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Classical Linear Regression Model: Assumptions and Diagnostic TestsYan ZengVersion , last updated on 10/05/2016AbstractSummary 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.

Oct 05, 2016 · 1 The Classical Linear Regression Model (CLRM) Let the column vector xk be the T observations on variable xk, k = 1; ;K, and assemble these data in an T K data matrix X.In most contexts, the first column of X is assumed to be a column of 1s: x1 = 2 6 6 6 4 1 1... 1 3 7 7 7 5 T 1 so that 1 is the constant term in the model. Let y be the T observations y1, , yT, and let " be …