Definition
Linear regression is a classical statistical tool that models relationship between a dependent variable or regress and Y, explanatory variable or regressor \(X = \{ x_1 , ..., x_I \}\) and a random term \(\varepsilon\) by fitting a linear function,
where \(\alpha _0\) is the constant term, the \(\alpha _i{\text s}\) are the respective parameters of independent variable, and I is the number of parameters to be estimated in the linear regression.
Key Points
Linear regression analysis is an important component for several tasks such as clustering, time series analysis, and information retrieval. For instance, it is a very powerful forecasting method for time series data. It helps identify the long-term movement of a certain data set based on given information and explore the dependent variable as function of time.
To solve the linear regression problem, there are various kinds of approaches...
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Recommended Reading
Draper, N.R., Smith, H. Applied Regression Analysis Wiley Series in Probability and Statistics, 1998.
Gross, J. Linear Regression, Springer, Berlin, 2003.
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Shen, J. (2009). Linear Regression. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_542
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DOI: https://doi.org/10.1007/978-0-387-39940-9_542
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