What Is Regression Analysis?
How Does Regression Analysis Work?
The general function of regression analysis is the regression function, and it begins with covariance. The covariance shows the direction of the relationship, either positive or negative. If the relationship is positive, one variable would increase as another variable does. If the relationship is negative, as one variable increases, the other decreases. Next, the correlation coefficient is calculated by dividing the covariance by the product of the standard deviation of the two variables. This limits the correlation to a value range of -1 to 1. Lastly, a regression equation (y = bx + a) can be used to forecast at any variable, where "y" is the dependent variable, "b" is the slope of the line, "x" is the independent variable, and "a" represents the slope of the line. This form of linear regression is also known as ordinary least squares.
Regression Analysis Visualized
The graph below visualizes a linear regression function across a specified data set. The red line represents the linear regression function and expresses the rate of change among the data points.
A Linear Regression
Regression Analysis and Machine Learning
Because regression analysis is frequently used for forecasting and making predictions, it is widely integrated within the realm of machine learning, specifically supervised learning. A regression analysis can be used to understand how independent variables are related to the dependent variable, and examine the relationship between the two. In some circumstances, a regression analysis can be used to infer a causal relationship between two variables, however it is often advised to use regression analysis in conjunction with other tests to avoid false relationships.
