Overview of Regression Analysis
The relationship between one variable and several other variables are often of interest to researchers. One variable is the smoking ratio, which compares the number of cigarettes smoked per day by males in each occupation to the total number of cigarettes smoked by all men of the same age. The standardized mortality ratio is another variable to consider. We may be interested in knowing the relationship between the derived mortality ratio and the smoking ratio in order to answer the question of whether smoking causes cancer. The scope of regression analysis encompasses this.
The results of a scientific experiment frequently lead to the question of whether two or more variables have a causal relationship. The statistical tool for investigating such a relationship is regression analysis. It is likely one of the oldest issues in the field of mathematical statistics, having been studied for almost two centuries.
The least squares approach, which was presented by Legendre in 1805 and Gauss in 1809, was the first form of linear regression. Legendre coined the phrase "least squares." Legendre and Gauss both used the method to determine the orbits of planets around the sun based on astronomical observations. Euler had attempted and failed to solve the same problem in 1748. In 1821, Gauss published a continuation of the theory of least squares, which included a version of the now-famous Gauss-Markov theorem, which is a basic theorem in the field of general linear models.
Linear regression necessitates that the model's regression parameters be linear. Regression analysis is a technique for determining the relationship between one or more response variables (also known as dependent variables, explained variables, predicted variables, or regressands, and usually denoted by y) and predictors (also known as independent variables, explanatory variables, control variables, or regressors, and usually denoted by x1, x2, xp).
Purpose of Regression Analysis
One of the most widely used statistical procedures in practice is regression analysis. Many scientific domains use regression analysis, including medicine, biology, agriculture, economics, engineering, sociology, and geology, to name a few. The three goals of regression analysis are as follows:
Types of Regression
On this topic, your comments/suggestions are highly appreciated. Type it in the comment section below. You can follow to this blog to receive notifications of new posts.
Is this article useful to you?
0 Comments