Statistic Packages Overview 2025
https://github.com/maxkleiner/maXbox5/tree/main/EKON29
- Regression & Correlation box script demos
- 7 Statistic Methods & 5 Diagramtypes
- Stats Package TeeChart & mrMath, Delphi AI
- Sklearn & Statsmodels (P4D)
- AGSI, JSON, EdgeView2 and Weather Reports
- Alternate Packages, Top & Flops Statistic
- This session shows various ways of using statistic and plots in your apps.
📊
ML Magazine – Visualization of Machine Learning Data
https://devm.io/reader/reading/ml-magazine/Volume%204/8533ef95484202124e17eaf9?fromSearch=true
A Base Class

System.Math.MomentSkewKurtosis is declared as
procedure MomentSkewKurtosis(const Data: array of Double; var M1,M2,M3,M4,Skew,Kurtosis,Extended);
Ex.: MomentSkewKurtosis ([1,2,3,4,5,5,5,6,7,8,9] ,M1,M2,M3,M4,Skew,Kurtosis)
writ(format('%2f %2f %2f %2f %2f %2f',[M1, M2, M3, M4, Skew, Kurtosis]))
5.00 5.45 0.00 64.36 0.00 2.16
MomentSkewKurtosis: Calculates the core factors of statistical analysis: the first four moments plus the coefficients of skewness and kurtosis.
- M1 is the Mean.
- M2 is the Variance.
- Skew reflects symmetry of distribution: M3 / (M2**(3/2))
- Kurtosis reflects flatness of distribution: M4 / Sqr(M2)
- Look at
M3andM4(the 3rd and 4th order moment) as helper variables in order to calculate skew and kurtosis (extracted from source):
https://github.com/fundamentalslib/fundamentals5/blob/master/Source/Maths/flcStatistics.pas
https://github.com/project-jedi/jcl/blob/master/jcl/source/common/JclStatistics.pas
https://github.com/TurboPack/SysTools/tree/master/source
https://github.com/TurboPack/SysTools/blob/master/source/StStat.pas
https://github.com/fundamentalslib/fundamentals5/blob/master/Source/Maths/flcStatistics.pas
Fundamentals 5 Library
Fundamentals 5 Code Library for Delphi and FreePascal
Includes:
- String, DateTime and dynamic array routines
- Unicode routines
- Hash (e.g. SHA256, SHA512, SHA1, SHA256, MD5)
- Integer (e.g. Word128, Word256, Int128, Int256)
- Huge Word, Huge Integer
- Decimal (Decimal32, Decimal64, Decimal128, HugeDecimal and signed decimals)
- Random number generators
- Cryptographic (Symmetric: AES, DES, RC2, RC4; Asymmetric: RSA, Diffie-Hellman, Elliptic Curve)
- Data structures (array, dictionary and map classes)
- Mathematics (Rational number, complex number, vector, matrix, statistics)

A Brain Class

https://github.com/maxkleiner/maXbox5/blob/main/EKON29/1442_Statistics_unittest.pas
7 Statistical Methods Criteria
- 1. Bayesian Inference (p(A))
- 2. Hypothesis Testing (t-test)
- 3. Factor Analysis (Loading Analysis)
- 4. Cluster Analysis (K-means)
- 5. Logistic Regression Classifier
- 6. Monte Carlo Simulation (Rnd)
- 7. Time Series Analysis (ARIMA)
- Data science is a multidisciplinary field that combines math, statistics, computer science, machine learning, and domain expertise to extract insights from data. While data science algorithms often put the spotlight, a solid foundation in statistical methods can be just as pivotal.
5 Important Diagram Types
Here, I’ll show you how to analyze a runtime script created dataset and extract meaningful insights with 5 diagram types:
- Bar Chart
- Scatter Plot
- Histogram
- Box Plot
- Correlation Matrix

