Introduction to Linear Regression
Linear regression is a fundamental statistical method used to model the relationship between a dependent variable and one or more independent variables. It is widely used in various fields such as finance, biology, and machine learning.
Concepts of Linear Regression
The goal of linear regression is to find the best-fitting straight line through a set of points. This line is known as the regression line and is represented by the equation:
y = mx + c
where y is the dependent variable, x is the independent variable, m is the slope, and c is the y-intercept.
Applications of Linear Regression
- Predicting housing prices based on features such as size and location.
- Forecasting stock market trends.
- Analyzing the impact of advertising spending on sales revenues.