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Assumptions of ordinary least-squares estimation

Abstract

The regression model can be used to describe the relationships between two or more variables in a sample without making any assumptions except that the dependent variable is continuous and that the relationships between these variables are linear. As a result, regression models can be used almost anytime in a purely descriptive manner to summarize the relationships between the variables in a sample. However, it is not possible to make valid statistical inferences about population parameters from sample statistics without making at least some assumptions. After all, statisticians must make certain assumptions about the characteristics of a population in order to derive the sampling distributions of the sample statistics drawn from that population. Fortunately, most of the assumptions associated with regression analysis are relatively weak in the sense that they are quite reasonable in most cases.

Keywords

Regression Coefficient Sample Statistic Central Limit Theorem Sampling Distribution Sample Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Plenum Press, New York 1997

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