Application of the Regression Analysis in Python, SPSS and Microsoft Excel Programs

Regression analysis has been one of the most used statistical methods during the past fifty years to analyze relationships between variables. Due to its flexibility, usefulness, applicability, theoretical and technical analysis, regression analysis has become an essential statistical tool for solving problems in the real world. In order to apply these scientific techniques successfully and effectively, one needs a vision and an understanding of both basic theory and its practical application in the various fields of engineering, physical sciences, mathematical sciences and statistics. So it blends theory and practice so it has a sufficiently deep understanding of the basic principles needed to apply regression building techniques. First, we will present a comprehensive study, but rather simple, study to basic regression knowledge by discussing how regression analysis works, the requirements and assumptions it relies on, and how we can define a regression analysis model that allows us to make critical business, customer, or project decisions. Each step of regression analysis is related to its implementation in several advanced and various computer programs such as Python, SPSS, and Microsoft Excel. We will show how to use a set of easy-to-learn statistical procedures for these programs that underlie regression analysis, which will allow us to analyze, map, validate regression results and assess the strength of the analysis. Interpreting the outputs may be somewhat difficult, but we will try to make this easier by studying an annotated case within the use, application, and interpretation of regression analysis.

Keywords: Machine Learning, Regression Analysis, Python, Spss, Microsoft Excel

Regression analysis has been one of the most used statistical methods during the past fifty years to analyze relationships between variables. Due to its flexibility, usefulness, applicability, theoretical and technical analysis, regression analysis has become an essential statistical tool for solving problems in the real world. In order to apply these scientific techniques successfully and effectively, one needs a vision and an understanding of both basic theory and its practical application in the various fields of engineering, physical sciences, mathematical sciences and statistics. So it blends theory and practice so it has a sufficiently deep understanding of the basic principles needed to apply regression building techniques. First, we will present a comprehensive study, but rather simple, study to basic regression knowledge by discussing how regression analysis works, the requirements and assumptions it relies on, and how we can define a regression analysis model that allows us to make critical business, customer, or project decisions. Each step of regression analysis is related to its implementation in several advanced and various computer programs such as Python, SPSS, and Microsoft Excel. We will show how to use a set of easy-to-learn statistical procedures for these programs that underlie regression analysis, which will allow us to analyze, map, validate regression results and assess the strength of the analysis. Interpreting the outputs may be somewhat difficult, but we will try to make this easier by studying an annotated case within the use, application, and interpretation of regression analysis.

Anahtar Kelimeler: Machine Learning, Regression Analysis, Python, Spss, Microsoft Excel

Page Range: 27-46

Al-Khaiat, S. S. J., Noori, M. Z. & Cengiz, M. A. (2022). Application of the Regression Analysis in Python, SPSS and Microsoft Excel Programs. Journal of Current Researches on Educational Studies, 12 (2), 27-46.