Stats Dependencies

Cross-platform class TChart
- TeeChart is a powerful charting component developed by Steema Software, widely used in Delphi and C++Builder environments. It provides a comprehensive set of tools for creating various types of charts, including line, bar, area, pie, point, and horizontal bar charts, which can be viewed in both 2D and 3D formats.
- Key Features
- Versatility: TeeChart supports a wide range of chart types and can be used for business, real-time, financial, and scientific applications. It is included in most Delphi and C++Builder products and supports both VCL and FM.
- Cross-Platform Support: TeeChart can be used to create applications for Windows (x32/x64) and macOS. For Apple iOS support, TeeChart Pro.
- Interactivity: TeeChart allows users to interact with charts by zooming, panning, and exporting to various file types. This makes it suitable for applications that require real-time data visualization and user interaction.
Demo: 820_U_ProbabilityDist2ekon29_1_64.pas
GUI or Console?

Mr Math Package
https://github.com/mikerabat/mrmath/blob/master/Statistics.pas
Demo: TestdemoStatisticClassifier64bit.exe
Important: On top of these classifiers there exists a few image database handling routines
and an 1D, 2D Haar Feature extractor which is based on an integral image approach.
The mrMath, mrStats, mrMatrix, mrImgUtils package includes:
- Standard Fisher LDA classifier
- Robust (and Fast Robust) version of this classifier
- Incremental (and Robust) Fisher LDA classifier learning.
- Support Vector Machines (least squares and lagrangian learning)
- Naive Bayes
- Simple Decission stumps
- Radial basis function
- C4.5 Decission trees.
- K-means
- Ensemble classifiers: AdaBoost, Gentle Boost, Bagging
- Simple feed forward Neural Nets
Mr Math II Test

- A testing app TestClassifier.dpr which shows the usage and performance of these classifiers on various tasks (e.g. face recognition) which we slightly improved, compiled and signed can be found on git or sourceforge:
- https://github.com/maxkleiner/mrai_mx5/tree/master/TestApp
- https://sourceforge.net/projects/maxbox5/files/binaries/TestClassifier64bit.exe/download
Stat Control

Code Control II
Write a stateful function, class, generator or co-routine that takes a series of floating point numbers, one at a time, and returns the running standard deviation of the series.
Use this to compute the standard deviation of this demonstration set, {2,4,4,4,5,5,7,9} which is 2. {\displaystyle \{2,4,4,4,5,5,7,9\}}

Gas storage timeline AGSI dataset
https://wiki.freepascal.org/pas2js_Electron_Web_Application
The data science tutorial explains the so called AGSI data storage and his visualization of the timeline. AGSI is the Aggregated Gas Storage Inventory and offers you the possibility to be kept up to date whenever a new service announcement or update from one of our data providers is posted on the website.

Statsmodels
Statsmodels is a Python library designed for statistical modeling, hypothesis testing, and data exploration with high reference.
It provides a wide range of statistical models, including linear regression, time series analysis, and generalized linear models.
The library supports both formula-based modeling (similar to R) and direct use of NumPy arrays.

Demo: 1417_statsmodels_64_delphi_python3.12.4debug130_EKON29.txt
Second multivariate with a higher R2:
Execstr(‘model=smf.ols(“Lottery~ Wealth+Literacy+np.log(Pop1831)+Suicides+Donations”,data=df).fit()’);


