Regression

Regression analysis in R and Python.

Regression is a method of studying the dependence of a variable y on another variable x. The analysis method is based on the fact that the variable under study can be described using a function that depends on the values ​​of another controlled variable. In other words, knowing the values ​​of X, you can quite accurately predict the corresponding value of y. There are two methods of regression analysis: pairwise linear regression and multiple correlation.

In this article we will talk about how to perform regression in the popular programming languages ​​`R` and `Python`. Also

Regression testing is the process of searching for errors (bugs) inside a function using a mathematical model that will predict the functioning of the program regardless of the selected input parameters. This type of testing is highly accurate. Mathematical prediction theory is used to find flaws in the code.

Regression analysis is a branch of mathematics. It is based on the study of linear dependencies between variables - a change in one affects the other. Linear dependence is expressed by an equation of the form y = ax + b. Coefficients a and b are determined by the least squares method and express the error performance of the tested model. Using the least squares method allows you to find not only the regression line itself, but also all the “deviation-prone” points located under it. This allows you to create a list of dangerous data points where new tests need to be implemented first.

The essence of the regression algorithm is to continuously increase the number of reproducible tests using optimization techniques and test parameters. The purpose of the algorithm is to continuously evaluate the number of samples produced against the generated test plan. All this can be expressed by the formula:

n(i+1) = n(i) + ln(Error/n)/db(l)

The J function describes the number