Regression Analysis

Linear, multiple linear, and nonlinear regression are statistical methods used to model relationships between variables. Linear regression is a foundational tool for making simple predictions and understanding basic relationships, while multiple linear regression extends this capability to more complex scenarios involving multiple factors. Nonlinear regression is crucial when relationships are not linear, as it can capture complex patterns in data. Regression analysis and algorithms are explained. Regression is compared with classification. Key aspects of various linear regression algorithms are discussed, including univariate and multivariate linear regression models. Multiple linear regression and multiple multivariate linear regression algorithms are outlined. Among the types of nonlinear regression algorithms, quadratic, polynomial, exponential, logarithmic, and power-law regression are mentioned. Regression lines and related terms are defined, such as best-fit line, positive and negative linear regression lines, hypothesis function, hypothesis space, and cost function. Evaluation metrics of linear regression are mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), residual sum of squares (SS Residual ), total sum of squares (SS Total ), R -squared , and adjusted R -squared . The assumptions of linear regression, including both univariate and multivariate linear regression, are outlined. Least squares regression is studied. Finding the best-fit line in linear regression using gradient descent involves training the linear regression algorithm in this manner. A comparison is made between simple linear regression, multiple linear regression, and polynomial regression.