OLS Regression Results
==============================================================================
Dep. Variable: Lottery R-squared: 0.492
Model: OLS Adj. R-squared: 0.461
Method: Least Squares F-statistic: 15.51
Date: Thu, 02 Oct 2025 Prob (F-statistic): 1.20e-10
Time: 17:54:49 Log-Likelihood: -369.10
No. Observations: 86 AIC: 750.2
Df Residuals: 80 BIC: 764.9
Df Model: 5
Covariance Type: nonrobust
===============================================================================
coef std err t P>|t| [0.025 0.975]
-------------------------------------------------------------------------------
Intercept 180.2691 35.830 5.031 0.000 108.965 251.573
Wealth 0.1715 0.094 1.833 0.070 -0.015 0.358
Literacy -0.2409 0.126 -1.911 0.060 -0.492 0.010
np.log(Pop1831) -25.1121 5.699 -4.407 0.000 -36.453 -13.771
Suicides 0.0002 7.37e-05 3.323 0.001 9.82e-05 0.000
Donations 0.0005 0.000 1.490 0.140 -0.000 0.001
==============================================================================
Omnibus: 6.163 Durbin-Watson: 1.665
Prob(Omnibus): 0.046 Jarque-Bera (JB): 5.537
Skew: -0.594 Prob(JB): 0.0628
Kurtosis: 3.363 Cond. No. 8.85e+05
==============================================================================
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 8.85e+05. This might indicate that there are
strong multicollinearity or other numerical problems.
('version:', '3.13.3 (tags/v3.13.3:6280bb5, Apr 8 2025, 14:47:33) [MSC v.1943 64 bit (AMD64)]')
dept Region Department Crime_pers Crime_prop Literacy Donations Infants Suicides MainCity Wealth Commerce Clergy Crime_parents Infanticide Donation_clergy Lottery Desertion Instruction Prostitutes Distance Area Pop1831
Statsmodels Code
- We download the Guerry dataset, a collection of historical data used in support of Andre-Michel Guerry’s 1833 Essay on the Moral Statistics of France. The data set is hosted
- online in comma-separated values format (CSV) by the Rdatasets repository. We could download the file locally and then load it using read_csv, but pandas takes care of all of this automatically for us:
Execstr(‘model = smf.ols(“Lottery ~ Wealth + Literacy + np.log(Pop1831)”, data=df).fit()’);

Code Behind Dataframe

P4D Primer

EdgeView2 SDK for Stats
- McJson use a Simple Object-Pascal native code using TList as internal data structure with a Single-pass string parser and is just one unit (McJSON) and just one class(TMcJsonItem) integrated in maXbox or Lazarus.
- RAD Studio 10.4 Sydney brings support for working with web content through the Chromium-based Edge WebView2 browser control in VCL applications via the new TEdgeBrowser component.
- Demo: 1324_weatherbox_regex_64_maincities.txt

https://sourceforge.net/projects/maxbox5/files/binaries/WebView2Loader_2739.dll/download
JS-Pas-Solution
- These projects have been selected based on their popularity, technical significance, and impact on the Delphi development community. They represent a mix of developer tools, frameworks, and components.
- Integrate dynamic, interactive, custom maps, statistic data and geospatial data into your apps by embedding JS:
- Weather, Navigation or Trafic Data uses huge statistical data with representation

1312_API_Demo64_5_javascript_maps.txt
Other Packages
Several statistical packages and libraries are available for Delphi, ranging from commercial solutions to open-source and freeware options.

