Regression ranks among the most popular statistical analysis methods across many research areas, including psychology. Typically, regression coefficients are displayed in tables. While this mode of presentation is information-dense, extensive tables can be cumbersome to read and difficult to interpret. Here, we introduce three novel visualizations for reporting regression results. Our methods allow researchers to arrange large numbers of regression models in a single plot. Using regression results from real-world as well as simulated data, we demonstrate the transformations which are necessary to produce the required data structure and how to subsequently plot the results. The proposed methods provide visually appealing ways to report regression results efficiently and intuitively. Potential applications range from visual screening in the model selection stage to formal reporting in research papers. The procedure is fully reproducible using the provided code and can be executed via free-of-charge, open-source software routines in R.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Statistics on applications received and approved under “Top Talent Pass Scheme”

Latest Data: July 2004 - June 2022.Contains the land use application statistics.Historic Data:Jul 2004 - Jun 2021; Jul 2004 - Jun 2017; 2004 - 2015 .read more

Statistics on applications received and approved under “Admission Scheme for the Second Generation of Chinese Hong Kong Permanent Residents” provides relevant figures concerning applications received and approved under “Admission Scheme for the Second Generation of Chinese Hong Kong Permanent Residents”

As of January 2025, around 13.7 percent of paid iOS apps admitted collecting data from users engaging with their mobile products. In comparison, approximately 53 percent of free-to-download iOS apps reported they collect private data from users worldwide, while approximately 86 percent of paid apps have not declared whether they collect users' privacy data.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of such statistics over M-dependent random fields of arbitrary but fixed dimension. By allowing for a variety of combinations and contrasts of sample means over differently-shaped local windows, this yields a flexible class of scan statistics that can be tailored to the particular application of interest. The latter is demonstrated for crack detection in 2D-images of different types of concrete. Together with a simulation study this indicates the potential of the proposed methodology for the detection of anomalies in a variety of situations.

Note: HR-heart rate, BMI-body mass index, WC-waist circumference, SBP-systolic blood pressure, DBP-diastolic blood pressure, FPG- fasting plasma glucose, PBG- plasma blood glucose, IR-insulin resistance, TG- triglyceride, PH- Hypertension, DM- Diabetes.

This statistic shows the data security patent application activity of leading technology companies from 2013 to 2017. Microsoft is the largest company in terms of data security patent applications, filing ** data security patent applications in 2016 alone.

The Statistical Mirroring Application is a powerful tool designed for analyzing and visualizing data through statistical mirroring methods. It offers a user-friendly interface for conducting in-depth statistical mirroring analysis, including proximity and deviation estimates of transformed data points from a defined location or reference estimates within a given distribution. Inspired by Kabirian-based isomorphic optinalysis, the application provides features for preprocessing data, designing statistical mirrors, optimizations and calculating estimates based on Kabirian-based isomorphic optinalysis models. Users can make direct numerical data entry, or alternatively, can upload CSV or Excel file, select analysis parameters such as mirror principal value and centering options, and generate comprehensive statistical mirroring results. With its intuitive design and robust functionality, the Statistical Mirroring Application empowers researchers, students, and professionals to explore and understand data relationships effectively. Additionally, the application comes with a comprehensive user manual to assist users in navigating its features and functionalities seamlessly.

According to a survey of global consumers, the share of respondents reporting to feel extremely comfortable with mobile apps accessing their personal data has almost doubled since 2021. In comparison, the number of users reporting to feel "very comfortable" with personal data sharing on mobile apps has decreased from **** in 2021 to **** in 2022.

Data on petroleum production, imports, inputs, stocks, exports, and prices. Weekly, monthly, and annual data available. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

As of June 2025, Google Play was the app store with the highest number of available apps. More than *** million apps were available at that time. The Apple App Store was the second-largest app store with just under ****million available apps for iOS. The exact number of apps fluctuates as Apple and Google regularly remove content from their app stores that is not consistent with their guidelines and compliance.

Statistics on the nature of the cases filed by the people against the first-level government agencies

The graph shows the changes in the g-index of ^ and the corresponding percentile for the sake of comparison with the entire literature. g-index is a scientometric index similar to g-index but put a more weight on the sum of citations. The g-index of a journal is g if the journal has published at least g papers with total citations of g2.

According to a survey conducted in 2022, ** percent of respondents from healthcare organizations at a mature stage of AI adoption stated that natural language text was used in their AI applications. Structured data was the most common data type on which AI models were applied by healthcare organizations in early-stage AI adoption.

Data analysis can be accurate and reliable only if the underlying assumptions of the used statistical method are validated. Any violations of these assumptions can change the outcomes and conclusions of the analysis. In this study, we developed Smart Data Analysis V2 (SDA-V2), an interactive and user-friendly web application, to assist users with limited statistical knowledge in data analysis, and it can be freely accessed at https://jularatchumnaul.shinyapps.io/SDA-V2/. SDA-V2 automatically explores and visualizes data, examines the underlying assumptions associated with the parametric test, and selects an appropriate statistical method for the given data. Furthermore, SDA-V2 can assess the quality of research instruments and determine the minimum sample size required for a meaningful study. However, while SDA-V2 is a valuable tool for simplifying statistical analysis, it does not replace the need for a fundamental understanding of statistical principles. Researchers are encouraged to combine their expertise with the software’s capabilities to achieve the most accurate and credible results.

Question Paper Solutions of chapter Approximation in numerical computation of Numerical and Statistical Analysis, 2nd Semester , Master of Computer Applications (2 Years)