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Description
Measurements are always subject to deviations from their true values, a phenomenon known as measurement uncertainty, especially when assessing or calibrating measurement tools or methodologies. To ensure quality control, it is essential to accurately quantify this uncertainty. GUMsim®, which adheres to the latest Guide to the Expression of Uncertainty in Measurement (GUM) and its supplement 1, utilizes sophisticated computational algorithms that facilitate a more effective determination of measurement uncertainty in line with ISO/IEC 17025 standards. The process of determining measurement uncertainty involves a mathematical relationship and statistical analysis of all variables influencing the measurement outcomes. To streamline this process, GUMsim provides a user-friendly input environment designed for various measurement models. Additionally, it offers a range of pre-defined application models that function as templates tailored to assist you in conducting specific evaluations, making it easier to embark on your measurement uncertainty assessments. This feature not only enhances the user experience but also encourages consistency in measurement practices across different applications.
Description
In a secure and manageable setting, users can swiftly derive insights from their data. Data can be collected in various formats and types, enabling the creation of new variables and the selection of specific cases of interest. Through effective data analysis techniques, both numerical and categorical variables can be thoroughly examined and analyzed. Results can be presented either in tabular form or through graphical representations. Additionally, users can investigate the relationships between different variables and assess the significance of these relationships. Various statistical tests, such as Pearson and Spearman correlations, Chi-Square tests, T-Tests for independent samples, Mann-Whitney, ANOVA, and Kruskal-Wallis, can be employed to achieve this. Moreover, the most commonly used measures of scale reliability can be easily selected and calculated. One can also verify the consistency of dimensions in the dataset. Utilizing measures like Cronbach's Alpha—both raw and standardized, with or without item deletion—Guttman’s six, and Intraclass correlation coefficients (ICC), provides further insights into the reliability of the data. This comprehensive approach ensures a thorough understanding of the data's structure and relationships.
API Access
Has API
API Access
Has API
Integrations
No details available.
Integrations
No details available.
Pricing Details
€870 one-time payment
Free Trial
Free Version
Pricing Details
$29.90/month/user
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
QuoData
Country
Germany
Website
www.quodata.de/gumsim
Vendor Details
Company Name
Quark Analytics
Founded
2019
Country
Portugal
Website
www.quarkanalytics.com
Product Features
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization
Product Features
Data Analysis
Data Discovery
Data Visualization
High Volume Processing
Predictive Analytics
Regression Analysis
Sentiment Analysis
Statistical Modeling
Text Analytics
Statistical Analysis
Analytics
Association Discovery
Compliance Tracking
File Management
File Storage
Forecasting
Multivariate Analysis
Regression Analysis
Statistical Process Control
Statistical Simulation
Survival Analysis
Time Series
Visualization