Statistical Analysis Techniques for Data Science: Reviewing statistical analysis techniques commonly used in data science, such as hypothesis testing, ANOVA, and regression analysis

Authors

  • Dr. Yuliya Shylenok Associate Professor of Applied Mathematics and Informatics, Belarusian State University of Informatics and Radioelectronics (BSUIR) Author

Keywords:

data science, ANOVA

Abstract

Statistical analysis is a fundamental aspect of data science, providing the tools to extract meaningful insights from data. This paper reviews key statistical analysis techniques commonly employed in data science, including hypothesis testing, analysis of variance (ANOVA), and regression analysis. The paper examines the principles behind these techniques, their applications in data science, and best practices for their implementation. The goal is to provide a comprehensive overview of statistical analysis techniques to aid data scientists in effectively analyzing and interpreting data.

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Published

03-08-2023

How to Cite

[1]
Dr. Yuliya Shylenok, “Statistical Analysis Techniques for Data Science: Reviewing statistical analysis techniques commonly used in data science, such as hypothesis testing, ANOVA, and regression analysis”, Australian Journal of Machine Learning Research & Applications, vol. 3, no. 2, pp. 181–190, Aug. 2023, Accessed: Nov. 22, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/91

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