What is Linear Regression?

Linear regression is a regression algorithm that deals with modeling a linear relationship between a continuous target variable and one or several continuous features. A typical example of data science using linear regression is price prediction based on various input attributes.

 

Why is Linear Regression used?

It is used to predict the numerical value of a continuous variable based on the known values of other variables. Furthermore, it determines the mathematical strength of the relationship between these variables, allowing analysts to quantify exactly how a specific numerical change in an independent variable alters the dependent variable.

 

What is the theoretical background behind how Linear Regression works?

The algorithm typically relies on the principle of Ordinary Least Squares (OLS) optimization. It calculates the mathematical difference, known as the "residual," between the actual data points in a dataset and the predicted points generated by the linear equation. The algorithm systematically adjusts the equation's parameters to minimize the sum of the squared residuals, effectively finding the mathematical "line of best fit" for the data.

 

 

 

What is the difference between Simple and Multiple Linear Regression?

Simple Linear Regression utilizes exactly one independent variable to calculate the prediction of the dependent variable. Multiple Linear Regression utilizes two or more independent variables to calculate the prediction for a single dependent variable, allowing the model to account for multiple influencing mathematical factors simultaneously.

 

Which programming languages and software libraries are used to implement Linear Regression?

The most common programming languages for implementing this algorithm are Python and R. In Python, it is primarily executed using the scikit-learn machine learning library, specifically by calling the LinearRegression class, or through the statsmodels library to generate detailed statistical summaries. In R, it is natively implemented using the built-in lm() (linear model) function without requiring external libraries.