Regression

Regression is a statistical measure used in finance, investing, and other disciplines that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables).

Overview

Regression helps investment and financial managers to value assets and understand the relationships between variables, such as commodity prices and the stocks of businesses dealing in those commodities.

Types of Regression

There are several types of regression models that you can use to analyze your data and interpret the results; however, you must choose the correct model based on the following criteria:

• Linear Regression: It is the simplest form of regression. It finds the best fitting straight line through the data points.
• Logistic Regression: It is used when the dependent variable is binary in nature.
• Polynomial Regression: It is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial.
• Stepwise Regression: It involves selection of independent variables to use in the model.
• Ridge Regression: It is a method of estimating the coefficients of multiple-regression models in scenarios where independent variables are highly correlated.
• Lasso Regression: It is a regression analysis method that performs both variable selection and regularization.

Applications of Regression

Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships.

See Also

• Correlation
• Covariance
• Standard Deviation
• Variance

References

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