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Classical Linear Regression Model: Assumptions and ...
Classical Linear Regression Model: Assumptions and Diagnostic TestsYan ZengVersion , last updated on 10/05/2016 AbstractSummary of statistical tests for the Classical Linear Regression Model (CLRM), based on Brooks [1],Greene [5] [6], Pedace [8], and Zeileis [10].Contents1 The Classical Linear Regression Model (CLRM)32 Hypothesis testing : The t-test and The F-test43 Violation of Assumptions : Detection of multicollinearity.. Consequence of ignoring near multicollinearity.. Dealing with multicollinearity..64 Violation of Assumptions : Detection of heteroscedasticity.. The Goldfeld-Quandt test.. The White s general test.. The Breusch-Pagan test.. The Park test.. Consequences of using OLS in the presence of heteroscedasticity.. Dealing with heteroscedasticity.. The generalised least squares method.. Transformation.. The White-corrected standard errors..95 Violation of Assumptions : Detection of autocorrelation.. Graphical test.. The run test (the Geary test).
Oct 05, 2016 · Assumptions and Diagnostic Tests Yan Zeng Version 1.1, last updated on 10/05/2016 Abstract Summary of statistical tests for the Classical Linear Regression Model (CLRM), based on Brooks [1], Greene [5] [6], Pedace [8], and Zeileis [10]. Contents 1 The Classical Linear Regression Model (CLRM) 3 2 Hypothesis Testing: The t-test and The F-test 4
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