Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
ASReml-SA is a robust statistical software specifically tailored for mixed models that utilize Residual Maximum Likelihood (REML) for parameter estimation. Linear mixed-effects models serve as a versatile and comprehensive method for analyzing numerous datasets frequently encountered in fields such as animal, plant, and aquatic breeding, as well as in agriculture, environmental sciences, and medical research. The newest version, ASReml-SA 4.2, boasts three times the memory capacity of its predecessor 4.1, allowing for significantly larger analytical tasks to be performed. With enhancements in parallel processing and the capability to allocate memory for specific operations, the software has also seen improvements in speed; a comparison table is provided below to illustrate the speed enhancements realized across various analyses. ASReml-SA 4.2 not only accelerates processing but also offers users the potential for optimized performance tailored to their specific hardware and analytical needs. Ultimately, these advancements reflect ASReml-SA's commitment to facilitating efficient and effective data analysis.
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
No
API Access
Has API
No
Integrations
ActiveScale
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$29.90/month/user
Free Trial
Yes
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
No
Vendor Details
Company Name
VSN International
Founded
2000
Country
United Kingdom
Website
www.vsni.co.uk
Vendor Details
Company Name
Quark Analytics
Founded
2019
Country
Portugal
Website
www.quarkanalytics.com
Product Features
Statistical Analysis
Analytics
No
Association Discovery
Yes
Compliance Tracking
Yes
File Management
No
File Storage
Yes
Forecasting
No
Multivariate Analysis
No
Regression Analysis
No
Statistical Process Control
No
Statistical Simulation
Yes
Survival Analysis
No
Time Series
No
Visualization
No
Product Features
Data Analysis
Data Discovery
No
Data Visualization
No
High Volume Processing
No
Predictive Analytics
No
Regression Analysis
No
Sentiment Analysis
No
Statistical Modeling
No
Text Analytics
No
Statistical Analysis
Analytics
No
Association Discovery
No
Compliance Tracking
No
File Management
No
File Storage
No
Forecasting
Yes
Multivariate Analysis
Yes
Regression Analysis
Yes
Statistical Process Control
Yes
Statistical Simulation
No
Survival Analysis
No
Time Series
No
Visualization
No