Definition of Linear regression
1. Noun. The relation between variables when the regression equation is linear: e.g., y = ax + b.
Category relationships: Statistics
Generic synonyms: Regression, Regression Toward The Mean, Simple Regression, Statistical Regression
Terms within: Regression Coefficient
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Lexicographical Neighbors of Linear Regression
Literary usage of Linear regression
Below you will find example usage of this term as found in modern and/or classical literature:
1. Introduction to Mathematical Statistics by Carl Joseph West (1918)
"linear regression. A straight line fitted to the means of the arrays is called a line of regression. A line of regression smooths the curve of regression. ..."
2. Change-Point Problems by Edward G. Carlstein, Hans-Georg Müller, David Siegmund (1994)
"The problem of detecting a change-point in a linear regression model has been addressed by many authors. While likelihood ratio statistics to test for ..."
3. L1-statistical Procedures and Related Topics by Yadolah Dodge (1997)
"1 Introduction In this paper we consider the multiple linear regression model Y = Z9 + e, (1) where Y is an nx 1 vector of dependent variables, ..."
4. Weighted Empiricals and Linear Modelsby Hira L. Koul by Hira L. Koul (1992)
"These processes are as basic to linear regression and autoregression models as the ordinary ... However their usefulness in studying linear regression and ..."
5. Surface Temperature Reconstructions for the Last 2,000 Years by National Research Council (U.S.) (2006)
"linear regression AND PROXY RECONSTRUCTION The most common form of proxy reconstruction depends on the use of a multi- variate linear regression. ..."
6. Analytic Statistical Models by Ib M. Skovgaard (1990)
"9 Piecewise linear regression Let Y\ , . . . , Yn be independent normally distributed random variables with common variance a2 and expectations given by ..."
7. Multivariate Analysis and Its Applications by Theodore Wilbur Anderson, Kʻai-tʻai Fang, Ingram Olkin (1994)
"It is shown how a collection of nonnested dependent normal linear regression models may be combined into a single linear model by imposing a parsimonious ..."