These packages support a wide range of statistical computations, from basic descriptive statistics to advanced multivariate analysis.
Tops & Flops Stats (from API to AI)
- **1. Awesome Delphi***
- **2. Delphi IDE Theme Editor**
- **3. DelphiMVCFramework (DMVCFramework)**
- **4. Delphi REST Client API***
- **5. Virtual-TreeView**
- **6. Delphi-OpenCV***
- **7. DUnitX**
- **8. Delphi IDE Colorizer**
- **9. CEF4Delphi***
- **10. OmniThreadLibrary**
https://github.com/maxkleiner/maXbox/blob/master/objectdetector3.ipynb
These projects have been selected based on their popularity, technical significance, and impact on the Delphi development community. They represent a mix of developer tools, frameworks, and components (perplexity,chatgpt,gemini prompts).
Tops & Flops Explain
https://github.com/maxkleiner/maXbox/blob/master/objectdetector3.ipynb
**1. Virtual-TreeView**
A highly flexible and powerful tree view control, Virtual-TreeView has become a staple in both open-source and commercial Delphi applications.
Its performance and extensibility have set the standard for advanced UI components in Delphi, making it one of the most influential libraries
in the ecosystem[1].
**2. OmniThreadLibrary**
This threading library greatly simplified the development of multi-threaded applications in Delphi. Its ease of use and robust feature
set have made it the go-to solution for Delphi developers needing parallelism and concurrency, ensuring Delphi apps remain competitive
on modern multi-core hardware[1].
**3. DelphiMVCFramework (DMVCFramework)**
As web development became critical, DMVCFramework provided Delphi developers with a modern, powerful framework for building
RESTful web applications and APIs. Its adoption has helped keep Delphi relevant in server-side and web API development[1].
**4. CEF4Delphi**
By enabling the embedding of Chromium-based browsers in Delphi applications, CEF4Delphi has allowed developers to
integrate modern web technologies and content directly into native apps, bridging the gap between desktop and web[1].
**5. DUnitX**
Unit testing is essential for modern software development, and DUnitX brought advanced, NUnit-inspired testing
capabilities to Delphi. Its adoption has fostered better software quality and test-driven development practices in the Delphi community[1].
**6. Delphi-OpenCV**
This project brought the power of the OpenCV computer vision library to Delphi developers, opening up new
possibilities in image processing and AI within the Delphi ecosystem[1].
**7. Delphi REST Client API**
As RESTful APIs became the backbone of modern applications, this library enabled Delphi developers to
easily consume web services, ensuring Delphi’s continued viability for connected applications[1].
**8. Awesome Delphi**
While not a library or component, this curated list of high-quality Delphi resources has become an essential
starting point for developers, helping to foster community and knowledge sharing[1].
**9. Delphi IDE Theme Editor & Delphi IDE Colorizer**
These tools have modernized the developer experience, allowing for extensive customization
and theming of the Delphi IDE, which has contributed to the productivity and satisfaction of Delphi programmers[1].
Flops
- **1. Delphi 8 and Delphi 2005 Releases**
- **2. Dreaded Sorts (THSorts) Demo**
- **3. Component Colossus Projects**
- **4. Projects Stuck on Old Delphi Versions**
- **5. Projects Dependent on Discontinued Technologies**
- **6. DLL Hell Projects**
- **7. Over-Componentized “Flavor of the Month” Projects**
- **8. Projects Broken by Unicode Transition**
- **9. Projects with Inflexible Architectures**
- **10. “Reinventing the Wheel” Projects**
Conclusion
- https://softwareschule.code.blog/2025/02/20/7-data-science-statistical-methods/
- Config and spec features of DMath, SKLearn, Statsmodels, mrMath library
- Troubleshooting: Typical miscalculations and their solutions with cleaned data
- Typical Features of these Packages
- Descriptive stats: mean, median, mode, variance, percentiles
- Probability distributions (PDF, CDF, inverse CDF)
- Random number generation
- Hypothesis testing (t-tests, chi-squared, ANOVA)
- Regression (linear, logistic, ridge, Poisson, non-linear)
- Multivariate techniques (PCA, factor analysis)
- Data visualization, function approximation
- Check the statistic package like TEE in feature-matrix
- https://www.embarcadero.com/docs/rad-studio-feature-matrix.pdf
https://raw.githack.com/breitsch2/maXbox4/master/assets/graph3.html
Method: Correlation
Plot: List or Matrix
View: Form or Browser
Many Thanks for coming!
Many Thanks for coming!
Materials:
http://www.softwareschule.ch/download/maxbox_starter140.pdf
http://www.softwareschule.ch/download/maxbox_starter149.pdf
https://blogs.embarcadero.com/why-a-data-scientist-chooses-delphi-for-powerful-real-world-visualizations/
Dienstag, 28. Oktober 2025
11:45 – 12:45, Max Kleiner
https://sourceforge.net/projects/maxbox5/files/EKON29/
Origin Slides:
Click to access maxbox_starter157.pdf
https://de.scribd.com/document/925708175/Maxbox-Starter157-EKON29-Statistic-Packages-2sign







Jouef est une marque d’origine française de modèles réduits de trains et de circuits routiers, créée en 1944 par Georges Huard à Champagnole, dans le département du Jura.







