Linear model

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Linear model

A linear model is a type of statistical model that assumes a linear relationship between the input variables (independent variables) and the single output variable (dependent variable). Linear models are widely used in various fields such as economics, biology, engineering, and social sciences due to their simplicity and interpretability.

Types of Linear Models[edit | edit source]

Linear models can be categorized into several types, including:

  • Simple linear regression: This model describes the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable.
  • Multiple linear regression: This model extends simple linear regression by using multiple explanatory variables to predict the dependent variable.
  • Generalized linear model: This model generalizes linear regression by allowing the dependent variable to have a non-normal distribution.

Mathematical Formulation[edit | edit source]

The general form of a linear model can be expressed as:

\[ y = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \cdots + \beta_p x_p + \epsilon \]

where:

  • \( y \) is the dependent variable.
  • \( \beta_0 \) is the intercept.
  • \( \beta_1, \beta_2, \ldots, \beta_p \) are the coefficients of the independent variables \( x_1, x_2, \ldots, x_p \).
  • \( \epsilon \) is the error term.

Assumptions[edit | edit source]

Linear models rely on several key assumptions:

  • Linearity: The relationship between the dependent and independent variables is linear.
  • Independence: The observations are independent of each other.
  • Homoscedasticity: The variance of the error terms is constant across all levels of the independent variables.
  • Normality: The error terms are normally distributed.

Applications[edit | edit source]

Linear models are used in various applications, including:

Related Pages[edit | edit source]

See Also[edit | edit source]

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