Linear regression

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Not only were such names as Egypt, Ashur, and Babylon well known, but at least to a certain extent and with much limitation and qualification, even the culture of the peoples was not altogether unfamiliar.

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Assumptions[ edit ] Standard linear regression models with standard estimation techniques make a number of assumptions about the predictor variables, the response variables and their relationship. Numerous extensions have been developed that allow each of these assumptions to be relaxed i. Generally these extensions make the estimation procedure more complex and time-consuming, and may also require more data in order to produce an equally precise model. Example of a cubic polynomial regression, which is a type of linear regression.

The following are the major assumptions made by standard linear regression models with standard estimation techniques e.

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Numerous extensions have been developed that allow each of these assumptions to be relaxed i. Generally these extensions make the estimation procedure more complex and time-consuming, and may also require more data in order to produce an equally precise model. Example of a cubic polynomial regression, which is a type of linear regression. The following are the major assumptions made by standard linear regression models with standard estimation techniques e.

This essentially means that the predictor variables x can be treated as fixed values, rather than random variables. This means, for example, that the predictor variables are assumed to be error-free—that is, not contaminated with measurement errors. Although this assumption is not realistic in many settings, dropping it leads to significantly more difficult errors-in-variables models.

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In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).The case of one explanatory variable is called simple linear more than one explanatory variable, the process is called multiple linear regression.

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