Delphi Quiz 30 Jahre Delphi EKON 29
A must see Operation System history
https://lnkd.in/e_KjeJdd
—————————-
1. Ab welcher Delphi Version is der String ein Unicodestring?
Antwort: Ab Delphi 2009 ist string ein UnicodeString und speichert Zeichen in UTF-16-Kodierung.
2. Welche Koierung wird in Unicode gebraucht?
UTF-16 BE oder UTF-16 LE
Antwort: UTF-16 LE (Little Endian)
3. Wie hiess der Codename von Delphi 2006?
A: DeXter
4. Wie hiess der Codename von Delphi XE6?
A: Tiburón
5. Wann ist der Vater von Pascal gestorben?
A: Januar 2024
6. Was ist der grösste Datentyp in Delphi?
A: Größter dynamischer Typ: string (UnicodeString, bis zu 2 GB möglich).
7. Von wem stammt das Buch Delphi 7 Kochbuch?
A: Von Walter Doberenz, Thomas Kowalski:
8. Seit wann gibt es Closures in Delphi?
A: Closures wurden in Delphi mit der Einführung von anonymen Methoden ab Delphi 2009
(Version 12, auch bekannt als Delphi Prism) unterstützt.
9. Was bedeutet ARC in Delphi?
A: automatische Referenzzählung (ARC) as automatic reference counter.
10. Wo in Delphi wird Nil definiert?
A: In Delphi wird nil als Konstante in der System-Unit definiert und hat den Wert von Pointer(0).


Finds a non-euclidean distance or similarity between two strings.
Jaro and Jaro-Winkler equations provides a score between two short strings where errors are more prone at the end of the string. Jaro’s equation measure is the weighted sum of the percentage of matching and transposed characters from each string. Winkler’s factor adds weight in Jaro’s formula to increase the calculated measure when there is a sequence of characters (a prefix) in both strings.
writeln(formatfloat('0.######', ssJaroWinkler('DWAYNE', 'DUANE')));
writeln(formatfloat('0.######', ssJaroWinkler('MARTHA', 'MARHTA')));
writeln(formatfloat('0.######', ssJaroWinkler('DIXON', 'DICKSONX')));
writeln(formatfloat('0.######', ssJaroWinkler('JELLYFISH', 'SMELLYFISH')));
The complexity of this algoritme resides in finding the matching and transposed characters. That is because of the interpretation of what are the matching conditions and the definition of transposed. Definitions of those two will make the score vary between implementations of this algorithme.




Install Py3.15 instead of Py3.14 (P4D Support Helpdesk)
The Standard install scanner in PythonEngine.pas checks KOWN_VERSIONS till 3.14 so how do you test Python4Delphi with a new 3.15?
(DllName: 'python312.dll'; RegVersion: '3.12'; APIVersion: 1013),
(DllName: 'python313.dll'; RegVersion: '3.13'; APIVersion: 1013),
(DllName: 'python314.dll'; RegVersion: '3.14'; APIVersion: 1013)
);
(DllName: 'libpython3.12.so'; RegVersion: '3.12'; APIVersion: 1013),
(DllName: 'libpython3.13.so'; RegVersion: '3.13'; APIVersion: 1013),
(DllName: 'libpython3.14.so'; RegVersion: '3.14'; APIVersion: 1013)
https://github.com/pyscripter/python4delphi/blob/master/Source/PythonEngine.pas
First you install the normal way your Py Windows Installer:


After the you rename the Default PATH from “C:\Users\User\AppData\Local\Programs\Python\Python315” to “C:\Users\User\AppData\Local\Programs\Python\Python314” and rename also the python315.dll to python314.dll. Note: your old active C:\Users\User\AppData\Local\Programs\Python\Python314 has to be set before to C:\Users\User\AppData\Local\Programs\Python\Python314_ as a save backup.

