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multicollinearity

  1. Nicole Seaman

    P1.T2.20.19. Regression diagnostics: omitted variables, heteroskedasticity, and multicollinearity

    Learning objectives: Explain how to test whether a regression is affected by heteroskedasticity. Describe approaches to using heteroskedastic data. Characterize multicollinearity and its consequences; distinguish between multicollinearity and perfect collinearity. Describe the consequences of...
  2. P

    F-statistic and T-statistics

    1)For a sample of 400 firms, the relationship between corporate revenue (Yi) and the average years of experience per employee (Xi) is modeled as follows: Yi = β1 + β2 Xi + εi, i = 1, 2,...,400 You wish to test the joint null hypothesis that β1 = 0 and β2 = 0 at the 95% confidence level. The...
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