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Datalyse: Integrating Statistics and Computation for Streamlined Financial Data Analysis, Linear Model Building, and Time Series Analysis
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Author(s): Tejal Alwyn Menezes (Christ University, India), Tina Dokaniya (Christ University, India), Elainne William Desouza (Christ University, India), Ishaan Rai (Christ University, India)and A. Vijayalakshmi (Christ University, India)
Copyright: 2025
Pages: 24
Source title:
Data Analytics and AI for Quantitative Risk Assessment and Financial Computation
Source Author(s)/Editor(s): Mohammad Gouse Galety (Samarkand International University of Technology, Uzbekistan), Jimbo Henri Claver (Samarkand Interntional University of Technology, Uzbekistan), A. V. Sriharsha (Mohan Babu University, India), Narasimha Rao Vajjhala (University of New York Tirana, Tirana, Albania)and Arul Kumar Natarajan (Samarkand International University of Technology, Uzbekistan)
DOI: 10.4018/979-8-3693-6215-0.ch006
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Abstract
Having robust financial markets is important for economic development. Using statistical analysis and data visualization on financial data helps identify trends and understand the underlying factors affecting various aspects of the economy. This allows policymakers to make better decisions. In this chapter, we aim to introduce 'Datalyse,' an R programming developed website capable of performing various statistical tests and analyses. 'Datalyse' intends to solve this complexity by integrating statistics and web development. It accepts user-input data, computes the summary statistics, validates assumptions, and performs t-tests, F-tests, and z-tests. It checks the basic assumptions and generates a regression model, both simple and multiple linear regression. Additionally, residual analysis is performed to check model adequacy. The chapter demonstrates the application of 'Datalyse' in analyzing data and deriving insights through statistical methods, mainly regression analysis.
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