Then you should see with ExecString(‘import sys,io,time’); and sys.version:
pyout: (3, '3.15.0a1 (tags/v3.15.0a1:aeff92d, Oct 14 2025, 11:51:55) [MSC v.1944 64 bit (AMD64)]')
pyout: (4, '3.15.0a1 (tags/v3.15.0a1:aeff92d, Oct 14 2025, 11:51:55) [MSC v.1944 64 bit (AMD64)]')
PythonOK True cl.count:2
Scripted Runtime Version of P4D:
https://github.com/maxkleiner/python4delphi/blob/master/Source/uPSI_PythonEngine.pas
Caution: After rename the path you could not use pip to install more packages:










Weather Stat Forecast Package
In this example we examine Solingen/DE




Train Station main station








Notes concerning Stat Pacs Overview
TeeChart
TeeChart in Delphi offers a wide range of statistical functions that can be used for data presentation and analysis.
The Pro version of TeeChart includes approximately 56 mathematical, statistical, and financial-oriented functions, while the standard version also supports numerous basic statistical functions.
- Overview of Statistical Functions in TeeChart
Descriptive Statistics: Mean, Median, Minimum, Maximum, Standard Deviation, Variance. - Regression Functions: Linear, Exponential, and Polynomial Regression.
- Distribution Functions and Normal Distribution (Gaussian Distribution).
- Moving Average.
- Historograms and Boxplots for graphically displaying distributions and outliers.
- Correlation Analysis and Trend Lines.
Special statistical charts such as: Normal Probability Plot, Weibull Probability Plot, Quantile-Quantile Plot, quality control charts (e.g., XBar, R, S, P, NP, U, C), EWMA, and Levey-Jennings Chart.
Many of these statistical functions are available as so-called “functions” (e.g., TMeanFunction, TrendFunction, TMovingAverageFunction) and can be applied directly to chart series.
Advanced Analyses Process Capability Statistics (Cp, Cpk, p), often for quality control. Dot plots and biplots for multivariate data analysis. With these functions and visualizations, you can perform statistical analyses in Delphi with TeeChart and display the results graphically.
JCL Statistics
The statistical functions in the Delphi component library JCLStatistics (part of the JEDI Code Library) often referred to as JCL) cover the most important standard functions of descriptive and inferential statistics for basic data analysis and evaluation.
The JCLStatistics unit offers, in particular:
- Overview of Typical Functions in JCLStatistics
Calculation of mean, median, minimum, and maximum. - Sum, product, variance, and standard deviation.
- Skewness and kurtosis.
- Special functions such as mode (most frequent value).
- Quantiles and percentiles.
- Range and range.
- Various summary evaluations.
The functions are typically available as individual procedures and functions such as mean, median, variance, etc. (often in the JclStatistics.pas unit). They are designed more for classic descriptive analyses,
but advanced statistical models such as regression or complex trend analyses are not included in JCLStatistics. Other libraries such as TeeChart or third-party components are better suited for such tasks.
Systools
Available as Getit package
The SysTools library for Delphi provides a collection of utility routines and classes, which also includes useful mathematical and some basic statistical functions. However, its focus is primarily on practical helpers, not on advanced statistical models.
- Statistical Functions in SysTools
Basic arithmetic operations: sum, mean, minimum, maximum, etc. - Sorting functions and high-precision mathematics.
- 1-D & 2-D barcode creation and money routines.
- Runtime math expression analyzer.
- Other math helpers such as standard deviation, random number generator, rounding functions, and logarithmic operations.
- No complex statistical models such as regression, correlation analysis, or distribution functions are included by default.
SysTools is suitable for numerical analyses and mathematical utility functions. For advanced statistical methods, Delphi libraries such as TeeChart, DMath, or scientific statistical packages (e.g., SDL Component Suite) are a more sensible choice.
Fundamentals 5
The statistical functions in Fundamentals 5, especially in the flcStatistics unit for Delphi,
cover the most important numerical and statistical analyses. Fundamentals 5 is a modern, open-source code base that provides many mathematical and data-related methods, including statistics.
- Key Statistical Functions in flcStatistics (Fundamentals 5)
Mean, median, minimum, maximum. - Variance, standard deviation.
