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Regression is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. The goal of regression analysis is to understand and quantify the relationship between variables and make predictions based on that understanding. It is widely used in various fields, including economics, finance, biology, psychology, and engineering.
a. Statistics:
- In traditional statistics, regression analysis is a well-established method for examining the relationship between variables, making predictions, and understanding the underlying structure of data.
- Statistical regression models often emphasize inference and hypothesis testing, helping researchers draw conclusions about population parameters based on sample data.
- Assumptions of normality, linearity, and homoscedasticity are typically emphasized in statistical regression analysis.
b. Machine Learning:
- In the context of machine learning, regression is a type of algorithm that can be used for predictive modeling.
- Machine learning regression models, including linear regression and more complex algorithms, are trained on data to learn patterns and relationships. The focus is often on making accurate predictions rather than on understanding the underlying structure.
- Machine learning regression models can handle large datasets, non-linear relationships, and high-dimensional feature spaces, offering more flexibility than some traditional statistical methods.
Key Concepts and Types of Regression:
1. Dependent and Independent Variables:
- The variable that you are trying to predict is called the dependent variable (or response variable).
- The variables used to predict the dependent variable are called independent variables (or predictor variables).
2. Linear Regression:
- Linear regression is the simplest form of regression.
- The relationship between the dependent and independent variables is assumed to be linear…
