Conditions and the Check-list for Linear Models-ECO6416

Conditions and the Check-list for Linear Models-ECO6416 -...

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Unformatted text preview: Conditions and the Check-list for Linear Models Almost all models of reality, including regression models, have assumptions that must be verified in order that the model has power to test hypotheses and for it to be able to predict accurately. The following is the list of basic assumptions (i.e., conditions) and the tools to check these necessary conditions. 1. Any undetected outliers may have major impact on the regression model. Outliers are a few observations that are not well fitted by the best" available model. In such case one, must first investigate the source of data, if there is no doubt about the accuracy or veracity of the observation, then it should be removed and the model should be refitted. You might like to use the Determination of the Outliers JavaScript to perform some numerical experimentation for validating and for a deeper understanding of the concepts 2. The dependent variable Y is a linear function of the independent variable X. This can be checked by carefully examining all the points in the...
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Conditions and the Check-list for Linear Models-ECO6416 -...

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