Inferential Aspects of Regression Models
Modified Tests for Non Normal Errors
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Regression analysis is one of the most widely used statistical techniques for establishing relationships between two or more variables. Regression analysis has become an increasing important powerful tool in the subject of applied statistics. In recent years the popularity of applied regression analysis has dramatically been rising up. To apply the regression analysis effectively. Statisticians need to aware and usage of several diagnostic measures for detecting violations of the assumptions about the model, model specification, detecting the presence of outliers, performance and validation of...
Regression analysis is one of the most widely used statistical techniques for establishing relationships between two or more variables. Regression analysis has become an increasing important powerful tool in the subject of applied statistics. In recent years the popularity of applied regression analysis has dramatically been rising up. To apply the regression analysis effectively. Statisticians need to aware and usage of several diagnostic measures for detecting violations of the assumptions about the model, model specification, detecting the presence of outliers, performance and validation of regression models. Residual analysis helps to the detect possible defects in the specification of the regression model and my suggest an improved respecification of the model.The central themes in the regression analysis are building models assessing fit, reliability and drawing conclusions. Regression analysis techniques are applied in almost every field of study, including social sciences, physical sciences, life & biological sciences, business economics, technology and humanities, engineering and management sciences
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