). Significant differences compared with each group using t-tests are indicated by asterisks (*p<0.05, **p<0.01 and ***p<0.001). Figure S5, concentration of plasmatic IL-7 in OSCC and healthy donors. The IL-7 concentration was determined by ELISA. The data was represented as mean pg/ml ± SD (healthy donor, n = 28; OSCC patients, n = 64). Statistical analysis was done by t-tests (**p<0.01). Table S1, clinicopathologic characteristics of the patients with OSCC and normal controls. (DOCX)

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

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.