A Simple Introduction to Regression Modeling using R | ||||
| Computational Journal of Mathematical and Statistical Sciences | ||||
| Volume 2, Issue 1 - Serial Number 2, April 2023, Page 52-79 PDF (883.41 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/cjmss.2023.189834.1002 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Amr R Kamel   1, 2; Mohamed Reda Abonazel 2
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| 1Department of Basic Sciences, Elgazeera High Institute for Computers and Information Systems, Ministry of Higher Education, Cairo, Egypt. | ||||
| 2Department of applied statistics and Econometrics, Faculty of Graduate Studies for Statistical Research, Cairo Uniersity, Giza 12613, Egypt | ||||
| Abstract | ||||
| In statistical modeling, regression analysis is a group of statistical processes used in R programming and statistics to determine the relationship between dataset variables. It is a solid technique for determining the factors that affect an issue of interest. You can confidently establish which elements are most important, which ones can be ignored, and how these factors interact when you do a regression. It can be used to simulate the long-term link between variables and gauge how strongly the relationships between them are related. Regression analysis is typically used to ascertain the relationship between the dataset's dependent and independent variables. Generally, regression analysis is used to determine the relationship between the dependent and independent variables of the dataset. Understanding how dependent variables change when one of the independent variables changes while the other independent variables remain constant is made easier with the use of regression analysis. As a result, it is easier to create a regression model and forecast values in response to changes in one of the independent variables. Based on the categories of dependent variables, the quantity of independent variables, and the contour of the regression line. In this paper, we use the R programming language to present various empirical investigations in statistics and econometrics. We next consider problems involving modeling the relationship between response and explanatory variables for linear and non-liner regression models. | ||||
| Keywords | ||||
| Regression Analysis; Practical Introduction; Estimation Methods; Multiple Regression; R software | ||||
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