**Description:**
Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. The simplest form of linear regression is simple linear regression, which involves two variables—one independent variable and one dependent variable. The relationship is expressed in the form of a line, represented by the equation:
\[ Y = β_0 + β_1X + ε \]
where:
- \( Y \) is the dependent variable (outcome),
- \( X \) is the independent variable (predictor),
- \( β_0 \) is the intercept,
- \( β_1 \) is the slope of the line,
- \( ε \) is the error term.
Multiple linear regression extends this to include multiple independent variables, allowing for more complex relationships. The goal of linear regression is to find the best-fitting line that minimizes the difference between the observed values and the values predicted by the model, typically using the least squares method.
Linear regression is widely used in various fields including economics, biology, engineering, and social sciences for forecasting, analyzing trends, and making predictions based on historical data.
**Tags:**
- Linear Regression
- Statistical Analysis
- Predictive Modeling
- Simple Linear Regression
- Multiple Linear Regression
- Machine Learning
- Data Science
- Least Squares Method
- Regression Analysis
- Data Visualization
- Statistical Modeling
- Independent Variable
- Dependent Variable
- Coefficient Estimation
- Trend Analysis
