Answer:
Regression analysis is a set of statistical methods used for the estimation of relationships between a dependent variable and one or more independent variables. It can be utilized to assess the strength of the relationship between variables and for modeling the future relationship between them.
Regression analysis includes several variations, such as linear, multiple linear, and nonlinear. The most common models are simple linear and multiple linear. Nonlinear regression analysis is commonly used for more complicated data sets in which the dependent and independent variables show a nonlinear relationship.
Regression analysis offers numerous applications in various disciplines, including finance.
Regression Analysis – Linear Model Assumptions
Linear regression analysis is based on six fundamental assumptions:
The dependent and independent variables show a linear relationship between the slope and the intercept.
The independent variable is not random.
The value of the residual (error) is zero.
The value of the residual (error) is constant across all observations.
The value of the residual (error) is not correlated across all observations.
The residual (error) values follow the normal distribution.
Regression Analysis – Simple Linear Regression
Simple linear regression is a model that assesses the relationship between a dependent variable and an independent variable. The simple linear model is expressed using the following equation:
Y = a + bX + ϵ
Where:
Y – Dependent variable
X – Independent (explanatory) variable
a – Intercept
b – Slope
ϵ – Residual (error)