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Important update on 16. February 2019: Some links, and especially downloads links on this personal website may not be uptodate, as I recently transferred the whole site from www.finaquant.com to software.tuncalik.com.
Give me some time; I will check and correct them one by one.
I decided to offer all the software introduced on this website open-source and free.
Check my shared folder for most uptodate downloads. Open-Source downloads of the C#/.NET libraries Finaquant Calcs and Finaquant Protos are also included in this shared download folder as zipped Visual Studio 2012 files.
You may contact me if you need any help or consulting for my analytical software.
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Recent Posts
- Minimum Total Loss Rounding in Python
- Travel and Assignment Planning Software in Python
- Top-Down Distribution of Quality Measurement Requirements using Table Functions in Python
- A Simple Notation for Data Tables
- Calculating Sales Commissions in Excel
- How to Add a User-Defined Table Function to Excel
- How to Apply a User-Defined Function on Rows of a Table in Excel
- Empowering Excel with Table Valued Functions
- Testing Table Valued Functions with LINQPad
- Building a Web Service that Receives and Returns Data Tables
- Optimizing Marketing Investment
- Reporting with ReportViewer in Visual Studio
- Simulation of Dealer Commissions with Matlab
- Finaquant’s Matrix and Vector Functions
- Table Function Demo with Microsoft Excel
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Tag Archives: R
Predictive Modelling with Linear Regression – 2
In this article, I will show the estimation model which was introduced in the previous related article in action with matlab and Simulink; estimating the time record of an athlete running 100-meters against the wind, with a weight attached to … Continue reading
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Tagged estimation, linear filters, linear regression, matlab, predictive model, R, screencast, video
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Quick Start Reference for MATLAB and R
This quick start reference originates from the short matlab tutorial that I had compiled for my students at the University of Applied Sciences HSZ-T in 2004 for my lecture Data Warehouse and Data Mining. It was a quick introduction to … Continue reading