- Sum, product, and various aggregate functions.
- Quantiles, percentiles, and range calculations.
- Skewness, kurtosis.
- Mode (most frequent value) and sorting of data series.
- Basic random number generators.
- For vectors and matrices: sum, product, norms, statistical measures of dispersion.
These functions are applicable to numeric arrays and generic data types and are based on classic statistics APIs. Fundamentals5 differs in its modern, universal, and packaged architecture, offering many mathematical and statistical methods from a single library.
TMS Physics Pack
Yes, TMS Software offers a “TMS Analytics & Physics Pack,” which is offered as a statistics and analysis package for Delphi.
Conclusion
The best statistics package for Delphi depends largely on the specific application, the required range of functions, and whether you value graphical visualization or robust numerical analysis. However, the following packages have proven particularly effective in the professional and enterprise sectors:
Recommendations for leading statistics packages in Delphi
TeeChart: Comprehensive, commercial toolkit for data visualization with 50+ statistical functions, supports many complex analyses, and offers very powerful graphical output. Also regressions, trends, histograms, box plots, and much more.
TMS Analytics & Physics Pack: Specifically for numerical and analytical statistics, including symbolic calculations, approximation, data fitting, numerical integration, and matrix calculations. Ideal when analytical methods and data science aspects are paramount.
JCLStatistics (JEDI): Open source, suitable for basic analysis, fast and easy to use, but less complex procedures. Suitable for standard statistics in code, but not for graphical analysis.
Fundamentals5 (flcstatistics): A very modern, object-oriented open-source solution with a wide range of numerical methods, particularly suitable for developers who value generic and portable statistical functions.
SDL Component Suite: Commercial, very broad mathematical and statistical spectrum, also recommended for specialized areas such as signal processing, medicine, or chemistry.
https://www.lohninger.eu/sdlindex.html
- Key Selection Criteria
For comprehensive graphical and numerical statistics: TeeChart or TMS Analytics & Physics Pack. - For open, easy integration of pure statistical data: JCL Statistics or Fundamentals5.
- For specialized scientific and industrial applications: SDL Component Suite.
There is no “absolutely best” statistical package – the choice depends on project requirements, budget, and the need for visual or analytical depth. In many enterprise applications, TeeChart is the leader, while in scientific applications, SDL Suite is often the choice, and in open-source projects, JCL or Fundamentals5 are often the choice.
http://datalab.epina.at/en_home.html
Epina DataLab is a simple to use statistics package running under Windows offering a well-balanced mixture of data editing capabilities, visualization methods and mathematical and statistical procedures. DataLab has been developed as a small but powerful tool for data manipulation and interpretation. The methods implemented in this package have been selected from a practitioners view.





Train Rain








This is the first iteration of the altair visualization of the scatterplot we made. Already the primary difference from the sketches is that the x and y axis have changed, with the y now showing the liquor licenses per 10k people and the x showing the percentage of binge drinkers in that city’s county, with each dot representing a city.





Master Python Course
https://dev.to/onlineproxy/a-pragmatists-guide-to-mastering-pythons-foundations-4p8j
This guide to AI data visualization breaks down the essentials of understanding and improving complex datasets with examples, tools, and proven strategies to support better model development and decision-making.
When it comes to AI, large and complex datasets are a necessary evil. To build accurate and reliable AI models, it is important to truly understand the data being used.

























































































































































































































































































































































































