Fundamental Assumptions in Linear Regression

Requirements Clarification & Assessment

Before diving into the assumptions of linear regression, it's crucial to understand the context in which these assumptions are applied. Linear regression is a statistical technique used to model the relationship between a dependent variable (often called the target or response variable) and one or more independent variables (predictors or features). The goal is to fit a linear equation to observed data.

Key Requirements:

1. Understanding of Linear Regression: Familiarity with how linear regression works is essential.
2. Knowledge of Assumptions: Awareness of the assumptions that must be met for the linear regression model to be valid.
3. Application Context: Identifying the dataset and context in which linear regression is being applied, such as time-series data, cross-sectional data, etc.
4. Technical Proficiency: Ability to assess whether the assumptions hold using statistical tests and diagnostic plots.