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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

WinSTAT serves as a powerful statistics Add-In for Microsoft Excel, providing comprehensive information about its features and benefits. Are you fed up with complex statistics software that requires expert-level knowledge? Discover why WinSTAT could be the ideal solution for your needs. Curious if it includes the functions and visualizations that you're looking for? Explore the function reference page, which showcases sample outputs for various tables and graphics associated with all functions. If you're still on the fence, the best way to determine its value is to experience WinSTAT firsthand. You can easily download the demo right here. We genuinely believe that WinSTAT will play a pivotal role in your statistical endeavors, so why wait? Take the step towards a brighter future in data analysis by ordering WinSTAT today. Among its offerings are regression analysis techniques such as linear, multiple, and polynomial regression, as well as correlation methods like Pearson, Spearman, and partial correlation. Additionally, it provides statistical tests including dependent and independent t-tests, along with various visualizations such as box-plots, scatterplots, histograms, and probit charts. Moreover, WinSTAT includes goodness-of-fit tests like Kolmogorov-Smirnov and Shapiro-Wilk, along with the Chi-square test, giving you a robust toolkit for your statistical needs.

Description

ndCurveMaster, a specialized curve fitting software, is designed to fit curves with multiple variables. It automatically applies nonlinear equations to your datasets. These can be observed or measured values. The software supports curve and surfaces fitting in 2D 3D 4D 5D ..., dimensions. ndCurveMaster is able to handle any data, no matter how complex or how many variables there are. ndCurveMaster, for example, can efficiently derive the optimal equations for a dataset that has six inputs (x1-x6) and a corresponding output Y. For example: Y = a0 - a1 - exp(x1)0.5 + a2 ln(x2)8... + a6 x65.2 to accurately match measured value. ndCurveMaster uses machine learning numerical methods to automatically fit the most suitable nonlinear regression function to your dataset, and discover the relationships between inputs and outputs. This tool supports various curve fitting methods, including linear, polynomial, and nonlinear methods. It also utilizes essential validation and goodness-of-fit tests to ensure accuracy. Additionally, ndCurveMaster provides advanced assessments, such as detecting overfitting and multicollinearity, using tools like the Variance Inflation Factor (VIF) and the Pearson correlation matrix.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Pascal
Python

Integrations

Pascal
Python

Pricing Details

$99.00
Free Trial
Free Version

Pricing Details

€289
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

R. Fitch Software

Website

www.winstat.com

Vendor Details

Company Name

SigmaLab Tomas Cepowski

Founded

2017

Country

Poland

Website

www.ndcurvemaster.com

Product Features

SPC

Corrective Actions (CAPA)
Data Entry
Data Linking
Data Management
Excel Loader
Job Management
OPC Data Collection
Performance Metrics
Point-of-Production Analysis
Real Time Data Collection
Regulatory Compliance

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

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